This document provides an overview of EMC, a company that provides tools and solutions for information management. EMC helps customers design, build, and manage intelligent information infrastructures to exploit the value of information for business advantage. EMC works with organizations of all sizes across various industries. It offers solutions, services, software, and systems to help customers meet critical business challenges relating to archiving, backup, compliance, and more. EMC has a strong record of innovation and leadership in the data storage market, and has over 33,000 employees worldwide.
Could you increase your knowledge—and raise your grade—i.docxfaithxdunce63732
Could you increase your knowledge—
and raise your grade—if you…
…used an online tutorial that assisted you with Access
and Excel skills mapped to this book?
…learned to use Microsoft’s SharePoint, the number one
organizational tool for file sharing and collaboration?
…had flashcards and student PowerPoints
to prepare for lectures?
Visit , a valuable tool
for your student success and your
business career.
www.myMISlab.com
www.myMISlab.com
INTEGRATING BUSINESS WITH TECHNOLOGY
By completing the projects in this text, students will be able to demonstrate business knowledge, application
software proficiency, and Internet skills.These projects can be used by instructors as learning assessment tools
and by students as demonstrations of business, software, and problem-solving skills to future employers. Here
are some of the skills and competencies students using this text will be able to demonstrate:
Business Application skills: Use of both business and software skills in real-world business applications.
Demonstrates both business knowledge and proficiency in spreadsheet, database, and Web page/blog creation
tools.
Internet skills: Ability to use Internet tools to access information, conduct research, or perform online
calculations and analysis.
Analytical, writing and presentation skills: Ability to research a specific topic, analyze a problem, think
creatively, suggest a solution, and prepare a clear written or oral presentation of the solution, working either
individually or with others in a group.
Business Application Skills
BUSINESS SKILLS
Finance and Accounting
Financial statement analysis
Pricing hardware anrj software
Technology rent vs. buy decision
Total Cost of Ownership (TCO) analysis
Analyzing telecommunications services anrj costs
Risk assessment
Retirement planning
Capital budgeting
Human Resources
Employee training and skills tracking
Job posting database and Web page
Manufacturing and Production
Analyzing supplier performance and pricing
Inventory management
Bill of materials cost sensitivity analysis
Sales and Marketing
Sales trend analysis
SOFTWARE SKILLS
Spreadsheet charts
Spreadsheet formulas
Spreadsheet downloading and formatting
Spreadsheet formulas
Spreadsheet formulas
Spreadsheet formulas
Spreadsheet charts and formulas
Spreadsheet formulas and logical functions
Spreadsheet formulas
Database design
Database querying and reporting
Database design
Web page design and creation
Spreadsheet date functions
Database functions
Data filtering
Importing data into a database
Database querying and reporting
Spreadsheet data tables
Spreadsheet formulas
Database querying and reporting
CHAPTER
Chapter 2*
Chapter 10
Chapter 5
Chapter 5*
Chapter 7
Chapter 8
Chapter 11
Chapter 14
Chapter 14*
Chapter 13*
Chapter 15
Chapter 2
Chapter 6
Chapter 12*
Chapter 1
Customer reservation system
Improving marketing decisions
Customer profiling
Customer service analysis
Sales lead and.
This document provides an overview of EMC, a company that provides tools and solutions for information management. EMC helps customers design, build, and manage intelligent information infrastructures to exploit the value of information for business advantage. EMC works with organizations of all sizes across various industries. It offers solutions, services, software, and systems to help customers meet critical business challenges relating to archiving, backup, compliance, and more. EMC has a strong record of innovation and leadership in the data storage market, and has over 33,000 employees worldwide.
Could you increase your knowledge—and raise your grade—i.docxfaithxdunce63732
Could you increase your knowledge—
and raise your grade—if you…
…used an online tutorial that assisted you with Access
and Excel skills mapped to this book?
…learned to use Microsoft’s SharePoint, the number one
organizational tool for file sharing and collaboration?
…had flashcards and student PowerPoints
to prepare for lectures?
Visit , a valuable tool
for your student success and your
business career.
www.myMISlab.com
www.myMISlab.com
INTEGRATING BUSINESS WITH TECHNOLOGY
By completing the projects in this text, students will be able to demonstrate business knowledge, application
software proficiency, and Internet skills.These projects can be used by instructors as learning assessment tools
and by students as demonstrations of business, software, and problem-solving skills to future employers. Here
are some of the skills and competencies students using this text will be able to demonstrate:
Business Application skills: Use of both business and software skills in real-world business applications.
Demonstrates both business knowledge and proficiency in spreadsheet, database, and Web page/blog creation
tools.
Internet skills: Ability to use Internet tools to access information, conduct research, or perform online
calculations and analysis.
Analytical, writing and presentation skills: Ability to research a specific topic, analyze a problem, think
creatively, suggest a solution, and prepare a clear written or oral presentation of the solution, working either
individually or with others in a group.
Business Application Skills
BUSINESS SKILLS
Finance and Accounting
Financial statement analysis
Pricing hardware anrj software
Technology rent vs. buy decision
Total Cost of Ownership (TCO) analysis
Analyzing telecommunications services anrj costs
Risk assessment
Retirement planning
Capital budgeting
Human Resources
Employee training and skills tracking
Job posting database and Web page
Manufacturing and Production
Analyzing supplier performance and pricing
Inventory management
Bill of materials cost sensitivity analysis
Sales and Marketing
Sales trend analysis
SOFTWARE SKILLS
Spreadsheet charts
Spreadsheet formulas
Spreadsheet downloading and formatting
Spreadsheet formulas
Spreadsheet formulas
Spreadsheet formulas
Spreadsheet charts and formulas
Spreadsheet formulas and logical functions
Spreadsheet formulas
Database design
Database querying and reporting
Database design
Web page design and creation
Spreadsheet date functions
Database functions
Data filtering
Importing data into a database
Database querying and reporting
Spreadsheet data tables
Spreadsheet formulas
Database querying and reporting
CHAPTER
Chapter 2*
Chapter 10
Chapter 5
Chapter 5*
Chapter 7
Chapter 8
Chapter 11
Chapter 14
Chapter 14*
Chapter 13*
Chapter 15
Chapter 2
Chapter 6
Chapter 12*
Chapter 1
Customer reservation system
Improving marketing decisions
Customer profiling
Customer service analysis
Sales lead and.
Preston Williams III is Senior Partner & Chief Information Officer (CIO) at GBC® Global Services. He is a pioneer and futurist with 20+ years of Big 4, Fortune 500, Global 100 and entrepreneurial experience. That experience includes Senior Auditor with Price Waterhouse (PriceWaterhouseCoopers), Controller for Lynn-Phill, IT Consultant with McGladrey & Pullen and Andersen Consulting (Accenture) as well as Project Executive at IBM® Global Services. He also worked as the first Product Manager for Global Data Collection with Dun & Bradstreet (D&B) and the first Chief Information Officer (CIO) at Langston University.
From 2002 to 2004, Preston served as the first Chief Executive Officer (CEO) & Managing Partner at GBC®. Incorporated in Delaware, USA; the firm operates in Kenya, Liberia, Uganda and the United States. He has successfully implemented sound, reliable, dynamic and robust IT systems integration and financial management solutions in North America, Asia Pacific (APAC), Latin America (LATAM), Europe, the Middle East and Africa (EMEA).
Mr. Williams is Chairman of the GBC® Board of Directors, a recipient of the IBM® Global Services Leadership Award, a member of the Global CIO Think Tank and a member of the Internet Evolution Executive Clan.
20 Emerging influencers in 2020 for big dataRiver11river
You might have not heard most of these names yet, but you surely will soon. This list is designed to recognize emerging talent in the fields of data and analytics – mostly entrepreneurs and up-and-coming talent who are informing, educating and inspiring others through data. They come from different sectors and backgrounds – from data architecture to visualization. The one thing that unites them is their passion for data.
The document outlines plans for a business-oriented social network called WeSpline that aims to connect enterprises and startups globally. It will use intelligent algorithms to help enterprises discover new technologies through efficient searching and recommendations. Startups will be able to actively search for and connect with potential enterprise clients around the world. The network will bring together various players in the innovation ecosystem such as investors, universities, and service providers to foster collaboration. It will leverage technologies like machine learning and natural language processing to continuously improve user experience.
Healthcare organizations are unique business entities that present challenges for optimally organizing governance, people, and services for next-generation BI. Learning from other industries that have adopted the concept of the business intelligence competency center (BICC), this article explores the available options and evaluates which service and organizational model appears to best fit healthcare providers and similarly complex organizations.
The document summarizes Reputation.com's online reputation management services. It states that over 67% of executives believe the economy is now reputation-based. 76% of executives expect to be searched online but 22% have never searched themselves. 69% of directors see reputation risk as the most important business risk. Reputation.com claims to help individuals and businesses control their online reputation and privacy. It is a pioneer in the field with customers in over 100 countries offering reputation management solutions.
Amira Saleh has over 20 years of experience in information technology management, web application development, and biosciences research. She is seeking a challenging position that utilizes her skills in IT, project management, marketing, and biological research. Her experience includes roles as an IT consultant, network manager, systems analyst, and graduate research assistant studying carcinogen risk assessment and marine bacteria.
Information Technology Innovator David Ward 2011ward2dr
David Ward is an experienced senior technology executive with over 20 years of experience in leadership roles at major financial institutions. He has expertise in areas such as technology strategies, business transformation, enterprise systems, infrastructure, and mergers and acquisitions. Throughout his career, he has delivered value to shareholders and improved customer satisfaction. Currently, he is seeking new opportunities to apply his experience and drive innovation.
BUSINESS INTELLIGENCE AND ANALYTICS RAMESH SHARDA DU.docxfelicidaddinwoodie
BUSINESS INTELLIGENCE
AND ANALYTICS
RAMESH SHARDA
DURSUN DELEN
EFRAIM TURBAN
TENTH EDITION
.•
TENTH EDITION
BUSINESS INTELLIGENCE
AND ANALYTICS:
SYSTEMS FOR DECISION SUPPORT
Ramesh Sharda
Oklahoma State University
Dursun Delen
Oklahoma State University
Efraim Turban
University of Hawaii
With contributions by
J.E.Aronson
Tbe University of Georgia
Ting-Peng Liang
National Sun Yat-sen University
David King
]DA Software Group, Inc.
PEARSON
Boston Columbus Indianapolis New York San Francisco Upper Saddle River
Amsterdam Cape Town Dubai London Madrid Milan Munich Paris Montreal Toronto
Delhi Mexico City Sao Paulo Sydney Hong Kong Seoul Singapore Taipei Tokyo
Editor in Chief: Stephanie Wall
Executive Editor: Bob Horan
Program Manager Team Lead: Ashley Santora
Program Manager: Denise Vaughn
Executive Marketing Manager: Anne Fahlgren
Project Manager Team Lead: Judy Leale
Project Manager: Tom Benfatti
Operations Specialist: Michelle Klein
Creative Director: Jayne Conte
Cover Designer: Suzanne Behnke
Digital Production Project Manager: Lisa
Rinaldi
Full-Service Project Management: George Jacob,
Integra Software
Solution
s.
Printer/Binder: Edwards Brothers Malloy-Jackson
Road
Cover Printer: Lehigh/Phoenix-Hagerstown
Text Font: Garamond
Credits and acknowledgments borrowed from other sources and reproduced, with permission, in this textbook
appear on the appropriate page within text.
Microsoft and/ or its respective suppliers make no representations about the suitability of the information
contained in the documents and related graphics published as part of the services for any purpose. All such
documents and related graphics are provided "as is" without warranty of any kind. Microsoft and/or its
respective suppliers hereby disclaim all warranties and conditions with regard to this information, including
all warranties and conditions of merchantability, whether express, implied or statutory, fitness for a particular
purpose, title and non-infringement. In no event shall Microsoft and/or its respective suppliers be liable for
any special, indirect or consequential damages or any damages whatsoever resulting from loss of use , data or
profits, whether in an action of contract, negligence or other tortious action, arising out of or in connection
with the use or performance of information available from the services.
The documents and related graphics contained herein could include technical inaccuracies or typographical
errors. Changes are periodically added to the information here in. Microsoft and/or its respective suppliers may
make improvements and/or changes in the product(s) and/ or the program(s) described herein at any time.
Partial screen shots may be viewed in full within the software version specified.
Microsoft® Windows®, and Microsoft Office® are registered trademarks of the Microsoft Corporation in the U.S.A.
and other countries. This book is not .
BUSINESS INTELLIGENCE AND ANALYTICS RAMESH SHARDA DUTawnaDelatorrejs
BUSINESS INTELLIGENCE
AND ANALYTICS
RAMESH SHARDA
DURSUN DELEN
EFRAIM TURBAN
TENTH EDITION
.•
TENTH EDITION
BUSINESS INTELLIGENCE
AND ANALYTICS:
SYSTEMS FOR DECISION SUPPORT
Ramesh Sharda
Oklahoma State University
Dursun Delen
Oklahoma State University
Efraim Turban
University of Hawaii
With contributions by
J.E.Aronson
Tbe University of Georgia
Ting-Peng Liang
National Sun Yat-sen University
David King
]DA Software Group, Inc.
PEARSON
Boston Columbus Indianapolis New York San Francisco Upper Saddle River
Amsterdam Cape Town Dubai London Madrid Milan Munich Paris Montreal Toronto
Delhi Mexico City Sao Paulo Sydney Hong Kong Seoul Singapore Taipei Tokyo
Editor in Chief: Stephanie Wall
Executive Editor: Bob Horan
Program Manager Team Lead: Ashley Santora
Program Manager: Denise Vaughn
Executive Marketing Manager: Anne Fahlgren
Project Manager Team Lead: Judy Leale
Project Manager: Tom Benfatti
Operations Specialist: Michelle Klein
Creative Director: Jayne Conte
Cover Designer: Suzanne Behnke
Digital Production Project Manager: Lisa
Rinaldi
Full-Service Project Management: George Jacob,
Integra Software
Solution
s.
Printer/Binder: Edwards Brothers Malloy-Jackson
Road
Cover Printer: Lehigh/Phoenix-Hagerstown
Text Font: Garamond
Credits and acknowledgments borrowed from other sources and reproduced, with permission, in this textbook
appear on the appropriate page within text.
Microsoft and/ or its respective suppliers make no representations about the suitability of the information
contained in the documents and related graphics published as part of the services for any purpose. All such
documents and related graphics are provided "as is" without warranty of any kind. Microsoft and/or its
respective suppliers hereby disclaim all warranties and conditions with regard to this information, including
all warranties and conditions of merchantability, whether express, implied or statutory, fitness for a particular
purpose, title and non-infringement. In no event shall Microsoft and/or its respective suppliers be liable for
any special, indirect or consequential damages or any damages whatsoever resulting from loss of use , data or
profits, whether in an action of contract, negligence or other tortious action, arising out of or in connection
with the use or performance of information available from the services.
The documents and related graphics contained herein could include technical inaccuracies or typographical
errors. Changes are periodically added to the information here in. Microsoft and/or its respective suppliers may
make improvements and/or changes in the product(s) and/ or the program(s) described herein at any time.
Partial screen shots may be viewed in full within the software version specified.
Microsoft® Windows®, and Microsoft Office® are registered trademarks of the Microsoft Corporation in the U.S.A.
and other countries. This book is not ...
Curtis HillTopic 07 Assignment Long-Term Care ChartHA30.docxdorishigh
The document discusses different types of long-term care services and compares their cost effectiveness, efficacy, and levels of patient satisfaction. It provides a chart that rates nursing homes, continuing care retirement communities, supportive housing programs, community services, and home care based on these three criteria. The document aims to help complete an assignment comparing and contrasting long-term care services.
Auditing A Practical Approach with Data Analytics, 2e Raymond Johnson, Laura ...DingBo3
This document provides an overview of the second edition of the textbook "Auditing: A Practical Approach with Data Analytics" by Raymond N. Johnson and Laura D. Wiley. Some key details include:
- The textbook takes a practical approach to teaching auditing concepts and incorporates data analytics.
- Chapters have been updated throughout to reflect recent changes in auditing standards, the CPA exam, and the increasing role of technology in auditing.
- New illustrations, examples, and discussions have been added on topics like audit evidence, internal controls, substantive procedures, and data analytics.
- The authors aim to help students develop critical thinking, ethical decision-making, and an ability to adapt to changes
eBook PDF textbook - Auditing A Practical Approach with Data Analytics, 2e Ra...EdwinPolack1
This document provides information about the second edition of the textbook "Auditing: A Practical Approach with Data Analytics" by Raymond N. Johnson and Laura D. Wiley.
The document includes the cover design, authors, editors involved in production, copyright information, table of contents, and dedication by the authors to their spouses for their support. It also provides brief biographies of the authors, Raymond N. Johnson and Laura D. Wiley, highlighting their professional experiences and areas of expertise in auditing.
Running head INFORMATION LITERACY 1INFORMATION LITERACY 2.docxwlynn1
Running head: INFORMATION LITERACY 1
INFORMATION LITERACY 2
INFORMATION LITERACY
GEN 499: General Education Capstone
October 14, 2019.
Ashford University Library has good resources for any academic material one wants to read. I am a business student and when I joined Ashford University I was a little worried about what might happen if I could not find the necessary academic materials to support my education. Another issue I found overwhelming at first was how to navigate the library database because there were so many options. If you click on a particular option at times they are not relevant to the topic under research. A friend directed me on how to navigate in the Databases A-Z. Nowadays it is easier because I followed all the instructions to the later.
I like the ProQuest Database because it has so many options someone can choose from and the resources are very helpful, (Brannon, 2017). I do not have any concerns but don't like the fact that Ashford Library pulls up student's research papers as references that have to be changed. In these databases, one has to use the subject topic to find readings or scholarly articles, (Nelson & Huffman, 2015). Some databases may not have the articles one is looking for because they are all specified for certain course work, if you are new it can be very overwhelming. I also realized that if I download a full PDF then all the details about the authors and references will be readily available.
Ashford University Library has improved skills in my business course because before the exams approach I am always equipped with adequate information. This keeps me away from using search engines like Google and some of the resources may not be credible. The best part with the resources that come from Ashford Library is that they help one reduce the reference format mistakes because they are already located on the articles, (Omar, et.al, 2018). The newspapers and other articles that are on the internet can be very difficult to cite at times. In general, the Ashford University Library is effective and reliable because it has good resources and citations which are accurate.
References
Brannon, P. C. (2017). ProQuest Regulatory Insight. Law Library Journal, 109(3), 484.
Nelson, N., & Huffman, J. (2015). Predatory journals in library databases: How much should we worry?. The serials librarian, 69(2), 169-192.
Omar, D., Preater, A., Clark, I., & Liebert, R. J. (2018). Inclusive reading lists: how libraries can support student and academic leadership.
INFORMATION
GOVERNANCE
Founded in 1807, John Wiley & Sons is the oldest independent publishing company in
the United States. With offi ces in North America, Europe, Asia, and Australia, Wiley
is globally committed to developing and marketing print and electronic products and
services for our customers’ professional and personal knowledge and understanding.
The Wiley CIO series provides information, tools, and insights to IT executives
a.
Running head INFORMATION LITERACY 1INFORMATION LITERACY 2.docxjeanettehully
Running head: INFORMATION LITERACY 1
INFORMATION LITERACY 2
INFORMATION LITERACY
GEN 499: General Education Capstone
October 14, 2019.
Ashford University Library has good resources for any academic material one wants to read. I am a business student and when I joined Ashford University I was a little worried about what might happen if I could not find the necessary academic materials to support my education. Another issue I found overwhelming at first was how to navigate the library database because there were so many options. If you click on a particular option at times they are not relevant to the topic under research. A friend directed me on how to navigate in the Databases A-Z. Nowadays it is easier because I followed all the instructions to the later.
I like the ProQuest Database because it has so many options someone can choose from and the resources are very helpful, (Brannon, 2017). I do not have any concerns but don't like the fact that Ashford Library pulls up student's research papers as references that have to be changed. In these databases, one has to use the subject topic to find readings or scholarly articles, (Nelson & Huffman, 2015). Some databases may not have the articles one is looking for because they are all specified for certain course work, if you are new it can be very overwhelming. I also realized that if I download a full PDF then all the details about the authors and references will be readily available.
Ashford University Library has improved skills in my business course because before the exams approach I am always equipped with adequate information. This keeps me away from using search engines like Google and some of the resources may not be credible. The best part with the resources that come from Ashford Library is that they help one reduce the reference format mistakes because they are already located on the articles, (Omar, et.al, 2018). The newspapers and other articles that are on the internet can be very difficult to cite at times. In general, the Ashford University Library is effective and reliable because it has good resources and citations which are accurate.
References
Brannon, P. C. (2017). ProQuest Regulatory Insight. Law Library Journal, 109(3), 484.
Nelson, N., & Huffman, J. (2015). Predatory journals in library databases: How much should we worry?. The serials librarian, 69(2), 169-192.
Omar, D., Preater, A., Clark, I., & Liebert, R. J. (2018). Inclusive reading lists: how libraries can support student and academic leadership.
INFORMATION
GOVERNANCE
Founded in 1807, John Wiley & Sons is the oldest independent publishing company in
the United States. With offi ces in North America, Europe, Asia, and Australia, Wiley
is globally committed to developing and marketing print and electronic products and
services for our customers’ professional and personal knowledge and understanding.
The Wiley CIO series provides information, tools, and insights to IT executives
a ...
Kurt Niziak is an IT consultant and trainer with over 25 years of experience solving complex problems. He specializes in change management, training, documentation, and presentations. Niziak holds numerous software certifications including Microsoft Office, Windows, and medical software. He has trained over 3,000 individuals and provided technical support for major law firms. Niziak's experience includes positions at the Massachusetts Department of Mental Health, CompuWorks Systems Inc., and Advanced Technology Corporation where he served as a Configuration Control Manager receiving a Secret Level Security Clearance.
Kurt Niziak is an IT consultant and trainer with over 25 years of experience solving complex problems. He specializes in change management, training, documentation, and presentations. Niziak holds numerous software certifications including Microsoft Office, Windows, and medical software. He has extensive experience providing software training to legal firms, government agencies, and healthcare organizations. Niziak's background includes roles as a training manager, consultant, and configuration control manager for the U.S. Navy.
Big Data Startups - Top Visualization and Data Analytics Startupswallesplace
1010Data provides a cloud-based big data analytics platform that allows customers to analyze large datasets using simple interfaces. Their platform offers fast data processing, scalability, and tools for data integration, visualization, and sharing insights. Major customers include companies in financial services, retail, consumer packaged goods, telecom, and healthcare that use 1010Data to gain insights from large customer and transactional datasets.
BUSINESS INTELLIGENCE AND ANALYTICS RAMESH SHARDA DUjenkinsmandie
BUSINESS INTELLIGENCE
AND ANALYTICS
RAMESH SHARDA
DURSUN DELEN
EFRAIM TURBAN
TENTH EDITION
.•
TENTH EDITION
BUSINESS INTELLIGENCE
AND ANALYTICS:
SYSTEMS FOR DECISION SUPPORT
Ramesh Sharda
Oklahoma State University
Dursun Delen
Oklahoma State University
Efraim Turban
University of Hawaii
With contributions by
J.E.Aronson
Tbe University of Georgia
Ting-Peng Liang
National Sun Yat-sen University
David King
]DA Software Group, Inc.
PEARSON
Boston Columbus Indianapolis New York San Francisco Upper Saddle River
Amsterdam Cape Town Dubai London Madrid Milan Munich Paris Montreal Toronto
Delhi Mexico City Sao Paulo Sydney Hong Kong Seoul Singapore Taipei Tokyo
Editor in Chief: Stephanie Wall
Executive Editor: Bob Horan
Program Manager Team Lead: Ashley Santora
Program Manager: Denise Vaughn
Executive Marketing Manager: Anne Fahlgren
Project Manager Team Lead: Judy Leale
Project Manager: Tom Benfatti
Operations Specialist: Michelle Klein
Creative Director: Jayne Conte
Cover Designer: Suzanne Behnke
Digital Production Project Manager: Lisa
Rinaldi
Full-Service Project Management: George Jacob,
Integra Software
Solution
s.
Printer/Binder: Edwards Brothers Malloy-Jackson
Road
Cover Printer: Lehigh/Phoenix-Hagerstown
Text Font: Garamond
Credits and acknowledgments borrowed from other sources and reproduced, with permission, in this textbook
appear on the appropriate page within text.
Microsoft and/ or its respective suppliers make no representations about the suitability of the information
contained in the documents and related graphics published as part of the services for any purpose. All such
documents and related graphics are provided "as is" without warranty of any kind. Microsoft and/or its
respective suppliers hereby disclaim all warranties and conditions with regard to this information, including
all warranties and conditions of merchantability, whether express, implied or statutory, fitness for a particular
purpose, title and non-infringement. In no event shall Microsoft and/or its respective suppliers be liable for
any special, indirect or consequential damages or any damages whatsoever resulting from loss of use, data or
profits, whether in an action of contract, negligence or other tortious action, arising out of or in connection
with the use or performance of information available from the services.
The documents and related graphics contained herein could include technical inaccuracies or typographical
errors. Changes are periodically added to the information herein. Microsoft and/or its respective suppliers may
make improvements and/or changes in the product(s) and/ or the program(s) described herein at any time.
Partial screen shots may be viewed in full within the software version specified.
Microsoft® Windows®, and Microsoft Office® are registered trademarks of the Microsoft Corporation in the U.S.A.
and other countries. This book is not sp ...
BUSINESS INTELLIGENCE AND ANALYTICS RAMESH SHARDA DUChereCoble417
BUSINESS INTELLIGENCE
AND ANALYTICS
RAMESH SHARDA
DURSUN DELEN
EFRAIM TURBAN
TENTH EDITION
.•
TENTH EDITION
BUSINESS INTELLIGENCE
AND ANALYTICS:
SYSTEMS FOR DECISION SUPPORT
Ramesh Sharda
Oklahoma State University
Dursun Delen
Oklahoma State University
Efraim Turban
University of Hawaii
With contributions by
J.E.Aronson
Tbe University of Georgia
Ting-Peng Liang
National Sun Yat-sen University
David King
]DA Software Group, Inc.
PEARSON
Boston Columbus Indianapolis New York San Francisco Upper Saddle River
Amsterdam Cape Town Dubai London Madrid Milan Munich Paris Montreal Toronto
Delhi Mexico City Sao Paulo Sydney Hong Kong Seoul Singapore Taipei Tokyo
Editor in Chief: Stephanie Wall
Executive Editor: Bob Horan
Program Manager Team Lead: Ashley Santora
Program Manager: Denise Vaughn
Executive Marketing Manager: Anne Fahlgren
Project Manager Team Lead: Judy Leale
Project Manager: Tom Benfatti
Operations Specialist: Michelle Klein
Creative Director: Jayne Conte
Cover Designer: Suzanne Behnke
Digital Production Project Manager: Lisa
Rinaldi
Full-Service Project Management: George Jacob,
Integra Software
Solution
s.
Printer/Binder: Edwards Brothers Malloy-Jackson
Road
Cover Printer: Lehigh/Phoenix-Hagerstown
Text Font: Garamond
Credits and acknowledgments borrowed from other sources and reproduced, with permission, in this textbook
appear on the appropriate page within text.
Microsoft and/ or its respective suppliers make no representations about the suitability of the information
contained in the documents and related graphics published as part of the services for any purpose. All such
documents and related graphics are provided "as is" without warranty of any kind. Microsoft and/or its
respective suppliers hereby disclaim all warranties and conditions with regard to this information, including
all warranties and conditions of merchantability, whether express, implied or statutory, fitness for a particular
purpose, title and non-infringement. In no event shall Microsoft and/or its respective suppliers be liable for
any special, indirect or consequential damages or any damages whatsoever resulting from loss of use, data or
profits, whether in an action of contract, negligence or other tortious action, arising out of or in connection
with the use or performance of information available from the services.
The documents and related graphics contained herein could include technical inaccuracies or typographical
errors. Changes are periodically added to the information herein. Microsoft and/or its respective suppliers may
make improvements and/or changes in the product(s) and/ or the program(s) described herein at any time.
Partial screen shots may be viewed in full within the software version specified.
Microsoft® Windows®, and Microsoft Office® are registered trademarks of the Microsoft Corporation in the U.S.A.
and other countries. This book is not sp ...
Delusional Disorders
Pakistani hought Processes
BACKGROUND
The client is a 34-year-old Pakistani female who moved to the United States in her late teens/early 20s. She is currently in an “arranged” marriage (her husband was selected for her since she was 9 years old). She presents to your office today following a 21 day hospitalization for what was diagnosed as “brief psychotic disorder.” She was given this diagnosis as her symptoms have persisted for less than 1 month.
Prior to admission, she was reporting visions of Allah, and over the course of a week, she believed that she was the prophet Mohammad. She believed that she would deliver the world from sin. Her husband became concerned about her behavior to the point that he was afraid of leaving their 4 children with her. One evening, she was “out of control” which resulted in his calling the police and her subsequent admission to an inpatient psych unit.
During today’s assessment, she appears quite calm, and insists that the entire incident was “blown out of proportion.” She denies that she believed herself to be the prophet Mohammad and states that her husband was just out to get her because he never loved her and wanted an “American wife” instead of her. She tells you that she knows this because the television is telling her so.
She currently weighs 140 lbs, and is 5’ 5”
SUBJECTIVE
Client reports that her mood is “good.” She denies auditory/visual hallucinations, but believes that the television does talk to her. She believes that Allah sends her messages through the TV. At times throughout the clinical interview, she becomes hostile towards the PMHNP, but then calms down.
You reviewed her hospital records and find that she has been medically worked up by a physician who reported her to be in overall good health. Lab studies were all within normal limits.
Client admits that she stopped taking her Risperdal about a week after she got out of the hospital because she thinks her husband is going to poison her so that he can marry an American woman.
MENTAL STATUS EXAM
The client is alert, oriented to person, place, time, and event. She is dressed appropriately for the weather and time of year. She demonstrates no noteworthy mannerisms, gestures, or tics. Her speech is slow and at times, interrupted by periods of silence. Self-reported mood is euthymic. Affect constricted. Although the client denies visual or auditory hallucinations, she appears to be “listening” to something. Delusional and paranoid thought processes as described, above. Insight and judgment are impaired. She is currently denying suicidal or homicidal ideation.
The PMHNP administers the PANSS which reveals the following scores:
-40 for the positive symptoms scale
-20 for the negative symptom scale
-60 for general psychopathology scale
Diagnosis: Schizophrenia, paranoid type
RESOURCES
§ Kay, S. R., Fiszbein, A., & Opler, L. A. (1987). The Positive and Negative Syndrome Scale (PANSS) for schizophrenia. Schizophrenia Bulleti.
Preston Williams III is Senior Partner & Chief Information Officer (CIO) at GBC® Global Services. He is a pioneer and futurist with 20+ years of Big 4, Fortune 500, Global 100 and entrepreneurial experience. That experience includes Senior Auditor with Price Waterhouse (PriceWaterhouseCoopers), Controller for Lynn-Phill, IT Consultant with McGladrey & Pullen and Andersen Consulting (Accenture) as well as Project Executive at IBM® Global Services. He also worked as the first Product Manager for Global Data Collection with Dun & Bradstreet (D&B) and the first Chief Information Officer (CIO) at Langston University.
From 2002 to 2004, Preston served as the first Chief Executive Officer (CEO) & Managing Partner at GBC®. Incorporated in Delaware, USA; the firm operates in Kenya, Liberia, Uganda and the United States. He has successfully implemented sound, reliable, dynamic and robust IT systems integration and financial management solutions in North America, Asia Pacific (APAC), Latin America (LATAM), Europe, the Middle East and Africa (EMEA).
Mr. Williams is Chairman of the GBC® Board of Directors, a recipient of the IBM® Global Services Leadership Award, a member of the Global CIO Think Tank and a member of the Internet Evolution Executive Clan.
20 Emerging influencers in 2020 for big dataRiver11river
You might have not heard most of these names yet, but you surely will soon. This list is designed to recognize emerging talent in the fields of data and analytics – mostly entrepreneurs and up-and-coming talent who are informing, educating and inspiring others through data. They come from different sectors and backgrounds – from data architecture to visualization. The one thing that unites them is their passion for data.
The document outlines plans for a business-oriented social network called WeSpline that aims to connect enterprises and startups globally. It will use intelligent algorithms to help enterprises discover new technologies through efficient searching and recommendations. Startups will be able to actively search for and connect with potential enterprise clients around the world. The network will bring together various players in the innovation ecosystem such as investors, universities, and service providers to foster collaboration. It will leverage technologies like machine learning and natural language processing to continuously improve user experience.
Healthcare organizations are unique business entities that present challenges for optimally organizing governance, people, and services for next-generation BI. Learning from other industries that have adopted the concept of the business intelligence competency center (BICC), this article explores the available options and evaluates which service and organizational model appears to best fit healthcare providers and similarly complex organizations.
The document summarizes Reputation.com's online reputation management services. It states that over 67% of executives believe the economy is now reputation-based. 76% of executives expect to be searched online but 22% have never searched themselves. 69% of directors see reputation risk as the most important business risk. Reputation.com claims to help individuals and businesses control their online reputation and privacy. It is a pioneer in the field with customers in over 100 countries offering reputation management solutions.
Amira Saleh has over 20 years of experience in information technology management, web application development, and biosciences research. She is seeking a challenging position that utilizes her skills in IT, project management, marketing, and biological research. Her experience includes roles as an IT consultant, network manager, systems analyst, and graduate research assistant studying carcinogen risk assessment and marine bacteria.
Information Technology Innovator David Ward 2011ward2dr
David Ward is an experienced senior technology executive with over 20 years of experience in leadership roles at major financial institutions. He has expertise in areas such as technology strategies, business transformation, enterprise systems, infrastructure, and mergers and acquisitions. Throughout his career, he has delivered value to shareholders and improved customer satisfaction. Currently, he is seeking new opportunities to apply his experience and drive innovation.
BUSINESS INTELLIGENCE AND ANALYTICS RAMESH SHARDA DU.docxfelicidaddinwoodie
BUSINESS INTELLIGENCE
AND ANALYTICS
RAMESH SHARDA
DURSUN DELEN
EFRAIM TURBAN
TENTH EDITION
.•
TENTH EDITION
BUSINESS INTELLIGENCE
AND ANALYTICS:
SYSTEMS FOR DECISION SUPPORT
Ramesh Sharda
Oklahoma State University
Dursun Delen
Oklahoma State University
Efraim Turban
University of Hawaii
With contributions by
J.E.Aronson
Tbe University of Georgia
Ting-Peng Liang
National Sun Yat-sen University
David King
]DA Software Group, Inc.
PEARSON
Boston Columbus Indianapolis New York San Francisco Upper Saddle River
Amsterdam Cape Town Dubai London Madrid Milan Munich Paris Montreal Toronto
Delhi Mexico City Sao Paulo Sydney Hong Kong Seoul Singapore Taipei Tokyo
Editor in Chief: Stephanie Wall
Executive Editor: Bob Horan
Program Manager Team Lead: Ashley Santora
Program Manager: Denise Vaughn
Executive Marketing Manager: Anne Fahlgren
Project Manager Team Lead: Judy Leale
Project Manager: Tom Benfatti
Operations Specialist: Michelle Klein
Creative Director: Jayne Conte
Cover Designer: Suzanne Behnke
Digital Production Project Manager: Lisa
Rinaldi
Full-Service Project Management: George Jacob,
Integra Software
Solution
s.
Printer/Binder: Edwards Brothers Malloy-Jackson
Road
Cover Printer: Lehigh/Phoenix-Hagerstown
Text Font: Garamond
Credits and acknowledgments borrowed from other sources and reproduced, with permission, in this textbook
appear on the appropriate page within text.
Microsoft and/ or its respective suppliers make no representations about the suitability of the information
contained in the documents and related graphics published as part of the services for any purpose. All such
documents and related graphics are provided "as is" without warranty of any kind. Microsoft and/or its
respective suppliers hereby disclaim all warranties and conditions with regard to this information, including
all warranties and conditions of merchantability, whether express, implied or statutory, fitness for a particular
purpose, title and non-infringement. In no event shall Microsoft and/or its respective suppliers be liable for
any special, indirect or consequential damages or any damages whatsoever resulting from loss of use , data or
profits, whether in an action of contract, negligence or other tortious action, arising out of or in connection
with the use or performance of information available from the services.
The documents and related graphics contained herein could include technical inaccuracies or typographical
errors. Changes are periodically added to the information here in. Microsoft and/or its respective suppliers may
make improvements and/or changes in the product(s) and/ or the program(s) described herein at any time.
Partial screen shots may be viewed in full within the software version specified.
Microsoft® Windows®, and Microsoft Office® are registered trademarks of the Microsoft Corporation in the U.S.A.
and other countries. This book is not .
BUSINESS INTELLIGENCE AND ANALYTICS RAMESH SHARDA DUTawnaDelatorrejs
BUSINESS INTELLIGENCE
AND ANALYTICS
RAMESH SHARDA
DURSUN DELEN
EFRAIM TURBAN
TENTH EDITION
.•
TENTH EDITION
BUSINESS INTELLIGENCE
AND ANALYTICS:
SYSTEMS FOR DECISION SUPPORT
Ramesh Sharda
Oklahoma State University
Dursun Delen
Oklahoma State University
Efraim Turban
University of Hawaii
With contributions by
J.E.Aronson
Tbe University of Georgia
Ting-Peng Liang
National Sun Yat-sen University
David King
]DA Software Group, Inc.
PEARSON
Boston Columbus Indianapolis New York San Francisco Upper Saddle River
Amsterdam Cape Town Dubai London Madrid Milan Munich Paris Montreal Toronto
Delhi Mexico City Sao Paulo Sydney Hong Kong Seoul Singapore Taipei Tokyo
Editor in Chief: Stephanie Wall
Executive Editor: Bob Horan
Program Manager Team Lead: Ashley Santora
Program Manager: Denise Vaughn
Executive Marketing Manager: Anne Fahlgren
Project Manager Team Lead: Judy Leale
Project Manager: Tom Benfatti
Operations Specialist: Michelle Klein
Creative Director: Jayne Conte
Cover Designer: Suzanne Behnke
Digital Production Project Manager: Lisa
Rinaldi
Full-Service Project Management: George Jacob,
Integra Software
Solution
s.
Printer/Binder: Edwards Brothers Malloy-Jackson
Road
Cover Printer: Lehigh/Phoenix-Hagerstown
Text Font: Garamond
Credits and acknowledgments borrowed from other sources and reproduced, with permission, in this textbook
appear on the appropriate page within text.
Microsoft and/ or its respective suppliers make no representations about the suitability of the information
contained in the documents and related graphics published as part of the services for any purpose. All such
documents and related graphics are provided "as is" without warranty of any kind. Microsoft and/or its
respective suppliers hereby disclaim all warranties and conditions with regard to this information, including
all warranties and conditions of merchantability, whether express, implied or statutory, fitness for a particular
purpose, title and non-infringement. In no event shall Microsoft and/or its respective suppliers be liable for
any special, indirect or consequential damages or any damages whatsoever resulting from loss of use , data or
profits, whether in an action of contract, negligence or other tortious action, arising out of or in connection
with the use or performance of information available from the services.
The documents and related graphics contained herein could include technical inaccuracies or typographical
errors. Changes are periodically added to the information here in. Microsoft and/or its respective suppliers may
make improvements and/or changes in the product(s) and/ or the program(s) described herein at any time.
Partial screen shots may be viewed in full within the software version specified.
Microsoft® Windows®, and Microsoft Office® are registered trademarks of the Microsoft Corporation in the U.S.A.
and other countries. This book is not ...
Curtis HillTopic 07 Assignment Long-Term Care ChartHA30.docxdorishigh
The document discusses different types of long-term care services and compares their cost effectiveness, efficacy, and levels of patient satisfaction. It provides a chart that rates nursing homes, continuing care retirement communities, supportive housing programs, community services, and home care based on these three criteria. The document aims to help complete an assignment comparing and contrasting long-term care services.
Auditing A Practical Approach with Data Analytics, 2e Raymond Johnson, Laura ...DingBo3
This document provides an overview of the second edition of the textbook "Auditing: A Practical Approach with Data Analytics" by Raymond N. Johnson and Laura D. Wiley. Some key details include:
- The textbook takes a practical approach to teaching auditing concepts and incorporates data analytics.
- Chapters have been updated throughout to reflect recent changes in auditing standards, the CPA exam, and the increasing role of technology in auditing.
- New illustrations, examples, and discussions have been added on topics like audit evidence, internal controls, substantive procedures, and data analytics.
- The authors aim to help students develop critical thinking, ethical decision-making, and an ability to adapt to changes
eBook PDF textbook - Auditing A Practical Approach with Data Analytics, 2e Ra...EdwinPolack1
This document provides information about the second edition of the textbook "Auditing: A Practical Approach with Data Analytics" by Raymond N. Johnson and Laura D. Wiley.
The document includes the cover design, authors, editors involved in production, copyright information, table of contents, and dedication by the authors to their spouses for their support. It also provides brief biographies of the authors, Raymond N. Johnson and Laura D. Wiley, highlighting their professional experiences and areas of expertise in auditing.
Running head INFORMATION LITERACY 1INFORMATION LITERACY 2.docxwlynn1
Running head: INFORMATION LITERACY 1
INFORMATION LITERACY 2
INFORMATION LITERACY
GEN 499: General Education Capstone
October 14, 2019.
Ashford University Library has good resources for any academic material one wants to read. I am a business student and when I joined Ashford University I was a little worried about what might happen if I could not find the necessary academic materials to support my education. Another issue I found overwhelming at first was how to navigate the library database because there were so many options. If you click on a particular option at times they are not relevant to the topic under research. A friend directed me on how to navigate in the Databases A-Z. Nowadays it is easier because I followed all the instructions to the later.
I like the ProQuest Database because it has so many options someone can choose from and the resources are very helpful, (Brannon, 2017). I do not have any concerns but don't like the fact that Ashford Library pulls up student's research papers as references that have to be changed. In these databases, one has to use the subject topic to find readings or scholarly articles, (Nelson & Huffman, 2015). Some databases may not have the articles one is looking for because they are all specified for certain course work, if you are new it can be very overwhelming. I also realized that if I download a full PDF then all the details about the authors and references will be readily available.
Ashford University Library has improved skills in my business course because before the exams approach I am always equipped with adequate information. This keeps me away from using search engines like Google and some of the resources may not be credible. The best part with the resources that come from Ashford Library is that they help one reduce the reference format mistakes because they are already located on the articles, (Omar, et.al, 2018). The newspapers and other articles that are on the internet can be very difficult to cite at times. In general, the Ashford University Library is effective and reliable because it has good resources and citations which are accurate.
References
Brannon, P. C. (2017). ProQuest Regulatory Insight. Law Library Journal, 109(3), 484.
Nelson, N., & Huffman, J. (2015). Predatory journals in library databases: How much should we worry?. The serials librarian, 69(2), 169-192.
Omar, D., Preater, A., Clark, I., & Liebert, R. J. (2018). Inclusive reading lists: how libraries can support student and academic leadership.
INFORMATION
GOVERNANCE
Founded in 1807, John Wiley & Sons is the oldest independent publishing company in
the United States. With offi ces in North America, Europe, Asia, and Australia, Wiley
is globally committed to developing and marketing print and electronic products and
services for our customers’ professional and personal knowledge and understanding.
The Wiley CIO series provides information, tools, and insights to IT executives
a.
Running head INFORMATION LITERACY 1INFORMATION LITERACY 2.docxjeanettehully
Running head: INFORMATION LITERACY 1
INFORMATION LITERACY 2
INFORMATION LITERACY
GEN 499: General Education Capstone
October 14, 2019.
Ashford University Library has good resources for any academic material one wants to read. I am a business student and when I joined Ashford University I was a little worried about what might happen if I could not find the necessary academic materials to support my education. Another issue I found overwhelming at first was how to navigate the library database because there were so many options. If you click on a particular option at times they are not relevant to the topic under research. A friend directed me on how to navigate in the Databases A-Z. Nowadays it is easier because I followed all the instructions to the later.
I like the ProQuest Database because it has so many options someone can choose from and the resources are very helpful, (Brannon, 2017). I do not have any concerns but don't like the fact that Ashford Library pulls up student's research papers as references that have to be changed. In these databases, one has to use the subject topic to find readings or scholarly articles, (Nelson & Huffman, 2015). Some databases may not have the articles one is looking for because they are all specified for certain course work, if you are new it can be very overwhelming. I also realized that if I download a full PDF then all the details about the authors and references will be readily available.
Ashford University Library has improved skills in my business course because before the exams approach I am always equipped with adequate information. This keeps me away from using search engines like Google and some of the resources may not be credible. The best part with the resources that come from Ashford Library is that they help one reduce the reference format mistakes because they are already located on the articles, (Omar, et.al, 2018). The newspapers and other articles that are on the internet can be very difficult to cite at times. In general, the Ashford University Library is effective and reliable because it has good resources and citations which are accurate.
References
Brannon, P. C. (2017). ProQuest Regulatory Insight. Law Library Journal, 109(3), 484.
Nelson, N., & Huffman, J. (2015). Predatory journals in library databases: How much should we worry?. The serials librarian, 69(2), 169-192.
Omar, D., Preater, A., Clark, I., & Liebert, R. J. (2018). Inclusive reading lists: how libraries can support student and academic leadership.
INFORMATION
GOVERNANCE
Founded in 1807, John Wiley & Sons is the oldest independent publishing company in
the United States. With offi ces in North America, Europe, Asia, and Australia, Wiley
is globally committed to developing and marketing print and electronic products and
services for our customers’ professional and personal knowledge and understanding.
The Wiley CIO series provides information, tools, and insights to IT executives
a ...
Kurt Niziak is an IT consultant and trainer with over 25 years of experience solving complex problems. He specializes in change management, training, documentation, and presentations. Niziak holds numerous software certifications including Microsoft Office, Windows, and medical software. He has trained over 3,000 individuals and provided technical support for major law firms. Niziak's experience includes positions at the Massachusetts Department of Mental Health, CompuWorks Systems Inc., and Advanced Technology Corporation where he served as a Configuration Control Manager receiving a Secret Level Security Clearance.
Kurt Niziak is an IT consultant and trainer with over 25 years of experience solving complex problems. He specializes in change management, training, documentation, and presentations. Niziak holds numerous software certifications including Microsoft Office, Windows, and medical software. He has extensive experience providing software training to legal firms, government agencies, and healthcare organizations. Niziak's background includes roles as a training manager, consultant, and configuration control manager for the U.S. Navy.
Big Data Startups - Top Visualization and Data Analytics Startupswallesplace
1010Data provides a cloud-based big data analytics platform that allows customers to analyze large datasets using simple interfaces. Their platform offers fast data processing, scalability, and tools for data integration, visualization, and sharing insights. Major customers include companies in financial services, retail, consumer packaged goods, telecom, and healthcare that use 1010Data to gain insights from large customer and transactional datasets.
BUSINESS INTELLIGENCE AND ANALYTICS RAMESH SHARDA DUjenkinsmandie
BUSINESS INTELLIGENCE
AND ANALYTICS
RAMESH SHARDA
DURSUN DELEN
EFRAIM TURBAN
TENTH EDITION
.•
TENTH EDITION
BUSINESS INTELLIGENCE
AND ANALYTICS:
SYSTEMS FOR DECISION SUPPORT
Ramesh Sharda
Oklahoma State University
Dursun Delen
Oklahoma State University
Efraim Turban
University of Hawaii
With contributions by
J.E.Aronson
Tbe University of Georgia
Ting-Peng Liang
National Sun Yat-sen University
David King
]DA Software Group, Inc.
PEARSON
Boston Columbus Indianapolis New York San Francisco Upper Saddle River
Amsterdam Cape Town Dubai London Madrid Milan Munich Paris Montreal Toronto
Delhi Mexico City Sao Paulo Sydney Hong Kong Seoul Singapore Taipei Tokyo
Editor in Chief: Stephanie Wall
Executive Editor: Bob Horan
Program Manager Team Lead: Ashley Santora
Program Manager: Denise Vaughn
Executive Marketing Manager: Anne Fahlgren
Project Manager Team Lead: Judy Leale
Project Manager: Tom Benfatti
Operations Specialist: Michelle Klein
Creative Director: Jayne Conte
Cover Designer: Suzanne Behnke
Digital Production Project Manager: Lisa
Rinaldi
Full-Service Project Management: George Jacob,
Integra Software
Solution
s.
Printer/Binder: Edwards Brothers Malloy-Jackson
Road
Cover Printer: Lehigh/Phoenix-Hagerstown
Text Font: Garamond
Credits and acknowledgments borrowed from other sources and reproduced, with permission, in this textbook
appear on the appropriate page within text.
Microsoft and/ or its respective suppliers make no representations about the suitability of the information
contained in the documents and related graphics published as part of the services for any purpose. All such
documents and related graphics are provided "as is" without warranty of any kind. Microsoft and/or its
respective suppliers hereby disclaim all warranties and conditions with regard to this information, including
all warranties and conditions of merchantability, whether express, implied or statutory, fitness for a particular
purpose, title and non-infringement. In no event shall Microsoft and/or its respective suppliers be liable for
any special, indirect or consequential damages or any damages whatsoever resulting from loss of use, data or
profits, whether in an action of contract, negligence or other tortious action, arising out of or in connection
with the use or performance of information available from the services.
The documents and related graphics contained herein could include technical inaccuracies or typographical
errors. Changes are periodically added to the information herein. Microsoft and/or its respective suppliers may
make improvements and/or changes in the product(s) and/ or the program(s) described herein at any time.
Partial screen shots may be viewed in full within the software version specified.
Microsoft® Windows®, and Microsoft Office® are registered trademarks of the Microsoft Corporation in the U.S.A.
and other countries. This book is not sp ...
BUSINESS INTELLIGENCE AND ANALYTICS RAMESH SHARDA DUChereCoble417
BUSINESS INTELLIGENCE
AND ANALYTICS
RAMESH SHARDA
DURSUN DELEN
EFRAIM TURBAN
TENTH EDITION
.•
TENTH EDITION
BUSINESS INTELLIGENCE
AND ANALYTICS:
SYSTEMS FOR DECISION SUPPORT
Ramesh Sharda
Oklahoma State University
Dursun Delen
Oklahoma State University
Efraim Turban
University of Hawaii
With contributions by
J.E.Aronson
Tbe University of Georgia
Ting-Peng Liang
National Sun Yat-sen University
David King
]DA Software Group, Inc.
PEARSON
Boston Columbus Indianapolis New York San Francisco Upper Saddle River
Amsterdam Cape Town Dubai London Madrid Milan Munich Paris Montreal Toronto
Delhi Mexico City Sao Paulo Sydney Hong Kong Seoul Singapore Taipei Tokyo
Editor in Chief: Stephanie Wall
Executive Editor: Bob Horan
Program Manager Team Lead: Ashley Santora
Program Manager: Denise Vaughn
Executive Marketing Manager: Anne Fahlgren
Project Manager Team Lead: Judy Leale
Project Manager: Tom Benfatti
Operations Specialist: Michelle Klein
Creative Director: Jayne Conte
Cover Designer: Suzanne Behnke
Digital Production Project Manager: Lisa
Rinaldi
Full-Service Project Management: George Jacob,
Integra Software
Solution
s.
Printer/Binder: Edwards Brothers Malloy-Jackson
Road
Cover Printer: Lehigh/Phoenix-Hagerstown
Text Font: Garamond
Credits and acknowledgments borrowed from other sources and reproduced, with permission, in this textbook
appear on the appropriate page within text.
Microsoft and/ or its respective suppliers make no representations about the suitability of the information
contained in the documents and related graphics published as part of the services for any purpose. All such
documents and related graphics are provided "as is" without warranty of any kind. Microsoft and/or its
respective suppliers hereby disclaim all warranties and conditions with regard to this information, including
all warranties and conditions of merchantability, whether express, implied or statutory, fitness for a particular
purpose, title and non-infringement. In no event shall Microsoft and/or its respective suppliers be liable for
any special, indirect or consequential damages or any damages whatsoever resulting from loss of use, data or
profits, whether in an action of contract, negligence or other tortious action, arising out of or in connection
with the use or performance of information available from the services.
The documents and related graphics contained herein could include technical inaccuracies or typographical
errors. Changes are periodically added to the information herein. Microsoft and/or its respective suppliers may
make improvements and/or changes in the product(s) and/ or the program(s) described herein at any time.
Partial screen shots may be viewed in full within the software version specified.
Microsoft® Windows®, and Microsoft Office® are registered trademarks of the Microsoft Corporation in the U.S.A.
and other countries. This book is not sp ...
Similar to Data Science & Big Data Analytics Discovering, Analyzing.docx (20)
Delusional Disorders
Pakistani hought Processes
BACKGROUND
The client is a 34-year-old Pakistani female who moved to the United States in her late teens/early 20s. She is currently in an “arranged” marriage (her husband was selected for her since she was 9 years old). She presents to your office today following a 21 day hospitalization for what was diagnosed as “brief psychotic disorder.” She was given this diagnosis as her symptoms have persisted for less than 1 month.
Prior to admission, she was reporting visions of Allah, and over the course of a week, she believed that she was the prophet Mohammad. She believed that she would deliver the world from sin. Her husband became concerned about her behavior to the point that he was afraid of leaving their 4 children with her. One evening, she was “out of control” which resulted in his calling the police and her subsequent admission to an inpatient psych unit.
During today’s assessment, she appears quite calm, and insists that the entire incident was “blown out of proportion.” She denies that she believed herself to be the prophet Mohammad and states that her husband was just out to get her because he never loved her and wanted an “American wife” instead of her. She tells you that she knows this because the television is telling her so.
She currently weighs 140 lbs, and is 5’ 5”
SUBJECTIVE
Client reports that her mood is “good.” She denies auditory/visual hallucinations, but believes that the television does talk to her. She believes that Allah sends her messages through the TV. At times throughout the clinical interview, she becomes hostile towards the PMHNP, but then calms down.
You reviewed her hospital records and find that she has been medically worked up by a physician who reported her to be in overall good health. Lab studies were all within normal limits.
Client admits that she stopped taking her Risperdal about a week after she got out of the hospital because she thinks her husband is going to poison her so that he can marry an American woman.
MENTAL STATUS EXAM
The client is alert, oriented to person, place, time, and event. She is dressed appropriately for the weather and time of year. She demonstrates no noteworthy mannerisms, gestures, or tics. Her speech is slow and at times, interrupted by periods of silence. Self-reported mood is euthymic. Affect constricted. Although the client denies visual or auditory hallucinations, she appears to be “listening” to something. Delusional and paranoid thought processes as described, above. Insight and judgment are impaired. She is currently denying suicidal or homicidal ideation.
The PMHNP administers the PANSS which reveals the following scores:
-40 for the positive symptoms scale
-20 for the negative symptom scale
-60 for general psychopathology scale
Diagnosis: Schizophrenia, paranoid type
RESOURCES
§ Kay, S. R., Fiszbein, A., & Opler, L. A. (1987). The Positive and Negative Syndrome Scale (PANSS) for schizophrenia. Schizophrenia Bulleti.
Deloitte’s 2020 Global Blockchain SurveyFrom promise to re.docxrandyburney60861
Deloitte’s 2020 Global
Blockchain Survey
From promise to reality
DELOITTE BLOCKCHAIN
At Deloitte, our people collaborate globally with clients, regulators, and policymakers on how
blockchain and digital assets are changing the face of business and government today. New
ecosystems are developing blockchain-based infrastructure and solutions to create innovative
business models and disrupt traditional ones. This is occurring in every industry and in most
jurisdictions globally. Our deep business acumen and global industry-leading audit, consulting,
tax, risk, and financial advisory services help organizations across industries achieve their
varying blockchain aspirations. Reach out to our leaders to discuss the evolving momentum of
blockchain and digital assets, prioritizing initiatives, and managing the opportunities and pain
points associated with blockchain adoption efforts. To learn more, let's talk.
https://www2.deloitte.com/us/en/pages/consulting/solutions/blockchain-solutions-and-services.html
Introduction: The evolution of blockchain 2
A more “real” reality for blockchain 4
Digital assets today and tomorrow 9
Cybersecurity 13
Global digital identity 15
Regulatory considerations 17
Governance in blockchain consortia 19
Regional analysis 21
Concluding thoughts: The road taken 24
Appendix 25
Endnotes 36
Contents
2
Introduction: The
evolution of blockchain
MORE THAN A decade has passed since the introduction of what we know today as blockchain technology. Over that time,
the promise of what the technology could offer
businesses and industries has evolved from a
cryptocurrency payment platform to something
bigger, game-changing, and truly disruptive. In
recent years, we have seen sentiment about
blockchain’s potential similarly evolving, along
with companies directing actual investment
dollars toward applications.
In Deloitte’s 2019 Global Blockchain Survey,
we observed this continuing trend in thinking
and investment, even if some vestiges of doubt
and old-school thinking remained about the
technology’s promise.1 This year’s survey
suggests that those doubts are fading further,
and that blockchain is solidly entrenched in
the strategic thinking of organizations across
industries, sectors, and applications.
There are more substantive examples in
the marketplace of how both startups and
mature businesses are deploying blockchain.
Organizations appear to be more committed than
ever to blockchain and are demonstrating this by
implementing it as part of their normal course
of business.
That’s the key takeaway from our 2020 Global
Blockchain Survey, which finds that leaders no
longer consider the technology groundbreaking
and merely promising—they now see it as integral
to organizational innovation. This year, the C-suite
is putting money and resources behind blockchain
as a strategic solution in more meaningful and
tangible ways—in projects big and not so big—
putting i.
DELL COMPANY’ Application of the accounting theories on the comp.docxrandyburney60861
DELL COMPANY’
Application of the accounting theories on the company
-stakeholder theory
-shareholder theory
-conceptual framework of a company
• Purpose
• Example
Topic: Sustainability Reporting in Accounting
Task details: Research the current state of Sustainability Reporting, including the issues, practices etc. using higher order analysis and explaining the implications for various stakeholders in relation to financial decision making.
Report: 1500 wordsexcluding the references ; executive summary, table of contents, appropriate headings and subheadings, recommendations/ findings/ conclusions, in-text referencing and reference list( Harvard -anglia style)
Assessment Type: Group report– combined group and individual assessment task.
Purpose: This assessment is designed to allow students to research and analyse current social issues in accounting and evaluate their impact on various stakeholders. As a group assessment, it further develops students’ team working s******s
Value: Total value is 30% made up of 10% Group marks for report plus 20% individual marks for presentation. This assignment marks will be scaled to a mark out of 30 total subject marks.
Topic: Sustainability Reporting in Accounting
Task Details: Groups are to research the current state of Sustainability Reporting. As a result of their research groups detailing the current state of sustainability reporting including the issues, practices, etc using higher order analysis and explaining the implications for various stakeholders in relation to financial decision making. The report should conclude with supported specific recommendations as to how organisations and their accounting advisors should proceed in light of the analysis.
Research requirements: Students need to support their analysis with reference from the text and minimum of ten (10) suitable, reliable, current and academically acceptable sources – check with your tutor if unsure of the validity of sources. Groups seeking Credit or above grades should support their analysis with increased number of reference sources comparable to the grade they are seeking.
Group Report 1500 + 10% word report format – Word .doc or .docx. Title page, executive summary, table of contents, appropriate headings and sub-headings, recommendations/findings/conclusions, in-text referencing and reference list (Harvard – Anglia style), attachments if relevant. Single spaced, font Times New Roman 12pt, Calibri 11pt or Arial 10pt.
Additional details:
DELL COMPANY
• Fina******** cost**************** cost*********** cost ********wcase the benefit equally
• Corporate culture
• Look at bigger perspective
• Showing impacts: shareholders, academic, media etc
• Directors release reports
• Capital market research
• AMP general meeting
• Showvcase the good and bad of the DELL COMPANY:ACCOUNTING ISSUES
.
Deliverable Length10–15 slides not including title and refere.docxrandyburney60861
Deliverable Length:
10–15 slides not including title and reference slides with 150-200 words speaker notes
OBJECTIVES
Create a PowerPoint presentation with speaker notes to educate others regarding the development of an operational budget and a capital budget. Be sure to include the following:
Provide the process for developing an operational budget.
Provide the process for developing a capital budget.
Differentiate between the operational and capital budgets.
Explain how the capital budget is required for strategic management.
Please submit your assignment.
.
Deliverable 6 - Using Business VisualsCompetencyExamine and de.docxrandyburney60861
Deliverable 6 - Using Business Visuals
Competency
Examine and design visual media communication to produce effective business materials.
Scenario
You are a website designer and are currently being considered as the designer for a complete redesign of a medical facility's website page. They have not updated their website in over 15 years due to the lack of staff. The website needs the redesign to target the specific visual preferences of patients and medical clients. They have several other designers that they are interviewing, and you decide to create a video presentation to impress them to choose you as their website designer.
As you begin your presentation, you decide that your design will include the following visual concepts and elements:
1. Visual Organization
2. Visual Simplicity
3. Visual Interactivity
4. Charts and Graphs
5. Images
In designing these visual elements, you keep the target audience of patients and medical clients as the main focus. You carefully consider what these types of people wish to see at a medical facility. You also consider what visual elements will attract the eye, and yet enhance a specific mood and emotional response for viewers.
After creating the website, you record your explanation of your visual design choices in an audio/video screen share.
Your presentation should be a maximum of 5 minutes.
:.:
4i/
w5
C*ú \rse G''t"[email protected]
O,+ tIo,"d, .,*¡?,-p*.+ '
È , l. t^.., * {.i *-oJ ç ,-^)
ô*t ¿a*lti.--', U*e- p-i.^-*.':&* -+
^c-cnsL u1uþLuaJl.*4t"' ,is ln ut*€., t **
u*,*
A
4+-
' L645 $. San Pablo Avenrle, Fnèsnor CaLifornia
A SingLo-Fani'Ly ResÍdtential" Property
fon
'À P P R A T S A T,+
ü,nlvergitü of 0a1í fo:r¡ia
tserkeløy, CaLÍfornLa
'(¿tt¡¡¡ 'Mr. Ken garefa)
as of
Idaroh. 61 196X
by
of
i!¡l
Sresno,
It
,".
TASLD OF CO}fTETVTS
%
ïntroduction
[it]-ePage... ..... .. . .
ï¡etter of Transnittal . . . , . . . . .
SunnarXr of SaLient Facts and Important
Pu.rpose of theAppraisal . r. . ¡ . .
Market"ij'aluelefínítion. ....
Photographs of Subject Property. . . .
taaa
alaa
Conclusions
aaaaao
aaaaaa
ra
a
a
a
o
a
a
Pa.ge
i
Íii
i-v
v
v
vi
a
a
Descrfption, AnaLysis, and. Conclusions
RegionalData..., o. r ..
CityData... o. o.. .....
Neighborhood. Data. . . . . . . . .
Site Data. . . . . . . . . r . . .
ïmprovementÐata... r . . . o.
I,and.Valug..,.... r. r i.
a L
b5
3-4
5-6
7-9
10
a
t
a
a
11-15
14
I5
15
a
t
a
The Value EstÍmates
Cost Approach to Value. . o . . . . . . . .
Market Data Approach to Value . . . .
Analysís and CoruelatÍon of Value Estímates
ConcLusion of Value. . . . . . . . . . r . . . .
a
ï,imiting CondÍtÍons.
Certífi catíon. . . .
ExhibÍt
Exhlbit
Exhibit
ExhÍbit
Exhibit
Exhibit
Exhíbít
Exhiblt
Exhlbit
Exhibit
ExhÍbit
Erchibit
r.6
17
1B
"19.20
.2L
.22
.27
.24
.25
,26
.28
.29
.3O
. 3l-77
a
aQualifieatíons of the AppraJ-ser a
Ad.denda
RegÍonal- Map. . . . . . . . . . . .
CountyMap. . . . .. . . . . . .. .
Cíty and. Neighborhood Map. . . . . .
Deliverable 5 - Proposed HR Initiatives Presentation
Assignment Content
Competency
Assess the impact of Human Resources practices on the success of the organization and its human capital.
Student Success Criteria
View the grading rubric for this deliverable by selecting the “This item is graded with a rubric” link, which is located in the
Details & Information
pane.
Scenario
The CEO of a technology-services B2B company has just announced a momentous change in its strategic direction. He wants to begin offering full turn-key technology facilities to customers instead of just offering additional technology solutions to customers’ existing facilities. This change will require many new employees with different knowledge, skills, and abilities than the current workforce. It will also require dramatically different approaches to marketing and sales. The HR Director asks you to help develop a proposal for a strategic HR plan to deal with the change and present it to the top leadership.
The plan will support the change by proposing HR initiatives related to:
Talent acquisition (new employees)
Talent development (current employees)
Instructions
Create a
presentation, with speaker’s notes
, that:
Describes at least one proposed initiative for each: talent acquisition and talent development.
Identifies metrics to be used to measure the success of initiatives.
Describes a realistic and effective data collection plan for the metrics you selected.
Identifies potential ethical issues and/or risks related to the proposed data collection plan.
Provides a risk mitigation plan as needed.
Details how each initiative would impact the business objectives of the organization.
Provides attribution for credible sources used in the presentation.
.
Deliverable 4 - Diversity and Inclusion PolicyAssignment Con.docxrandyburney60861
Deliverable 4 - Diversity and Inclusion Policy
Assignment Content
Competency
Create policies and procedures that manage risk, are legally compliant, and align to organizational strategy.
Student Success Criteria
View the grading rubric for this deliverable by selecting the “This item is graded with a rubric” link, which is located in the
Details & Information
pane.
Scenario
You are the HR Director of a 3-star hotel chain that has locations throughout the United States. In a review of last year’s recruitment, selection, and hiring data, you realize that these HR practices have not resulted in the level of employee diversity desired by the CEO. Currently, there is no formal diversity and inclusion policy for the organization. Therefore, you propose that a new policy document be written and communicated to all employees, as well as be incorporated into all training programs for those involved in the recruitment and selection processes. The CEO agrees with your proposal.
Instructions
Create a
diversity and inclusion policy
that:
Details the policy objective and scope.
Explains the difference between diversity and inclusion.
Outlines initiatives to promote diversity and inclusion throughout the organization.
Includes instructions for employees to follow if they feel they have been subjected to any treatment that is in violation of the policy and/or listed initiatives promoting diversity and inclusion.
Provides attribution for credible sources used in the policy.
.
Deliverable 4 - Global Environment ChallengesCompetencyC.docxrandyburney60861
Deliverable 4 - Global Environment Challenges
Competency
Create solutions for organizational and leadership challenges in a global environment.
Scenario
You are the HR Training and Development Manager at Lots of Stuff International, a global company. The company has offices around the globe, which requires employees to work with peers in multiple countries in cross-functional and cross-global teams. The company has recently conducted an employee engagement survey across all areas of the company. The results indicate a lack of engagement and satisfaction of employees who work in these global-cross functional teams. Upon investigation, you discover that employees indicate dissatisfaction with a lack of community and social interaction in their teams. They indicate this may be a function of culture and religious differences, time zone differences, or work ethic differences. This has led employees to be less invested in working together. The CEO, Ms. Amelia Rienhardt, has tasked you with creating a plan to develop community within these teams across the global workforce, with the end goal of enhancing engagement and satisfaction. This plan will be unveiled to all teams in a company-wide presentation.
Instructions
Create a presentation, including speaker notes, presenting your global employee engagement plan. The presentation should:
Assess factors that may lead to a lack of employee engagement and satisfaction in working in global cross-functional teams.
Address each identified factor from your assessment:
Cultural differences
Religious differences
Time zone differences
Work ethic differences
Recommend a process to develop communication channels in diverse teams.
Provide counsel on dealing with cross-cultural conflict.
Develop a strategy for ongoing cross-cultural team building.
Identify ideas for enhancing social interactions between cross-cultural work teams.
Include a plan for the use of technology for employee engagement and social interaction.
Be sure to provide proper attribution for credible sources used in the presentation.
.
Deliverable 03 - Humanities (Test-Out Sophia Replacement)
Competency
Formulate, express, and support individual perspectives on diverse works and issues.
Instructions
You will act as a critic for some of the main subjects covered in the humanities. You will conduct a series of short, evaluative critiques of film, philosophy, literature, music, and myth. You will respond to five different prompts, and each response should include an analysis of the topics using terminology unique to that subject area and should include an evaluation as to why the topic stands the test of time. The five prompts are as follows:
Choose a film and offer an analysis of why it is an important film, and discuss it in terms of film as art. Your response should be more than a summary of the film.
Imagine you had known Plato and Aristotle and you had a conversation about how we
fall in love
. Provide an overview of how Plato would explain falling in love, and then provide an overview of how Aristotle might explain falling in love.
Compare and contrast the two poems below:
LOVE’S INCONSISTENCY
I find no peace, and all my war is done;
I fear and hope, I burn and freeze likewise
I fly above the wind, yet cannot rise;
And nought I have, yet all the world I seize on;
That looseth, nor locketh, holdeth me in prison, And holds me not, yet can I ’scape no wise;
Nor lets me live, nor die, at my devise,
And yet of death it giveth none occasion.
Without eyes I see, and without tongue I plain;
I wish to perish, yet I ask for health;
I love another, and yet I hate myself;
I feed in sorrow, and laugh in all my pain;
Lo, thus displeaseth me both death and life,
And my delight is causer of my grief.
Petrarch
After great pain a formal feeling comes—
The nerves sit ceremonious like tombs;
The stiff Heart questions—was it He that bore?
And yesterday—or centuries before?
The feet mechanical go round
A wooden way
Of ground or air or ought
Regardless grown,
A quartz contentment like a stone.
This is the hour of lead
Remembered if outlived
As freezing persons recollect
The snow—
First chill, then stupor, then
The letting go
Emily Dickinson
4. Compare and contrast these two pieces of music:
Beethoven’s Violin Romance No. 2
Scott Joplin’s Maple Leaf Rag
5.Explain in classical terms why a modern character is a hero. Choose from either Luke Skywalker, Indiana Jones, Bilbo Baggins, Harry Potter, Katniss Everdeen, or Ender Wiggins.
Grading Rubric
0
1
2
3
4
Category
Not Submitted
No pass
Competence
Proficiency
Mastery
Analysis
Not Submitted
Provides an explanation of the topic but doesn't use terminology common to the subject.
Provides an explanation of the topic using terminology common to the subject.
Provides a detailed explanation of the topic using terminology common to the subject.
Explains in great detail the topic using terminology common to the subject and references other ideas/works in that subject.
Evaluation
Not Submit.
The document provides instructions for a humanities assessment that requires critiquing and analyzing various topics through short responses. The assessment includes five prompts requiring analysis of a film as art, a discussion of how Plato and Aristotle would explain falling in love, a comparison of two poems, a comparison of two pieces of music, and an explanation of why a modern character exemplifies classical heroism. For each prompt, the response must include terminology specific to the subject area and an evaluation of why the topic stands the test of time.
DEFINITION a brief definition of the key term followed by t.docxrandyburney60861
DEFINITION
:
a brief definition of the key term followed by the APA reference for the term; this does not count in the word requirement.
SUMMARY
:
Summarize the article in your own words- this should be in the 150-200 word range. Be sure to note the article's author, note their credentials and why we should put any weight behind his/her opinions, research or findings regarding the key term.
ANALYSIS
:
Using 300-350 words, write a brief analysis, in your own words of how the article relates to the selected chapter Key Term. An analysis is not rehashing what was already stated in the article, but the opportunity for you to add value by sharing your experiences, thoughts and opinions. This is the most important part of the assignment.
REFERENCES
:
All references must be listed at the bottom of the submission--in APA format.
Be sure to use the headers in your submission to ensure that all aspects of the assignment are completed as required.
DiSCUSSION:
Describe social bandwidth and share an experience you’ve had with this concept within your previous interactions.
.
Definition of HIVAIDS. What are the symptoms and general characteri.docxrandyburney60861
Definition of HIV/AIDS. What are the symptoms and general characteristics of HIV/AIDS
What is the best way to bring awareness to AIDS in the school system.
Detailed explanation of a classroom activity, instructional technique, or program that can be utilized at a school to help a student with HIV/AIDS.
Use a minimum of three (3) resources including peer reviewed articles.
Use APA format.
.
Definition of Ethos and How to Use it1. Trustworthiness Does y.docxrandyburney60861
Definition of Ethos and How to Use it
1. Trustworthiness Does your audience believe you are a good person who can be trusted to tell the truth?
2. Similarity Does the writer try to get the reader to identify with him or her? This can be done through language
3. Authority Does the writer have formal or informal authority? Does the writer try to relate to the reader?
4. Reputation What are the expertise the writer uses? How many does he use? What are their areas of authority?
Logos: Logical reasoning, which has two bases:
Deductive reasoning, and
Inductive reasoning
Deductive Reasoning
Deductive reasoning generally start with one or more premises, and then comes to a conclusion from them. Premises can be facts, claims, evidence, or a previously proven conclusion. The key is that in a deductive argument, if the writer’s premises are true, then the conclusion must be true.
1. Education determines one’s class base.
2. One’s class base will shape one’s employment.
3. Therefore, education will determine one’s employment.
Inductive Reasoning
Inductive reasoning is similar in that it consists of premises, which lead to a conclusion. The difference is that the conclusion is not guaranteed to be true — we can only state it with some degree of confidence.
For example, consider the following inductive argument:
5. All Six Minutes articles you have read in the past were insightful. (premise)
6. This is a Six Minutes article. (premise)
Therefore, this article is insightful. (conclusion)
How to Identify Logos
Make it Understandable: Does the writer make the argument understandable? What tools does he or she use to do this?
Make it Logical: Does the arguments make sense? Or does the writer require the reader to make an extreme leap of faith? How easy is it for the writer to make a connection to the argument?
Make it Real: Does the writer make the argument real? Is the argument concrete or abstract?
The language plain language: Does the writer use technical jargon or is a portion of language used for a specific reader that isn’t familiar with the reader?
Does the writer use short words and phrases over long and convoluted counterparts?
The language is explicit: Does the writer make his or her argument plain? What techniques does he or she use to establish explicit argument?
The writer uses a couple premises, to establish his or her position? Are they relatable? Do they show relationship between them? “And these five advantages — capital costs, scheduling, inventory control, marketing, and employee satisfaction — together make this a winning proposal.”
Trace sequences or processes in order.
Does the writer jump around to different places or is there an order to his or her steps that create clarity or confusion for the reader?
Use comparisons, analogies, and metaphors.
Does the writer introduce new concepts, with an appropriate analogy which helps the audience understand the new concept in terms of how they already understand the old one?.
Definition Multimodal refers to works that use a combination .docxrandyburney60861
Definition:
Multimodal refers to works that use a combination of
modes
, including words, static images, moving images, and sounds.
Examples:
Works include print advertisements, commercials, videos, websites.
Assignment:
Write a summary-analysis paper on a multimodal advertisement.
Methods of critique
: Propaganda Techniques
Length:
2-3 pages (summary intro, two bodies, conclusion)
.
Definition Argument Essay AssignmentGoal Write a 1,500.docxrandyburney60861
Definition Argument Essay Assignment
Goal
Write a 1,500-1,750-word essay using five to seven academic resources in which you argue that a contested “case” involving the sale, trade, or donation of human organs fits (or does not fit) within a given category. A case may include a specific news article, story, or incident illustrating a dilemma or controversy relating to the exchange of human organs. The case does not need to be a court case.
Directions
Follow these steps when composing your essay:
1. Start by selecting a controversial case found in the media involving the sale, trade, or donation of human organs. For example, an appropriate case might include a story in the news about an organ broker, and the term to define might be “criminal.”
2. Decide what category you think your case belongs in, with the understanding that others may disagree with you about the definition of your category, and/or whether your chosen case matches your category.
3. In the opening of your essay, introduce the case you will examine and pose your definition question. Do not simply summarize here. Instead, introduce the issue and offer context.
4. To support your argument, define the boundaries of your category (criteria) by using a commonly used definition or by developing your own extended definition. Defining your boundaries simply means naming the criteria by which you will discuss your chosen case involving the sale, trade, or donation of human organs. If you determine, for example, that an organ broker is a criminal, what criteria constitute this? A criminal may intentionally harm others, which could be one of your criteria.
5. In the second part of your argument (the match), show how your case meets (or does not meet) your definition criteria. Perhaps by comparing or sizing up your controversial case to other cases can help you to develop your argument.
This essay is NOT simply a persuasive essay on the sale, trade, or donation of human organs. It is an argumentative essay where the writer explains what a term means and uses a specific case to explore the meaning of that term in depth.
First Draft Grading
· You will receive completion points for the first draft based upon the successful submission of a complete draft.
· Because your first draft is a completion grade, do not assume that this grade reflects or predicts the final grade. If you do not consider your instructor’s comments, you may be deducted points on your final draft.
Final Draft Grading
The essay will be graded using a rubric. Please review the rubric prior to beginning the assignment to become familiar with the assignment criteria and expectations.
Sources
· Include in-text citations and a references page in GCU Style for FIVE to SEVEN scholarly sources outside of class texts.
· These sources should be used to support any claims you make and should be present in the text of the essay.
· Use the GCU Library to help you find sources.
· Include this research in the paper i.
DEFINITION a brief definition of the key term followed by the APA r.docxrandyburney60861
DEFINITION: a brief definition of the key term followed by the APA reference for the term; this does not count in the word requirement.
SUMMARY: Summarize the article in your own words- this should be in the 150-200-word range. Be sure to note the article's author, note their credentials and why we should put any weight behind his/her opinions, research or findings regarding the key term.
DISCUSSION: Using 300-350 words, write a brief discussion, in your own words of how the article relates to the selected chapter Key Term. A discussion is not rehashing what was already stated in the article, but the opportunity for you to add value by sharing your experiences, thoughts and opinions. This is the mostimportant part of the assignment.
REFERENCES: All references must be listed at the bottom of the submission--in APA format. (continued) Be sure to use the headers in your submission to ensure that all aspects of the assignment are completed as required.
.
Defining Privacy in Employee Health ScreeningCases Ethical .docxrandyburney60861
Defining Privacy in Employee Health Screening
Cases: Ethical Ramifications Concerning
the Employee/Employer Relationship
V
Michele Simms
ABSTRACT. Issues of privacy and employee health screen-
ing rank as two of the most important ethical concerns
organizations will face in the next five years. Despite the
increasing numbers of social scientists researching personal
privacy and the current focus on workplace privacy rights as
one of the most dynamic areas of employment law, the
concept of privacy remains relatively ahstract. Understand-
ing how the courts defme privacy and use the expectation of
privacy standards is paramount given the strategic impor-
tance of the law as a legal socializing agent. This article
reports on two federal court decisions involving employer
drug and HIV testing whose determinations relied on
assumptions about the psychological dimensions of privacy.
How the courts define privacy, the outcome of this defini-
tion and the ethical ramifications as it affects the employee/
employer relationship are discussed.
Introduction
Each year American companies require employees to
submit to millions of blood and urine tests, x-rays,
and other medical and laboratory procedures. "In
fact, with the exception of typing and similar skills
tests for office and clerical employees, medical
screening is the most widely used pre-employment
test in all major employment categories" (BNA,
1987). It is predicted that in the next five years
testing will become a standard requirement when
applying for employment and/or health and life
insurance (Rothstein, 1989).
Michele Simms, as an adjunct professor of business communication
and organizational behavior, has taught at the University of
Michigan, Wayne State University and Oakland University
schools of business in Michigan. In addition to teaching, she
consults in the areas of worksite wellness, alternative dispute
resolution, transition management and change.
One factor contributing to the increase in em-
ployee health screening is the development of drug
abuse and AIDS as socially compelling public health
concerns (Falco and Cikins, 1989) that are costly to
employers, thus leading to an increase and/or initia-
tion of drug and HIV testing in both private and
public sector employment. One concern associated
with health screening is the issue of privacy and the
parallel communication activity of self-disclosure
that is used to express and maintain privacy states.
The issues of privacy and testing involve the
fundamental conflict of ethical principles between
individual rights and public safety needs and are the
subject today of increasing legislative and judicial
activity. A peripheral ethical concern that has not
been addressed but of equal importance is whether
the psychological dimensions of privacy are ac-
knowledged in court decisions involving employer
health screening practices. Traditionally lawyers and
judges Htigate and decide cases based upon principles
of legal positivi.
Define diversity” and inclusion” as applied to your pre.docxrandyburney60861
Define “diversity” and “inclusion” as applied to your presentation that will compare two healthcare organizations. Describe the two healthcare organizations you are comparing, including type and degree of diversity and inclusion, as well as organization type, size, location, and other distinguishing factors. Include supporting sources.
Analyze the culture of the two healthcare organizations and how each is influenced by diversity and inclusion.
Compare the cultures of the two healthcare organizations based on the role of diversity and inclusion in each, and strengths and weaknesses that relate to or derive from the degree of diversity and inclusion.
Summarize your conclusions on the impact of diversity and inclusion on organizational culture in healthcare settings based on your comparison.
Apply leadership strategies for a nurse executive to promote greater diversity, retain diverse staff members, and build cohesive teams and work groups.
.
हिंदी वर्णमाला पीपीटी, hindi alphabet PPT presentation, hindi varnamala PPT, Hindi Varnamala pdf, हिंदी स्वर, हिंदी व्यंजन, sikhiye hindi varnmala, dr. mulla adam ali, hindi language and literature, hindi alphabet with drawing, hindi alphabet pdf, hindi varnamala for childrens, hindi language, hindi varnamala practice for kids, https://www.drmullaadamali.com
The simplified electron and muon model, Oscillating Spacetime: The Foundation...RitikBhardwaj56
Discover the Simplified Electron and Muon Model: A New Wave-Based Approach to Understanding Particles delves into a groundbreaking theory that presents electrons and muons as rotating soliton waves within oscillating spacetime. Geared towards students, researchers, and science buffs, this book breaks down complex ideas into simple explanations. It covers topics such as electron waves, temporal dynamics, and the implications of this model on particle physics. With clear illustrations and easy-to-follow explanations, readers will gain a new outlook on the universe's fundamental nature.
Exploiting Artificial Intelligence for Empowering Researchers and Faculty, In...Dr. Vinod Kumar Kanvaria
Exploiting Artificial Intelligence for Empowering Researchers and Faculty,
International FDP on Fundamentals of Research in Social Sciences
at Integral University, Lucknow, 06.06.2024
By Dr. Vinod Kumar Kanvaria
This presentation includes basic of PCOS their pathology and treatment and also Ayurveda correlation of PCOS and Ayurvedic line of treatment mentioned in classics.
Walmart Business+ and Spark Good for Nonprofits.pdfTechSoup
"Learn about all the ways Walmart supports nonprofit organizations.
You will hear from Liz Willett, the Head of Nonprofits, and hear about what Walmart is doing to help nonprofits, including Walmart Business and Spark Good. Walmart Business+ is a new offer for nonprofits that offers discounts and also streamlines nonprofits order and expense tracking, saving time and money.
The webinar may also give some examples on how nonprofits can best leverage Walmart Business+.
The event will cover the following::
Walmart Business + (https://business.walmart.com/plus) is a new shopping experience for nonprofits, schools, and local business customers that connects an exclusive online shopping experience to stores. Benefits include free delivery and shipping, a 'Spend Analytics” feature, special discounts, deals and tax-exempt shopping.
Special TechSoup offer for a free 180 days membership, and up to $150 in discounts on eligible orders.
Spark Good (walmart.com/sparkgood) is a charitable platform that enables nonprofits to receive donations directly from customers and associates.
Answers about how you can do more with Walmart!"
A review of the growth of the Israel Genealogy Research Association Database Collection for the last 12 months. Our collection is now passed the 3 million mark and still growing. See which archives have contributed the most. See the different types of records we have, and which years have had records added. You can also see what we have for the future.
How to Build a Module in Odoo 17 Using the Scaffold MethodCeline George
Odoo provides an option for creating a module by using a single line command. By using this command the user can make a whole structure of a module. It is very easy for a beginner to make a module. There is no need to make each file manually. This slide will show how to create a module using the scaffold method.
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...PECB
Denis is a dynamic and results-driven Chief Information Officer (CIO) with a distinguished career spanning information systems analysis and technical project management. With a proven track record of spearheading the design and delivery of cutting-edge Information Management solutions, he has consistently elevated business operations, streamlined reporting functions, and maximized process efficiency.
Certified as an ISO/IEC 27001: Information Security Management Systems (ISMS) Lead Implementer, Data Protection Officer, and Cyber Risks Analyst, Denis brings a heightened focus on data security, privacy, and cyber resilience to every endeavor.
His expertise extends across a diverse spectrum of reporting, database, and web development applications, underpinned by an exceptional grasp of data storage and virtualization technologies. His proficiency in application testing, database administration, and data cleansing ensures seamless execution of complex projects.
What sets Denis apart is his comprehensive understanding of Business and Systems Analysis technologies, honed through involvement in all phases of the Software Development Lifecycle (SDLC). From meticulous requirements gathering to precise analysis, innovative design, rigorous development, thorough testing, and successful implementation, he has consistently delivered exceptional results.
Throughout his career, he has taken on multifaceted roles, from leading technical project management teams to owning solutions that drive operational excellence. His conscientious and proactive approach is unwavering, whether he is working independently or collaboratively within a team. His ability to connect with colleagues on a personal level underscores his commitment to fostering a harmonious and productive workplace environment.
Date: May 29, 2024
Tags: Information Security, ISO/IEC 27001, ISO/IEC 42001, Artificial Intelligence, GDPR
-------------------------------------------------------------------------------
Find out more about ISO training and certification services
Training: ISO/IEC 27001 Information Security Management System - EN | PECB
ISO/IEC 42001 Artificial Intelligence Management System - EN | PECB
General Data Protection Regulation (GDPR) - Training Courses - EN | PECB
Webinars: https://pecb.com/webinars
Article: https://pecb.com/article
-------------------------------------------------------------------------------
For more information about PECB:
Website: https://pecb.com/
LinkedIn: https://www.linkedin.com/company/pecb/
Facebook: https://www.facebook.com/PECBInternational/
Slideshare: http://www.slideshare.net/PECBCERTIFICATION
How to Make a Field Mandatory in Odoo 17Celine George
In Odoo, making a field required can be done through both Python code and XML views. When you set the required attribute to True in Python code, it makes the field required across all views where it's used. Conversely, when you set the required attribute in XML views, it makes the field required only in the context of that particular view.
2. Manufactured in the United States of America
10987654321
No part ofthis publication may be reproduced, stored in a
retrieval system or transmitted in any form or by any means,
electronic, mechanical, photocopying,
recording, scanning or otherwise, except as permitted under
Sections 107 or 108 of the 1976 United States Copyright Act,
without either the prior written permis-
sion of the Publisher, or authorization through payment of the
appropriate per-copy fee to the Copyright Clearance Center, 222
Rosewood Drive, Danvers, MA
01923, (978) 750-8400, fax (978) 646-8600. Requests to the
Publisher for permission should be addressed to the Permissions
Department, John Wiley & Sons, Inc.,
111 River Street, Hoboken, NJ 07030, (201) 748-6011, fax
(201) 748-6008, or online at http: I /www. wiley. com/
go/permissions.
limit ofliability/DisclaimerofWarranty: The publisher and the
author make no representations or warranties with respect to the
accuracy or completeness of
the contents of this work and specifically disclaim all
warranties, including without limitation warranties of fitness for
a particular purpose. No warranty may be
created or extended by sales or promotional materials. The
advice and strategies contained herein may not be suitable for
every situation. This work is sold with
the understanding that the publisher is not engaged in rendering
legal, accounting, or other professional services. If professional
assistance is required, the
services of a competent professional person should be sought.
Neither the publisher nor the author shall be liable for damages
arising herefrom. The fact that an
organization or Web site is referred to in this work as a citation
3. and/or a potential source of further information does not mean
that the author or the publisher
endorses the information the organization or website may
provide or recommendations it may make. Further, readers
should be aware that Internet websites
listed in this work may have changed or disappeared between
when this work was written and when it is read.
For general information on our other products and services
please contact our Customer Care Department within the United
States at (877) 762-2974, outside the
United States at (317) 572-3993 orfax (317) 572-4002.
Wiley publishes in a variety of print and electronic formats and
by print-on-demand. Some material included with standard print
versions of this book may not be
included in e-books or in print-on-demand.lf this book refers to
media such as a CD or DVD that is not included in the version
you purchased, you may download
this material at http: I /book support. wiley. com. For more
information about Wiley products, visit www. wiley. com.
library of Congress Control Number: 2014946681
Trademarks: Wiley and the Wiley logo are trademarks or
registered trademarks of John Wiley & Sons, Inc. and/or its
affiliates, in the United States and other coun-
tries, and may not be used without written permission. All other
trademarks are the property of their respective owners. John
Wiley & Sons, Inc. is not associated
with any product or vendor mentioned in this book.
Credits
4. Executive Editor
Carol Long
Project Editor
Kelly Talbot
Production Manager
Kathleen Wisor
Copy Editor
Karen Gill
Manager of Content Development
and Assembly
Mary Beth Wakefield
Marketing Director
David Mayhew
Marketing Manager
Carrie Sherrill
Professional Technology and Strategy Director
Ba rry Pruett
Business Manager
Amy Knies
Associate Publisher
Jim Minatel
5. Project Coordinator, Cover
Patrick Redmond
Proofreader
Nancy Carrasco
Indexer
Johnna Van Hoose Dinse
Cover Designer
Mallesh Gurram
About the Key Contributors
David Dietrich heads the data science education team within
EMC Education Services, where he leads the
curriculum, strategy and course development related to Big Data
Analytics and Data Science. He co-au-
thored the first course in EMC's Data Science curriculum, two
additional EMC courses focused on teaching
leaders and executives about Big Data and data science, and is a
contributing author and editor of this
book. He has filed 14 patents in the areas of data science, data
privacy, and cloud computing.
David has been an advisor to severa l universities looking to
develop academic programs related to data
analytics, and has been a frequent speaker at conferences and
industry events. He also has been a a guest lecturer at universi-
ties in the Boston area. His work has been featured in major
publications including Forbes, Harvard Business Review, and
6. the
2014 Massachusetts Big Data Report, commissioned by
Governor Deval Patrick.
Involved with analytics and technology for nearly 20 years,
David has worked with many Fortune 500 companies over his
career, holding mu lti ple roles involving analytics, including
managing ana lytics and operations teams, delivering analytic
con-
sulting engagements, managing a line of analytical software
products for regulating the US banking industry, and developing
Sohware-as-a-Service and BI-as-a-Service offerings.
Additionally, David collaborated with the U.S. Federal Reserve
in develop-
ing predictive models for monitoring mortgage portfolios.
Barry Heller is an advisory technical education consultant at
EMC Education Services. Barry is a course developer and cu r-
riculum advisor in the emerging technology areas of Big Data
and data science. Prior to his current role, Barry was a consul-
tant research scientist leadi ng numerous analytical initiatives
within EMC's Total Customer Experience
organization. Early in his EMC career, he managed the
statistical engineering group as well as led the
data warehousing efforts in an Enterprise Resource Planning
(ERP) implementation. Prior to joining EMC,
Barry held managerial and analytical roles in reliability
engineering functions at medical diagnostic and
technology companies. During his career, he has applied his
quantitative skill set to a myriad of business
applications in the Customer Service, Engineering, Ma
nufacturing, Sales/Marketing, Finance, and Legal
7. arenas. Underscoring the importance of strong executive
stakeholder engagement, many of his successes
have resulted from not only focusing on the technical details of
an analysis, but on the decisions that will be resulting from
the analysis. Barry earned a B.S. in Computational Mathematics
from the Rochester Institute ofTechnology and an M.A. in
Mathematics from the State University of New York (SUNY)
New Paltz.
Beibei Yang is a Technical Education Consultant of EMC
Education Services, responsible for developing severa l open
courses
at EMC related to Data Science and Big Data Analytics. Beibei
has seven years of experi ence in the IT industry. Prior to EMC
she
worked as a sohware engineer, systems manager, and network
manager for a Fortune 500 company where she introduced
new technologies to improve efficiency and encourage
collaboration. Beibei has published papers to
prestigious conferences and has filed multiple patents. She
received her Ph.D. in computer science from
the University of Massachusetts Lowell. She has a passion
toward natural language processing and data
mining, especially using various tools and techniques to find
hidden patterns and tell storie s with data.
Data Science and Big Data Analytics is an exciting domain
where the potential of digital information is
maximized for making intelligent business decisions. We
believe that this is an area that will attract a lot of
talented students and professiona ls in the short, mid, and long
8. term.
Acknowledgments
EMC Education Services embarked on learning this subject with
the intent to develop an "open" curriculum and
certification. It was a challenging journey at the time as not
many understood what it would take to be a true
data scientist. After initial research (and struggle), we were able
to define what was needed and attract very
talented professionals to work on the project. The course, "Data
Science and Big Data Analytics," has become
well accepted across academia and the industry.
Led by EMC Education Services, this book is the result of
efforts and contributions from a number of key EMC
organizations and supported by the office of the CTO, IT,
Global Services, and Engi neering. Many sincere
thanks to many key contributors and subject matter experts
David Dietrich, Barry Heller, and Beibei Yang
for their work developing content and graphics for the chapters.
A special thanks to subject matter experts
John Cardente and Ganesh Rajaratnam for their active
involvement reviewing multiple book chapters and
providing valuable feedback throughout the project.
We are also grateful to the fol lowing experts from EMC and
Pivotal for their support in reviewing and improving
the content in this book:
Aidan O'Brien Joe Kambourakis
9. Alexander Nunes Joe Milardo
Bryan Miletich John Sopka
Dan Baskette Kathryn Stiles
Daniel Mepham Ken Taylor
Dave Reiner Lanette Wells
Deborah Stokes Michael Hancock
Ellis Kriesberg Michael Vander Donk
Frank Coleman Narayana n Krishnakumar
Hisham Arafat Richard Moore
Ira Sch ild Ron Glick
Jack Harwood Stephen Maloney
Jim McGroddy Steve Todd
Jody Goncalves Suresh Thankappan
Joe Dery Tom McGowa n
We also thank Ira Schild and Shane Goodrich for coordinating
this project, Mallesh Gurram for the cover design, Chris Conroy
and Rob Bradley for graphics, and the publisher, John Wiley
and Sons, for timely support in bringing this book to the
10. industry.
Nancy Gessler
Director, Education Services, EMC Corporation
Alok Shrivastava
Sr. Direc tor, Education Services, EMC Corporation
Contents
Introduction ................ . .. . .....• . •.. ... .... •..... .. .. . .. .
.......... .. ... . ..................... •.•...... xvii
Chapter 1 • Introduction to Big Data Analytics ................... . . .
....................... 1
1.1 Big Data Overview ..................... ....... .....•... • ...... . . .
........ • .. ... . . ... ....... ....... 2
1.1.1 Data Structures .. . .. . . . .. ................ ... ... . .. . ...... . ..
.. .... . .................... ..... . .. . . . .. 5
1.1.2 Analyst Perspective on Data Repositories .
............................. . .......... .......•. ... ... .. .. 9
1.2 State of the Practice in Analytics
................................................................. . 11
1.2.1 Bl Versus Data Science .............. .... ....... . .. . ........... . .
. .... . ....................... .. .... 12
1.2.2 Current Analytical Architecture ... . .... .• . . ................
.... .............. .... .... ...... •.. . ..... 13
1.2.3 Drivers of Big Data .................................................... . .
. .. ................. .. ... . . 15
1.2.4 Emerging Big Data Ecosystem and a New Approach to
Analytics .. ....... ...... . ............ .. ....... 16
24. Technological advances and the associated changes in practical
daily life have produced a rapidly expanding
"parallel universe" of new content, new data, and new
information sources all around us. Regardless of how one
defines it, the phenomenon of Big Data is ever more present,
ever more pervasive, and ever more important. There
is enormous value potential in Big Data: innovative insights,
improved understanding of problems, and countless
opportunities to predict-and even to shape-the future. Data
Science is the principal means to discover and
tap that potential. Data Science provides ways to deal with and
benefit from Big Data: to see patterns, to discover
relationships, and to make sense of stunningly varied images
and information.
Not everyone has studied statistical analysis at a deep level.
People with advanced degrees in applied math-
ematics are not a commodity. Relatively few organizations have
committed resources to large collections of data
gathered primarily for the purpose of exploratory analysis. And
yet, while applying the practices of Data Science
to Big Data is a valuable differentiating strategy at present, it
will be a standard core competency in the not so
distant future.
How does an organization operationalize quickly to take
advantage of this trend? We've created this book for
that exact purpose.
EMC Education Services has been listening to the industry and
organizations, observing the multi-faceted
transformation of the technology landscape, and doing direct
research in order to create curriculum and con-
tent to help individuals and organizations transform themselves.
For the domain of Data Science and Big Data
25. Analytics, our educational strategy balances three things:
people-especially in the context of data science teams,
processes-such as the analytic lifecycle approach presented in
this book, and tools and technologies-in this case
with the emphasis on proven analytic tools.
So let us help you capitalize on this new "parallel universe" that
surrounds us. We invite you to learn about
Data Science and Big Data Analytics through this book and
hope it significantly accelerates your efforts in the
transformational process.
Introduction
Big Data is creating significant new opportunities for
organizations to derive new value and create competitive
advantage from their most valuable asset: information. For
businesses, Big Data helps drive efficiency, quality, and
personalized products and services, producing improved levels
of customer satisfaction and profit. For scientific
efforts, Big Data analytics enable new avenues of investigation
with potentially richer results and deeper insights
than previously available. In many cases, Big Data analytics
integrate structured and unstructured data with real-
time feeds and queries, opening new paths to innovation and
insight.
This book provides a practitioner's approach to some of the key
techniques and tools used in Big Data analytics.
Knowledge ofthese methods will help people become active
contributors to Big Data analytics projects. The book's
content is designed to assist multiple stakeholders: business and
data analysts looking to add Big Data analytics
skills to their portfolio; database professionals and managers of
26. business intelligence, analytics, or Big Data groups
looking to enrich their analytic skills; and college graduates
investigating data science as a career field.
The content is structured in twelve chapters. The first chapter
introduces the reader to the domain of Big Data,
the drivers for advanced analytics, and the role of the data
scientist. The second chapter presents an analytic project
lifecycle designed for the particular characteristics and
challenges of hypothesis-driven analysis with Big Data.
Chapter 3 examines fundamental statistical techniques in the
context of the open source R analytic software
environment. This chapter also highlights the importance of
exploratory data analysis via visualizations and reviews
the key notions of hypothesis development and testing.
Chapters 4 through 9 discuss a range of advanced analytical
methods, including clustering, classification,
regression analysis, time series and text analysis.
Chapters 10 and 11 focus on specific technologies and tools that
support advanced analytics with Big Data. In
particular, the Map Reduce paradigm and its instantiation in the
Hadoop ecosystem, as well as advanced topics
in SOL and in-database text analytics form the focus of these
chapters.
XVIII ! INTRODUCTION
Chapter 12 provides guidance on operationalizing Big Data
analytics projects. This chapter focuses on creat·
ing the final deliverables, converting an analytics project to an
ongoing asset of an organization's operation, and
27. creating clear, useful visual outputs based on the data.
EMC Academic Alliance
University and college faculties are invited to join t he
Academic Alliance program to access unique "ope n"
curriculum-based education on the following top ics:
• Data Science and Big Data Analytics
• Information Storage and Management
• Cloud Infrastructure and Services
• Backup Recovery Systems and Architecture
The program provides faculty with course re sources to prepare
students for opportunities that exist in today's
evolving IT industry at no cost. For more information, visit
http: // education . EMC . com/ academicalliance.
EMC Proven Professional Certification
EMC Proven Professional is a leading education and
certification program in the IT industry, providing compre-
hensive coverage of information storage technologies,
virtualization, cloud computing, data science/ Big Data
analytics, and more.
Being proven means investing in yourself and formally
validating your expertise.
This book prepares you for Data Science Associate (EMCDSA)
certification. Visit http : I I educat i on . EMC
. com for details.
28. INTRODUCTION TO BIG DATA ANAL YTICS
Much has been written about Big Data and the need for
advanced analytics within industry, academ ia,
and government. Availa bility of new data sources and the rise
of more complex analytical opportunities
have created a need to rethink existing data architectures to
enable analytics that take advantage of Big
Data. In addition, sig nificant debate exists about what Big Data
is and what kinds of skil ls are required to
make best use of it. This chapter explains severa l key concepts
to clarify what is meant by Big Data, why
adva nced analyt ics are needed, how Data Science differs from
Business Intelligence (BI), and what new
roles are needed for the new Big Data ecosystem.
1.1 Big Data Overview
Data is created constantly, and at an ever-increasing rate.
Mobile phones, social media, imaging technologies
to determine a medical diagnosis-all these and more create new
data, and that must be stored somewhere
for some purp ose. Devices and sensors automatically generate
diagnostic information that needs to be
stored and processed in real time. Merely keeping up with this
huge influx of data is difficult, but su bstan-
tially more cha llenging is analyzing vast amounts of it,
29. especially when it does not conform to traditional
notions of data structure, to identify meaningful patterns and
extract useful information. These challenges
of the data deluge present the opportunity to transform business,
government, science, and everyday life.
Several industries have led the way in developing their ability
to gather and exploit data:
• Credit ca rd companies monitor every purchase their
customers make and can identify fraudulent
purchases with a high degree of accuracy using rules derived by
processing billions of transactions.
• Mobi le phone companies analyze subscribers' calling patterns
to determine, for example, whether a
caller's frequent contacts are on a rival network. If that rival
network is offeri ng an attractive promo-
tion t hat might cause the subscriber to defect, the mobile phone
company can proactively offer the
subscriber an incentive to remai n in her contract.
• For compan ies such as Linked In and Facebook, data itself is
their primary product. The valuations of
these compan ies are heavi ly derived from the data they gather
and host, which contains more and
more intrinsic va lue as the data grows.
Three attributes stand out as defining Big Data characteristics:
• Huge volume of data: Rather than thousands or millions of
rows, Big Data can be billions of rows and
millions of columns.
• Complexity of data t ypes and st ructures: Big Data reflects
30. the variety of new data sources, forma ts,
and structures, including digital traces being left on the web and
other digital repositories for subse-
quent analysis.
• Speed of new dat a crea tion and growt h: Big Data can
describe high velocity data, with rapid data
ingestion and near real time analysis.
Although the vol ume of Big Data tends to attract the most
attention, genera lly the variety and veloc-
ity of the data provide a more apt defi nition of Big Data. (Big
Data is sometimes described as havi ng 3 Vs:
volu me, vari ety, and velocity.) Due to its size or structure, Big
Data cannot be efficiently analyzed using on ly
traditional databases or methods. Big Data problems req uire
new tools and tech nologies to store, manage,
and realize the business benefit. These new tools and
technologies enable creation, manipulation, and
1.1 Big Data Overview
management of large datasets and t he storage environments that
house them. Another definition of Big
Data comes from the McKi nsey Global report from 2011:
Big Data is data whose s cale, dis tribution, diversity, and/ or
timeliness require th e
use of new technical architectures and analytics to e nable
insights that unlock ne w
sources of business value.
31. McKinsey & Co.; Big Data: The Next Frontier for Innovation,
Competit ion, and
Prod uctivity [1]
McKinsey's definition of Big Data impl ies that orga nizations
will need new data architectures and ana-
lytic sandboxes, new tools, new analytical methods, and an
integration of multiple skills into the new ro le
of the data scientist, which will be discussed in Section 1.3.
Figure 1-1 highlights several sources of the Big
Data deluge.
What's Driving Data Deluge?
Mobile
Sensors
Smart
Grids
Social
Media
Geophysical
Exploration
FtGURE 1-1 What 's driving the da ta deluge
Video
Surveillance
• Medical Imaging
Video
32. Rendering
Gene
Seque ncing
The rate of data creation is accelerating, driven by many of the
items in Figure 1-1.
Social media and genetic sequencing are among the fastest-
growing sources of Big Data and examples
of untraditional sources of data being used for analysis.
For example, in 2012 Facebook users posted 700 status updates
per second worldwide, which can be
leveraged to deduce latent interests or political views of users
and show relevant ads. For instance, an
update in wh ich a woman changes her relationship status from
"single" to "engaged" wou ld t rigger ads
on bri dal dresses, wedding plann ing, or name-changing
services.
Facebook can also construct social graphs to ana lyze which
users are connected to each other as an
interconnected network. In March 2013, Facebook released a
new featu re called "Graph Search," enabling
users and developers to search social graphs for people with
similar interests, hobbies, and shared locations.
INTRODUCTION TO BIG DATA ANALYTICS
Another example comes from genomics. Genetic sequencing and
human genome mapping provide a
detailed understanding of genetic makeup and lineage. The
33. health care industry is looking toward these
advances to help predict which illnesses a person is li kely to
get in his lifetime and take steps to avoid these
maladies or reduce their impact through the use of personalized
med icine and treatment. Such tests also
highlight typical responses to different medications and
pharmaceutical drugs, heightening risk awareness
of specific drug treatments.
While data has grown, the cost to perform this work has fall en
dramatically. The cost to sequence one
huma n genome has fallen from $100 million in 2001 to $10,000
in 2011, and the cost continues to drop. Now,
websites such as 23andme (Figure 1-2) offer genotyp ing for
less than $100. Although genotyping analyzes
on ly a fraction of a genome and does not provide as much
granularity as genetic sequencing, it does point
to the fact that data and complex analysis is becoming more
prevalent and less expensive to deploy.
23 pairs of
chromosomes.
One unique you.
Bring your ancestry to life.
F1ncl out what percent or your DNA comes !rom
populations around the world. rang1ng from East As1a
Sub-Saharan Alllca Europe, and more. B1eak
European ancestry down 1010 d1st1nct regions such as
the Bnush Isles. Scnnd1navla Italy and Ashkenazi
Jewish. People IVIh mixed ancestry. Alncan
Amencans. Launos. and Nauve Amencans w111 also
34. get a detailed breakdown.
20.5%
( .t A! n
Find relatives across
continents or across
the street.
Build your family tree
and enhance your
ex erience.
: 38.6%
· s, b·S 1h Jn Afr c.an
24.7%
Europe.,,
•
' Share your knowledge. Watch it
row.
FIGURE 1-2 Examples of what can be learned through
genotyping, from 23andme.com
1.1 Big Dat a Overview
As illustrated by the examples of social media and genetic
sequencing, ind ividuals and organizations
both derive benefits from analysis of ever-larger and more comp
lex data sets that require increasingly
powerful analytical capabilities.
35. 1.1.1 Data Structures
Big data can come in multiple forms, including structured and
non -structured data such as financial
data, text files, multimedia files, and genetic mappings.
Contrary to much of the traditional data ana lysis
performed by organizations, most of the Big Data is
unstructured or semi-structured in nature, which
requires different techniques and tools to process and analyze.
[2) Distributed computing environments
and massively parallel processing (MPP) architectures that
enable parallelized data ingest and analysis are
the preferred approach to process such complex data.
With this in mind, this section takes a closer look at data
structures.
Figure 1-3 shows four types of data structures, with 80-90% of
future data growth coming from non-
structured data types. [2) Though different, the four are
commonly mixed. For example, a classic Relational
Database Management System (RDBMS) may store call logs for
a software support call center. The RDBMS
may store characteristics of the support calls as typical
structured data, with attributes such as time stamps,
machine type, problem type, and operating system. In addition,
the system will likely have unstructured,
quasi- or semi-structured data, such as free-form call log
information taken from an e-mail ticket of the
problem, customer chat history, or transcript of a phone call
describing the technical problem and the solu-
tion or aud io file of the phone call conversation. Many insights
36. could be extracted from the unstructured,
quasi- or semi-structu red data in the call center data.
'0
Q)
E
u
2
iii
Q)
0
~
Big Data Characteristics: Data Structures
Data Growth Is Increasingly Unstructured
I
Structured
FIGURE 1-3 Big Data Growth is increasingly unstructured
INTRODUCTION TO BIG DATA ANALYTICS
Although analyzing structured data tends to be the most familiar
technique, a different technique is
required to meet the challenges to analyze semi-structured data
(shown as XML), quasi-structured (shown
as a clickstream), and unstructured data.
Here are examples of how each of the four main types of data
structures may look.
37. o Structured data: Data containing a defined data type, format,
and structure (that is, transaction data,
online analytical processing [OLAP] data cubes, traditional
RDBMS, CSV files, and even simple spread-
sheets). See Figure 1-4.
SUMMER FOOD SERVICE PROGRAM 11
Data as of August 01. 2011)
Fiscal Number of Peak (July) Meals Total Federal
Year Sites Participation Served Expenditures 2]
---Thousands-- -MiL- -Million$-
1969 1.2 99 2.2 0.3
1970 1.9 227 8.2 1.8
1971 3.2 569 29.0 8.2
1972 6.5 1,080 73.5 21.9
1973 11.2 1,437 65.4 26.6
1974 10.6 1,403 63.6 33.6
1975 12.0 1,785 84.3 50.3
1976 16.0 2,453 104.8 73.4
TQ3] 22.4 3,455 198.0 88.9
1977 23.7 2,791 170.4 114.4
1978 22.4 2,333 120.3 100.3
1979 23.0 2,126 121.8 108.6
1980 21.6 1,922 108.2 110.1
1981 20.6 1,726 90.3 105.9
1982 14.4 1,397 68.2 87.1
1983 14.9 1,401 71.3 93.4
1984 15.1 1,422 73.8 96.2
1985 16.0 1,462 77.2 111.5
1986 16.1 1,509 77.1 114.7
1987 16.9 1,560 79.9 129.3
1988 17.2 1,577 80.3 133.3
38. 1989 18.5 1.652 86.0 143.8
1990 19? 1 ~Q? 91? 1~11
FIGURE 1-4 Example of structured data
o Semi-structured data: Textual data files with a discernible
pattern that enables parsing (such
as Extensible Markup Language [XML] data files that are self-
describing and defined by an XML
schema). See Figure 1-5.
o Quasi-structured data: Textual data with erratic data formats
that can be formatted with effort,
tools, and time (for instance, web clickstream data that may
contain inconsistencies in data values
and formats). See Figure 1-6.
o Unstructured data: Data that has no inherent structure, which
may include text documents, PDFs,
images, and video. See Figure 1-7.
1.1 Big Data Ove rvi ew
Quasi-structured data is a common phenomenon that bears
closer scrutiny. Consider the following
example. A user attend s the EMC World conference and
subsequently runs a Google search online to find
information related to EMC and Data Scien ce. This would
produce a URL such as https: I /www . googl e
. c om/ #q=EMC+ data +scienc e and a list of results, such as in
the first graphic of Figure 1-5.
- ~ ....- . .
39. •• 0
o:.~t.a c!':a=-set.•"~t.t-e">
<z:.~ca l':cc.p-eq-.:.:.v•"X-:J;.-cc:r.;:a c.:.t:~" cc::te::c.•"
:.::·~d.Q"e , c~.=cr:."!•: ">
<t.:.e:"!>~~C - :ead.:. ~o Clc~d Co~~e.:.~~, 3~Q' Dace., a ::d
T:~sced ! ! Sol~t.:.o~s</t.:.t!e>
clc::d cc::,r·..:e.:.::r; . ">
<l.:.::k =e:•"se;·:es!':eee" 1':=-et•" / R. /a;;e;;t c;s / ccv.rrp""' /
jo;n:e· ~ ze: c':." >
<l.:.::k =~:•"St.i':es!:eet." !-:.=-et•" / B1/a.s:t::;s t c,;u /
1ooorrapo g c / rra ·-. . C!!!! '" >
<l.:. ::Jc :-el""" !!t.)-'les!'l.eec " l':=e~•" / 5~ /a.:.;ets / c .,, /
corr:rtgjJ/ .. c!lcO""'. ve:-,.cade:- c:~s">
<l.:.::.k =e:• " st.:,·:esl':ee:t. " !':.:et• " 15· / a;;ee, t
c:z:Jisgrur:c ... / -e:;n;o;gs· ve:-tco;c• c='a ">
<~c::.;::t. :.:,1=e•" t.ex::. / : ·e:;asc::.pt. " s:-c• '" // c l a; t o ;n:
P' ' p;•"" ccrrt-.~ .. dce:t:t.- ; - ><I sc:l.p:t.>
< :~ c:.:.;::t. .!l:c •"' / R. /a:.sec:J(<~.;/ cgrr;;:c""/rred•--.1z ..
_2 I 6 I 2 .;;,;. "'j;. ~ 3 "' ></ ~c:.:.pt.>
FIGURE 1-5 Example of semi-structured data
Tool!un
QUKkt~b~
b:plorerbars
Go to
Stop
41. Scientist- EM( Educa tion, Training, and Certification." This
brings the user to an erne . com site focu sed on
this topic and a new URL, h t t p s : I / e d ucation . e rne . com/
guest / campa i gn / data_ science
INTRODUCTION TO BIG DATA ANALYTICS
1
. aspx, that displays the page shown as (2) in Figure 1-6. Arrivi
ng at this site, the user may decide to click
to learn more about the process of becoming certified in data
science. The user chooses a link to ward the
top of the page on Certifications, bringing the user to a new
URL: ht tps : I I education. erne. com/
guest / certifica tion / framewo rk / stf / data_science . aspx,
which is (3) in Figure 1-6.
Visiting these three websites adds three URLs to the log files
monitoring the user's computer or network
use. These three URLs are:
https: // www.google . com/# q=EMC+data+ s cience
https: // education . emc.com/ guest / campaign/ data science .
aspx
https : // education . emc . com/ guest / certification/ framework
/ stf / data_
science . aspx
- - ...... - .._.. ............. _
O.Uk*-andi'IO..~T~ · OIC~ o
---·- t..._ ·-- . -- ·-A-- ------·----- .. -,.. _ , _____ ....
43. 1.1 Big Data Overview
FIGURE 1-7 Example of unstructured data: video about
Antarctica expedition [3]
This set of three URLs reflects the websites and actions taken to
find Data Science inform ation related
to EMC. Together, this comprises a clicksrream that can be
parsed and mined by data scientists to discover
usage patterns and uncover relation ships among clicks and
areas of interest on a website or group of sites.
The four data types described in this chapter are sometimes
generalized into two groups: structured
and unstructu red data. Big Data describes new kinds of data
with which most organizations may not be
used to working. With this in mind, the next section discusses
common technology arch itectures from the
standpoint of someone wanting to analyze Big Data.
1.1.2 Analyst Perspective on Data Repositories
The introduction of spreadsheets enabled business users to crea
te simple logic on data structured in rows
and columns and create their own analyses of business
problems. Database administrator training is not
requ ired to create spreadsheets: They can be set up to do many
things qu ickly and independently of
information technology (IT) groups. Spreadsheets are easy to
share, and end users have control over the
logic involved. However, their proliferation can result in "many
44. versions of the t ruth." In other words, it
can be challenging to determine if a particular user has the most
relevant version of a spreadsheet, with
the most current data and logic in it. Moreover, if a laptop is
lost or a file becomes corrupted, the data and
logic within the spreadsheet could be lost. This is an ongoing
challenge because spreadsheet programs
such as Microsoft Excel still run on many computers worldwide.
With the proliferation of data islands (or
spread marts), the need to centralize the data is more pressing
than ever.
As data needs grew, so did mo re scalable data warehousing
solutions. These technologies enabled
data to be managed centrally, providing benefits of security,
failover, and a single repository where users
INTRODUCTION TO BIG DATA ANALYTICS
could rely on getting an "official" source of data for finan cial
reporting or other mission-critical tasks. This
structure also enabled the creation ofOLAP cubes and 81
analytical tools, which provided quick access to a
set of dimensions within an RD8MS. More advanced features
enabled performance of in-depth analytical
techniques such as regressions and neural networks. Enterprise
Data Warehouses (EDWs) are critica l for
reporting and 81 tasks and solve many of the problems that
proliferating spreadsheets introduce, such as
which of multiple versions of a spreadsheet is correct. EDWs-
45. and a good 81 strategy-provide direct data
feeds from sources that are centrally managed, backed up, and
secured.
Despite the benefits of EDWs and 81, these systems tend to
restri ct the flexibility needed to perform
robust or exploratory data analysis. With the EDW model, data
is managed and controlled by IT groups
and database administrators (D8As), and data analysts must
depend on IT for access and changes to the
data schemas. This imposes longer lead ti mes for analysts to
get data; most of the time is spent waiting for
approvals rather than starting meaningful work. Additionally,
many times the EDW rul es restrict analysts
from building datasets. Consequently, it is com mon for
additional systems to emerge containing critical
data for constructing analytic data sets, managed locally by
power users. IT groups generally dislike exis-
tence of data sources outside of their control because, unlike an
EDW, these data sets are not managed,
secured, or backed up. From an analyst perspective, EDW and
81 solve problems related to data accuracy
and availabi lity. However, EDW and 81 introduce new
problems related to flexibility and agil ity, which were
less pronounced when dealing with spreads heets.
A solution to this problem is the analytic sandbox, which
attempts to resolve the conflict for analysts and
data scientists with EDW and more formally managed corporate
data. In this model, the IT group may still
46. manage the analytic sandboxes, but they will be purposefully
designed to enable robust analytics, while
being centrally managed and secured. These sandboxes, often
referred to as workspaces, are designed to
enable teams to explore many datasets in a controlled fashion
and are not typically used for enterprise-
level financial reporting and sales dashboards.
Many times, analytic sa ndboxes enable high-performance
computing using in-database processing-
the analytics occur within the database itself. The idea is that
performance of the analysis will be better if
the analytics are run in the database itself, rather than bringing
the data to an analytical tool that resides
somewhere else. In-database analytics, discussed further in
Chapter 11, "Advanced Analytics- Technology
and Tools: In-Database Analytics." creates relationships to
multiple data sources within an organization and
saves time spent creating these data feeds on an individual
basis. In-database processing for deep analytics
enables faster turnaround time for developing and executing
new analytic models, while reducing, though
not eli minating, the cost associated with data stored in local,
"shadow" file systems. In addition, rather
than the typical structured data in the EDW, analytic sandboxes
ca n house a greater variety of data, such
as raw data, textual data, and other kinds of unstructured data,
without interfering with critical production
databases. Table 1-1 summarizes the characteristics of the data
repositories mentioned in this section.
47. TABLE 1-1 Types of Data Repositories, from an Analyst
Perspective
Data Repository Characteristics
Spreadsheets and
data marts
("spreadmarts")
Spreadsheets and low-volume databases for record keeping
Analyst depends on data extracts.
Data Warehouses
Analytic Sandbox
(works paces)
1.2 State of the Practice in Analytics
Centralized data containers in a purpose-built space
Suppo rt s Bl and reporting, but restri cts robust analyses
Ana lyst d ependent o n IT and DBAs for data access and
schema changes
Ana lysts must spend significant t ime to g et aggregat ed and d
isaggre-
gated data extracts f rom multiple sources.
48. Data assets gathered f rom multiple sources and technologies fo
r ana lysis
Enables fl exible, high-performance ana lysis in a
nonproduction environ-
ment; can leverage in-d atabase processing
Reduces costs and risks associated w ith data replication into
"shadow" file
systems
"Analyst owned" rather t han "DBA owned"
There are several things to consider with Big Data Analytics
projects to ensure the approach fits w ith
the desired goals. Due to the characteristics of Big Data, these
projects le nd them selves to decision su p-
port for high-value, strategic decision making w ith high
processing complexi t y. The analytic techniques
used in this context need to be iterative and fl exible, due to the
high volume of data and its complexity.
Performing rapid and complex analysis requires high throughput
network con nections and a consideration
for the acceptable amount of late ncy. For instance, developing
a real- t ime product recommender for a
website imposes greater syst em demands than developing a
near· real·time recommender, which may
still pro vide acceptable p erform ance, have sl ight ly greater
49. latency, and may be cheaper to deploy. These
considerations requi re a different approach to thinking about
analytics challenges, which will be explored
further in the next section.
1.2 State of the Practice in Analytics
Current business problems provide many opportunities for
organizations to become more analytical and
data dri ven, as shown in Table 1 ·2.
TABLE 1-2 Business Drivers for Advanced Analytics
Business Driver Examples
Optimize business operations
Identify business ri sk
Predict new business opportunities
Comply w ith laws or regu latory
requirements
Sales, pricing, profitability, efficiency
Customer churn, fraud, default
Upsell, cross-sell, best new customer prospects
Anti-Money Laundering, Fa ir Lending, Basel II-III, Sarbanes-
Oxley(SOX)
50. INTRODUCTION TO BIG DATA ANALYTICS
Table 1-2 outlines four categories of common business problems
that organizations contend with where
they have an opportunity to leverage advanced analytics to
create competitive advantage. Rather than only
performing standard reporting on these areas, organizations can
apply advanced analytical techniques
to optimize processes and derive more value from these common
tasks. The first three examples do not
represent new problems. Organizations have been trying to
reduce customer churn, increase sales, and
cross-sell customers for many years. What is new is the
opportunity to fuse advanced analytical techniques
with Big Data to produce more impactful analyses for these
traditional problems. The last example por-
trays emerging regulatory requirements. Many compliance and
regulatory laws have been in existence for
decades, but additional requirements are added every year,
which represent additional complexity and
data requirements for organizations. Laws related to anti-money
laundering (AML) and fraud prevention
require advanced analytical techniques to comply with and
manage properly.
1.2.1 81 Versus Data Science
The four business drivers shown in Table 1-2 require a variety
of analytical techniques to address them prop-
erly. Although much is written generally about analytics, it is
important to distinguish between Bland Data
Science. As shown in Figure 1-8, there are several ways to
compare these groups of analytical techniques.
One way to evaluate the type of analysis being performed is to
51. examine the time horizon and the kind
of analytical approaches being used. Bl tends to provide reports,
dashboards, and queries on business
questions for the current period or in the past. Bl systems make
it easy to answer questions related to
quarter-to-date revenue, progress toward quarterly targets, and
understand how much of a given product
was sold in a prior quarter or year. These questions tend to be
closed-ended and explain current or past
behavior, typically by aggregating historical data and grouping
it in some way. 81 provides hindsight and
some insight and generally answers questions related to "when"
and "where" events occurred.
By comparison, Data Science tends to use disaggregated data in
a more forward-looking, exploratory
way, focusing on analyzing the present and enabling informed
decisions about the future. Rather than
aggregating historical data to look at how many of a given
product sold in the previous quarter, a team
may employ Data Science techniques such as time series
analysis, further discussed in Chapter 8, "Advanced
Analytical Theory and Methods: Time Series Analysis," to
forecast future product sales and revenue more
accurately than extending a simple trend line. In addition, Data
Science tends to be more exploratory in
nature and may use scenario optimization to deal with more
open-ended questions. This approach provides
insight into current activity and foresight into future events,
while generally focusing on questions related
to "how" and "why" events occur.
Where 81 problems tend to require highly structured data
organized in rows and columns for accurate
reporting, Data Science projects tend to use many types of data
sources, including large or unconventional
52. datasets. Depending on an organization's goals, it may choose to
embark on a 81 project if it is doing reporting,
creating dashboards, or performing simple visualizations, or it
may choose Data Science projects if it needs
to do a more sophisticated analysis with disaggregated or varied
datasets.
Exploratory
Analytical
Approach
Explanatory
I
, .. -- ---,
1 Busin ess 1
1 Inte lligence 1
, .... _____ ..,
Past
fiGUR E 1 ·8 Comparing 81 with Data Science
1.2.2 Current Analytical Architecture
1 .2 State ofthe Practice In Analytlcs
Predictive Analytics and Data Mini ng
(Data Sci ence)
Typical • Optimization. predictive modo lin£
Techniques forocastlnC. statlatlcal analysis
53. and • Structured/unstructured data. many
Data Types types of sources, very Ioree datasata
Common
Questions
Typical
Techniques
and
Data Types
Tim e
Common
Questions
• What II ... ?
• What's tho optlmaltconarlo tor our bualnoss?
• What wtll happen next? What II these trend$
continuo? Why Is this happonlnt?
Busi ness Intelligence
• Standard and ad hoc reportlnc. dashboards.
alerts, queries, details on demand
• Structured data. traditional sourcoa.
manac:eable datasets
• What happened lut quarter?
• How many units sold?
54. • Whore Is the problem? In whic h situations?
Future
As described earlier, Data Science projects need workspaces
that are purpose-built for experimenting with
data, with flexible and agile data architectures. Most
organizations still have data warehouses that provide
excellent support for traditional reporting and simple data
analysis activities but unfortunately have a more
difficult time supporting more robust analyses. This section
examines a typical analytical data architecture
that may exist within an organization.
Figure 1-9 shows a typical data architecture and several of the
challenges it presents to data scientists
and others trying to do advanced analytics. This section
examines the data flow to the Data Scientist and
how this individual tits into the process of getting data to
analyze on proj ects.
INTRODUCTION TO BIG DATA ANALYTICS
FIGURE 1-9 Typical analytic architecture
i..l ,_,
It
An alysts
55. Dashboards
Reports
Al erts
1. For data sources to be loaded into the data wa rehouse, data
needs to be well understood,
structured, and normalized with the appropriate data type defini
t ions. Although th is kind of
centralization enabl es security, backup, and fai lover of highly
critical data, it also means that data
typically must go through significant preprocessing and
checkpoints before it can enter this sort
of controll ed environment, which does not lend itself to data
exploration and iterative analytic s.
2. As a result of t his level of control on the EDW, add itional
local systems may emerge in the form of
departmental wa rehou ses and loca l data marts t hat business
users create to accommodate thei r
need for flexible analysis. These local data marts may not have
the same constraints for secu-
ri ty and structu re as the main EDW and allow users to do some
level of more in-depth analysis.
However, these one-off systems reside in isolation, often are not
synchronized or integrated with
other data stores, and may not be backed up.
3. Once in the data warehouse, data is read by additional
applications across the enterprise for Bl
and reporting purposes. These are high-priority operational
processes getting critical data feeds
from the data warehouses and repositories.
56. 4. At the end of this workfl ow, analysts get data provisioned
for their downstream ana lytics.
Because users generally are not allowed to run custom or
intensive analytics on production
databases, analysts create data extracts from the EDW to
analyze data offline in R or other local
analytical tools. Many times the se tools are lim ited to in-
memory analytics on desktops analyz-
ing sa mples of data, rath er than the entire population of a
dataset. Because the se analyses are
based on data extracts, they reside in a separate location, and
the results of the analysis-and
any insights on the quality of the data or anomalies- rarely are
fed back into the main data
repository.
Because new data sources slowly accum ulate in the EDW due
to the rigorous validation and
data struct uring process, data is slow to move into the EDW,
and the data schema is slow to change.
1.2 State of the Practice in Analytics
Departmental data warehouses may have been originally
designed for a specific purpose and set of business
needs, but over time evolved to house more and more data,
some of which may be forced into existing
schemas to enable Bland the creation of OLAP cubes for
analysis and reporting. Although the EDW achieves
the objective of reporting and sometimes the creation of
dashboards, EDWs generally limit the ability of
analysts to iterate on the data in a separate nonproduction
environment where they can conduct in-depth
57. analytics or perform analysis on unstructured data.
The typical data architectures just described are designed for
storing and processing mission-critical
data, supporting enterprise applications, and enabling corporate
reporting activities. Although reports and
dashboards are still important for organizations, most
traditional data architectures inhibit data exploration
and more sophisticated analysis. Moreover, traditional data
architectures have several additional implica-
tions for data scientists.
o High-value data is hard to reach and leverage, and predictive
analytics and data mining activities
are last in line for data. Because the EDWs are designed for
central data management and reporting,
those wanting data for analysis are generally prioritized after
operational processes.
o Data moves in batches from EDW to local analytical tools.
This workflow means that data scientists
are limited to performing in-memory analytics (such as with R,
SAS, SPSS, or Excel), which will restrict
the size of the data sets they can use. As such, analysis may be
subject to constraints of sampling,
which can skew model accuracy.
o Data Science projects will remain isolated and ad hoc, rather
than centrally managed. The implica-
tion of this isolation is that the organization can never harness
the power of advanced analytics in a
scalable way, and Data Science projects will exist as
nonstandard initiatives, which are frequently not
aligned with corporate business goals or strategy.
All these symptoms of the traditional data architecture result in
58. a slow "time-to-insight" and lower
business impact than could be achieved if the data were more
readily accessible and supported by an envi-
ronment that promoted advanced analytics. As stated earlier,
one solution to this problem is to introduce
analytic sandboxes to enable data scientists to perform
advanced analytics in a controlled and sanctioned
way. Meanwhile, the current Data Warehousing solutions
continue offering reporting and Bl services to
support management and mission-critical operations.
1.2.3 Drivers of Big Data
To better understand the market drivers related to Big Data, it is
helpful to first understand some past
history of data stores and the kinds of repositories and tools to
manage these data stores.
As shown in Figure 1-10, in the 1990s the volume of
information was often measured in terabytes.
Most organizations analyzed structured data in rows and
columns and used relational databases and data
warehouses to manage large stores of enterprise information.
The following decade saw a proliferation of
different kinds of data sources-mainly productivity and
publishing tools such as content management
repositories and networked attached storage systems-to manage
this kind of information, and the data
began to increase in size and started to be measured at petabyte
scales. In the 2010s, the information that
organizations try to manage has broadened to include many
other kinds of data. In this era, everyone
and everything is leaving a digital footprint. Figure 1-10 shows
a summary perspective on sources of Big
Data generated by new applications and the scale and growth
rate of the data. These applications, which
generate data volumes that can be measured in exabyte scale,
59. provide opportunities for new analytics and
driving new value for organizations. The data now comes from
multiple sources, such as these:
INTRODUCTION TO BIG DATA ANALYTICS
• Medical information, such as genomic sequencing and diag
nostic imagi ng
• Photos and video footage uploaded to the World Wide Web
• Video surveillance, such as the thousands of video ca meras
spread across a city
• Mobile devices, which provide geospatiallocation data of the
users, as well as metadata about text
messages, phone calls, and application usage on smart phones
• Smart devices, which provide sensor-based collection of
information from smart electric grids, smart
bu ildings, and many other public and ind ustry infrastructures
• Nontraditional IT devices, including the use of radio-freq
uency identifica tion (RFID) reader s, GPS
navigation systems, and seismic processing
MEASURED IN MEASURED IN WILL BE MEASURED IN
TERABYTES PET A BYTES EXABYTES
lTB • 1.000GB lPB • l .OOOTB lEB l .OOOPB
IIEII
You(D
60. .... ~ .. ·,
A n '' . ~
I b ~
~
~
SMS
w: '-----"
ORACLE =
1.9905 20005 201.05
( RDBMS & DATA (CONTENT & DIGITAL ASSET (NO-SQL
& KEY VALUE)
WAREHOUSE) MANAGEMENT)
FIGURE 1-10 Data evolution and the rise of Big Data sources
Th e Big Data t rend is ge nerating an enorm ous amount of
information from many new sources. This
data deluge requires advanced analytics and new market players
to take adva ntage of these opportunities
and new market dynamics, which wi ll be discussed in the
following section.
1.2.4 Emerging Big Data Ecosystem and a New Approach to
Analytics
Organ izations and data collectors are realizing that the data
they ca n gath er from individuals contain s
intrinsic value and, as a result, a new economy is emerging. As
this new digital economy continues to
61. 1.2 State of the Practice in Analytics
evol ve, the market sees the introduction of data vendors and
data cl eaners that use crowdsourcing (such
as Mechanica l Turk and Ga laxyZoo) to test the outcomes of
machine learning techniques. Other vendors
offer added va lue by repackaging open source tools in a
simpler way and bri nging the tools to market.
Vendors such as Cloudera, Hortonworks, and Pivotal have
provid ed thi s value-add for the open source
framework Hadoop.
As the new ecosystem takes shape, there are four main groups
of playe rs within this interconnected
web. These are shown in Figure 1-11.
• Data devices [shown in the (1) section of Figure 1-1 1] and the
"Sensornet" gat her data from multiple
locations and continuously generate new data about th is data.
For each gigabyte of new data cre-
ated, an additional petabyte of data is created about that data.
[2)
• For example, consider someone playing an online video game
through a PC, game console,
or smartphone. In this case, the video game provider captures
data about the skill and levels
attained by the playe r. Intelligent systems monitor and log how
and when the user plays the
game. As a consequence, the game provider can fine -tune the
difficulty of the game,
62. suggest other related games that would most likely interest the
user, and offer add itional
equipment and enhancements for the character based on the
user's age, gender, and
interests. Th is information may get stored loca lly or uploaded
to the game provider's cloud
to analyze t he gaming habits and opportunities for ups ell and
cross-sell, and identify
archetypica l profiles of specific kinds of users.
• Smartphones provide another rich source of data . In add ition
to messag ing and basic phone
usage, they store and transmit data about Internet usage, SMS
usage, and real-time location.
This metadata can be used for analyzing traffic patterns by sca
nning the density of smart-
phones in locations to track the speed of cars or the relative
traffi c congestion on busy
roads. In t his way, GPS devices in ca rs can give drivers real-
time updates an d offer altern ative
routes to avoid traffic delays .
• Retail shopping loyalty cards record not just the amo unt an
individual spends, but the loca-
tions of stores that person visits, the kind s of products
purchased, the stores where goods
are purchased most ofte n, and the combinations of prod ucts
purchased together. Collecting
this data provides insights into shopping and travel habits and
the likelihood of successful
advertiseme nt targeting for certa in types of retail promotions.
• Data collectors [the blue ovals, identified as (2) within Figure
1-1 1] incl ude sa mple entities that
col lect data from the dev ice and users.
63. • Data resul ts from a cable TV provider tracking the shows a
person wa tches, which TV
channels someone wi ll and will not pay for to watch on
demand, and t he prices someone is
will ing to pay fo r premiu m TV content
• Retail stores tracking the path a customer takes through their
store w hile pushing a shop-
ping cart with an RFID chip so they can gauge which products
get the most foot traffic using
geospatial data co llected from t he RFID chips
• Data aggregators (the dark gray ovals in Figure 1-11, marked
as (3)) make sense of the data co llected
from the various entities from the "Senso rN et" or the "Internet
ofThings." These org anizatio ns
compile data from the devices an d usage pattern s collected by
government agencies, retail stores,
INT RODUCTION TO BIG DATA ANALYTIC S
and websites. ln turn, t hey can choose to transform and package
the data as products to sell to list
brokers, who may want to generate marketing lists of people
who may be good targets for specific ad
campaigns.
• Data users and buyers are denoted by (4) in Figu re 1-11.
These groups directly benefit from t he data
collected and aggregated by others within the data value chain.
• Retai l ba nks, acting as a data buyer, may want to know
which customers have the hig hest
64. likelihood to apply for a second mortgage or a home eq uity line
of credit. To provide inpu t
for this analysis, retai l banks may purchase data from a data
aggregator. This kind of data
may include demograp hic information about people living in
specific locations; people who
appear to have a specific level of debt, yet still have solid credit
scores (or other characteris-
tics such as paying bil ls on time and having savings accounts)
that can be used to infer cred it
worthiness; and those who are sea rching the web for
information about paying off debts or
doing home remodeling projects. Obtaining data from these
various sources and aggrega-
tors will enable a more targeted marketing campaign, which
would have been more chal-
lenging before Big Data due to the lack of information or high-
performing technologies.
• Using technologies such as Hadoop to perform natural
language processing on
unstructured, textual data from social media websites, users can
gauge the reaction to
events such as presidential campaigns. People may, for
example, want to determine public
sentiments toward a candidate by analyzing related blogs and
online comments. Similarl y,
data users may want to track and prepare for natural disasters by
identifying which areas
a hurricane affects fi rst and how it moves, based on which
geographic areas are tweeting
about it or discussing it via social med ia.
r:t Data
.::J Devices {'[I t Ptto...r r.r..., l UC)(.K VlOLU l !I ill UO.
AI''
65. (,.MI
CfitUII CAfW COtPl!UR
RfAO(H
~ .~
Iff [) llOfO MfOICAI
IMoC'oi"G
Law
EniCHCefllefll
Data
Users/ Buyers
0
Media
FIGURE 1-11 Emerging Big Data ecosystem
Do live!)'
So Mea
'I If,. [Ill AN [
Privato
Investigators
/ lawyors
1.3 Key Roles for the New Big Data Ecosyst e m
66. As il lustrated by this emerging Big Data ecosystem, the kinds
of data and the related market dynamics
vary greatly. These data sets ca n include sensor data, text,
structured datasets, and social med ia . With this
in mind, it is worth recall ing that these data sets will not work
wel l within trad itional EDWs, which were
architected to streamline reporting and dashboards and be
centrally managed.lnstead, Big Data problems
and projects require different approaches to succeed.
Analysts need to partner with IT and DBAs to get the data they
need within an analytic sandbox. A
typical analytical sandbox contains raw data, agg regated data,
and data with mu ltiple kinds of structure.
The sandbox enables robust exploration of data and requires a
savvy user to leverage and take advantage
of data in the sandbox environment.
1.3 Key Roles for the New Big Data Ecosystem
As explained in the context of the Big Data ecosystem in
Section 1.2.4, new players have emerged to curate,
store, produce, clean, and transact data. In addition, the need
for applying more advanced ana lytica l tech-
niques to increasing ly complex business problems has driven
the emergence of new roles, new technology
platforms, and new analytical methods. This section explores
the new roles that address these needs, and
subsequent chapters explore some of the analytica l methods
and technology platforms.
The Big Data ecosystem demands three ca tegories of roles, as
shown in Figure 1-12. These roles were
67. described in the McKinsey Global study on Big Data, from May
2011 [1].
Three Key Roles of The New Data Ecosystem
Role
Deep Analytical Talent
Data Savvy Professionals
Technology and Data Enablers
Data Scientists
.. Projected U.S. tal ent
gap: 1.40 ,000 to 1.90,000
.. Projected U.S. talent
gap: 1..5 million
Note: RcuresaboYe m~ • projected talent CDP In US In 201.8.
as ihown In McKinsey May 2011 article "81& Data: l he Nut
rront* t ot
Innovation. Competition. and Product~
FIGURE 1-12 Key roles of the new Big Data ecosystem
The first group- Deep Analytical Talent- is technically savvy,
with strong analytical skills. Members pos-
sess a combi nation of skills to handle raw, unstructured data
and to apply complex analytical techniques at
INTRODUCTION TO BIG DATA ANALYTICS
68. massive scales. This group has advanced training in quantitative
disciplines, such as mathematics, statistics,
and machine learning. To do their jobs, members need access to
a robust analytic sandbox or workspace
where they can perform large-scale analytical data experiments.
Examples of current professions fitting
into this group include statisticians, economists,
mathematicians, and the new role of the Data Scientist.
The McKinsey study forecasts that by the year 2018, the United
States will have a talent gap of 140,000-
190,000 people with deep analytical talent. This does not
represent the number of people needed with
deep analytical talent; rather, this range represents the
difference between what will be available in the
workforce compared with what will be needed. In addition,
these estimates only reflect forecasted talent
shortages in the United States; the number would be much
larger on a global basis.
The second group-Data Savvy Professionals-has less technical
depth but has a basic knowledge of
statistics or machine learning and can define key questions that
can be answered using advanced analytics.
These people tend to have a base knowledge of working with
data, or an appreciation for some of the work
being performed by data scientists and others with deep
analytical talent. Examples of data savvy profes-
sionals include financial analysts, market research analysts, life
scientists, operations managers, and business
and functional managers.
The McKinsey study forecasts the projected U.S. talent gap for
this group to be 1.5 million people by
the year 2018. At a high level, this means for every Data
69. Scientist profile needed, the gap will be ten times
as large for Data Savvy Professionals. Moving toward becoming
a data savvy professional is a critical step
in broadening the perspective of managers, directors, and
leaders, as this provides an idea of the kinds of
questions that can be solved with data.
The third category of people mentioned in the study is
Technology and Data Enablers. This group
represents people providing technical expertise to support
analytical projects, such as provisioning and
administrating analytical sandboxes, and managing large-scale
data architectures that enable widespread
analytics within companies and other organizations. This role
requires skills related to computer engineering,
programming, and database administration.
These three groups must work together closely to solve complex
Big Data challenges. Most organizations
are familiar with people in the latter two groups mentioned, but
the first group, Deep Analytical Talent,
tends to be the newest role for most and the least understood.
For simplicity, this discussion focuses on
the emerging role of the Data Scientist. It describes the kinds of
activities that role performs and provides
a more detailed view of the skills needed to fulfill that role.
There are three recurring sets of activities that data scientists
perform:
o Reframe business challenges as analytics challenges.
Specifically, this is a skill to diagnose busi-
ness problems, consider the core of a given problem, and
determine which kinds of candidate analyt-
ical methods can be applied to solve it. This concept is explored
further in Chapter 2, "Data Analytics
70. lifecycle."
o Design, implement, and deploy statistical models and data
mining techniques on Big Data. This
set of activities is mainly what people think about when they
consider the role of the Data Scientist:
1.3 Key Roles for the New Big Data Ecosystem
namely, applying complex or advanced ana lytical methods to a
variety of busi ness problems using
data. Chapter 3 t hrough Chapter 11 of this book introd uces the
reader to many of the most popular
analytical techniques and tools in this area.
• Develop insights that lead to actionable recommendations. It
is critical to note that applying
advanced methods to data problems does not necessarily drive
new business va lue. Instead, it is
important to learn how to draw insights out of the data and
communicate them effectively. Chapter 12,
"The Endgame, or Putting It All Together;' has a brief overview
of techniques for doing this.
Data scientists are generally thoug ht of as having fi ve mai n
sets of skills and behaviora l characteristics,
as shown in Figure 1-13:
• Quantitative skill: such as mathematics or statistics
• Technical aptitude: namely, software engineering, machine
learning, and programming skills
71. • Skeptical mind-set and critica l thin king: It is important that
data scientists can examine their work
critica lly rather than in a one-sided way.
• Curious and creative: Data scientists are passionate about data
and finding creative ways to solve
problems and portray information.
• Communicative and collaborative: Data scie ntists must be
able to articulate the business val ue
in a clear way and collaboratively work with other groups,
including project sponsors and key
stakeholders.
Quantitative
Technical
Skeptical
Curious and
Creative
Communlcativr
and
CDDaborati~
fiGURE 1 Profile of a Data Scientist
INTRODUCTION TO BIG DATA ANALYTICS
Data scientists are generally comfortable using this blend of
skills to acquire, manage, analyze, and
72. visualize data and tell compelling stories about it. The next
section includes examples of what Data Science
teams have created to drive new value or innovation with Big
Data.
1.4 Examples of Big Data Analytics
After describing the emerging Big Data ecosystem and new
roles needed to support its growth, this section
provides three examples of Big Data Analytics in different
areas: retail, IT infrastructure, and social media.
As mentioned earlier, Big Data presents many opportunities to
improve sa les and marketing ana lytics.
An example of this is the U.S. retailer Target. Cha rles Duhigg's
book The Power of Habit [4] discusses how
Target used Big Data and advanced analytical methods to drive
new revenue. After analyzing consumer-
purchasing behavior, Target's statisticians determin ed that the
retailer made a great deal of money from
three main life-event situations.
• Marriage, when people tend to buy many new products
• Divorce, when people buy new products and change their
spending habits
• Pregnancy, when people have many new things to buy and
have an urgency to buy t hem
Target determined that the most lucrative of these life-events is
the thi rd situation: pregnancy. Using
73. data collected from shoppers, Ta rget was able to identify this
fac t and predict which of its shoppers were
pregnant. In one case, Target knew a female shopper was
pregnant even before her family knew [5]. This
kind of knowledge allowed Target to offer specifi c coupons and
incentives to thei r pregnant shoppers. In
fact, Target could not only determine if a shopper was pregnant,
but in which month of pregnancy a shop-
per may be. This enabled Target to manage its inventory, knowi
ng that there would be demand for specific
products and it wou ld likely vary by month over the com ing
nine- to ten- month cycl es.
Hadoop [6] represents another example of Big Data innovation
on the IT infra structure. Apache Hadoop
is an open source framework that allows companies to process
vast amounts of information in a highly paral-
lelized way. Hadoop represents a specific implementation of t
he MapReduce paradigm and was designed
by Doug Cutting and Mike Cafa rel la in 2005 to use data with
varying structu res. It is an ideal technical
framework for many Big Data projects, which rely on large or
unwieldy data set s with unconventiona l data
structures. One of the main benefits of Hadoop is that it
employs a distributed file system, meaning it can
use a distributed cluster of servers and commodity hardware to
process larg e amounts of data. Some of
the most co mmon examples of Hadoop imp lementations are in
the social med ia space, where Hadoop
ca n manage transactions, give textual updates, and develop
74. social graphs among millions of users. Twitter
and Facebook generate massive amounts of unstructured data
and use Hadoop and its ecosystem of tools
to manage this hig h volu me. Hadoop and its ecosystem are
covered in Chapter 10, "Adva nced Ana lytics-
Technology and Tools: MapReduce and Hadoop."
Finally, social media represents a tremendous opportunity to
leverage social and professional interac-
tions to derive new insights. Linked In exemplifies a company
in which data itself is the product. Early on,
Linkedln founder Reid Hoffman saw the opportunity to create a
social network for working professionals.
Exercises
As of 2014, Linkedln has more than 250 million user accounts
and has added many additional features and
data-related products, such as recruiting, job seeker too ls,
advertising, and lnMa ps, whic h show a social
graph of a user's professional network. Figure 1-14 is an
example of an In Map visualization that enables
a Linked In user to get a broader view of the interconnectedness
of his contacts and understand how he
knows most of them .
fiGURE 1-14 Data visualization of a user's social network using
lnMaps
Summary
Big Data comes from myriad sources, including social media,
75. sensors, the Internet ofThings, video surveil-
lance, and many sources of data that may not have been
considered data even a few years ago. As businesses
struggle to keep up with changing market requirements, some
companies are finding creative ways to apply
Big Data to their growing business needs and increasing ly
complex problems. As organizations evolve
their processes and see the opportunities that Big Data can
provide, they try to move beyond t raditional Bl
activities, such as using data to populate reports and
dashboards, and move toward Data Science- driven
projects that attempt to answer more open-ended and complex
questions.
However, exploiting the opportunities that Big Data presents
requires new data architectures, includ -
ing analytic sandboxes, new ways of working, and people with
new skill sets. These drivers are causing
organizations to set up analytic sandboxes and build Data
Science teams. Although some organizations are
fortunate to have data scientists, most are not, because there is a
growing talent gap that makes finding
and hi ring data scientists in a timely man ner difficult. Still,
organizations such as those in web retail, health
care, genomics, new IT infrast ructures, and social media are
beginning to take advantage of Big Data and
apply it in creati ve and novel ways.
Exercises
1. What are the three characteristics of Big Data, and what are
the main considerations in processing Big
76. Data?
2 . What is an analytic sa ndbox, and why is it important?
3. Explain the differences between Bland Data Science.
4 . Describe the challenges of the current analytical architecture
for data scientists.
5. What are the key skill sets and behavioral characteristics of a
data scientist?
INTRODUCTION TO BIG DATA ANALYTICS
Bibliography
[1] C. B. B. D. Manyika, "Big Data: The Next Frontier for
Innovation, Competition, and Productivity,"
McKinsey Globa l Institute, 2011 .
[2] D. R. John Ga ntz, "The Digital Universe in 2020: Big Data,
Bigger Digital Shadows, and Biggest
Growth in the Far East," IDC, 2013.
[3] http: I l www. willisresilience . coml emc-data l ab [Online].
[4] C. Duhigg, The Power of Habit: Why We Do What We Do in
Life and Business, New York: Random
House, 2012.
[5] K. Hil l, "How Target Figured Out a Teen Girl Was Pregnant
Before Her Father Did," Forb es, February
2012.
[6] http: I l hadoop. apache . org [Online].
77. DATA ANALYTICS LIFECYCLE
Data science projects differ from most traditional Business
Intelligence projects and many data ana lysis
projects in that data science projects are more exploratory in
nature. For t his reason, it is critical to have a
process to govern them and ensure t hat the participants are
thorough and rigorous in their approach, yet
not so rigid that the process impedes exploration.
Many problems that appear huge and daunting at first can be
broken down into smaller pieces or
actionable phases that can be more easily addressed. Having a
good process ensures a comprehensive and
repeatable method for conducting analysis. In addition, it helps
focus time and energy early in the process
to get a clear grasp of the business problem to be solved.
A common mistake made in data science projects is rushing into
data collection and analysis, wh ich
precludes spending sufficient time to plan and scope the amount
of work involved, understanding requ ire-
ments, or even framing the business problem properly.
Consequently, participants may discover mid-stream
that the project sponsors are actually trying to achieve an
objective that may not match the available data,
or they are attempting to address an interest that differs from