Introduction to Business Analytics Part 1 published by BeamSync.
BeamSync is providing business analytics training course in Bangalore. If you are looking for analytics training then visit BeamSync. Regular classes are running during the weekend.
For details visit: http://beamsync.com/business-analytics-training-bangalore/
Business Intelligence And Business Analytics | ManagementTransweb Global Inc
Business Intelligence is the initial basic step of Business Analytics. It refers to gathering raw and complex data, and converting it into systematic and logical information in a format that is usable by the end user. Copy the link given below and paste it in new browser window to get more information on Business Intelligence And Business Analytics:-
http://www.transtutors.com/homework-help/management/managing-information-technology/business-intelligence-analytics/
Introduction to Business Analytics Part 1 published by BeamSync.
BeamSync is providing business analytics training course in Bangalore. If you are looking for analytics training then visit BeamSync. Regular classes are running during the weekend.
For details visit: http://beamsync.com/business-analytics-training-bangalore/
Business Intelligence And Business Analytics | ManagementTransweb Global Inc
Business Intelligence is the initial basic step of Business Analytics. It refers to gathering raw and complex data, and converting it into systematic and logical information in a format that is usable by the end user. Copy the link given below and paste it in new browser window to get more information on Business Intelligence And Business Analytics:-
http://www.transtutors.com/homework-help/management/managing-information-technology/business-intelligence-analytics/
This presentation will help you understand the basic building blocks of Business Intelligence. Learn how decisions are triggered, the complete decision process and who makes decisions in the corporate world.
More importantly, understand core components of a Business Intelligence architecture such as a data warehouse, data mining, OLAP (Online analytical procession) , OLTP (Online Transaction Processing) and data reporting. Each component plays an integral part which enables today's managers and decision makers collect, analyze and interpret data to make it actionable for decision making.
Business intelligence has become an integral part that needs to be incorporated to ensure business survival. It is a tool that helps analyze historical data and forecast future so that your are always one step ahead in your business.
Please feel free to like, share and comment as you please!
In this new Accenture Finance & Risk presentation we explore how our capability design and data sourcing methodology provides financial services firms with a framework for addressing their data processing, report population and reporting requirement needs.
Highlights of the Business Analytics seminar by Gary Cokins from October 21, 2014 presentation with Illinois CPA Society.
Gary Cokins is an internationally recognized expert, speaker, and author in performance improvement systems and cost management.
http://www.GaryCokins.com
This presentation introduces big data and explains how to generate actionable insights using analytics techniques. The deck explains general steps involved in a typical analytics project and provides a brief overview of the most commonly used predictive analytics methods and their business applications.
Vijay Adamapure is a Data Science Enthusiast with extensive experience in the field of data mining, predictive modeling and machine learning. He has worked on numerous analytics projects ranging from healthcare, business analytics, renewable energy to IoT.
Vijay presented these slides during the Internet of Everything Meetup event 'Predictive Analytics - An Overview' that took place on Jan. 9, 2015 in Mumbai. To join the Meetup group, register here: http://bit.ly/1A7T0A1
Business intelligence- Components, Tools, Need and Applicationsraj
As part of the research project for the course Technical Foundations of Information Systems at the University of Illinois, our team worked on the topic, Business Intelligence. The presentation focuses on what is Business Intelligence, its various components, latest tools, the need of BI as well as applications of this technology. This project deals with the latest development of BI technologies (hardware or software) and includes comprehensive literature survey from Journals, and the Internet.
This presentation is an Introduction to the importance of Data Analytics in Product Management. During this talk Etugo Nwokah, former Chief Product Officer for WellMatch, covered how to define Data Analytics why it should be a first class citizen in any software organization
This presentation will help you understand the basic building blocks of Business Intelligence. Learn how decisions are triggered, the complete decision process and who makes decisions in the corporate world.
More importantly, understand core components of a Business Intelligence architecture such as a data warehouse, data mining, OLAP (Online analytical procession) , OLTP (Online Transaction Processing) and data reporting. Each component plays an integral part which enables today's managers and decision makers collect, analyze and interpret data to make it actionable for decision making.
Business intelligence has become an integral part that needs to be incorporated to ensure business survival. It is a tool that helps analyze historical data and forecast future so that your are always one step ahead in your business.
Please feel free to like, share and comment as you please!
In this new Accenture Finance & Risk presentation we explore how our capability design and data sourcing methodology provides financial services firms with a framework for addressing their data processing, report population and reporting requirement needs.
Highlights of the Business Analytics seminar by Gary Cokins from October 21, 2014 presentation with Illinois CPA Society.
Gary Cokins is an internationally recognized expert, speaker, and author in performance improvement systems and cost management.
http://www.GaryCokins.com
This presentation introduces big data and explains how to generate actionable insights using analytics techniques. The deck explains general steps involved in a typical analytics project and provides a brief overview of the most commonly used predictive analytics methods and their business applications.
Vijay Adamapure is a Data Science Enthusiast with extensive experience in the field of data mining, predictive modeling and machine learning. He has worked on numerous analytics projects ranging from healthcare, business analytics, renewable energy to IoT.
Vijay presented these slides during the Internet of Everything Meetup event 'Predictive Analytics - An Overview' that took place on Jan. 9, 2015 in Mumbai. To join the Meetup group, register here: http://bit.ly/1A7T0A1
Business intelligence- Components, Tools, Need and Applicationsraj
As part of the research project for the course Technical Foundations of Information Systems at the University of Illinois, our team worked on the topic, Business Intelligence. The presentation focuses on what is Business Intelligence, its various components, latest tools, the need of BI as well as applications of this technology. This project deals with the latest development of BI technologies (hardware or software) and includes comprehensive literature survey from Journals, and the Internet.
This presentation is an Introduction to the importance of Data Analytics in Product Management. During this talk Etugo Nwokah, former Chief Product Officer for WellMatch, covered how to define Data Analytics why it should be a first class citizen in any software organization
Data Analysis Methods 101 - Turning Raw Data Into Actionable InsightsDataSpace Academy
Data analytics is powerful for organisations. It can help companies improve their overall efficiency and effectiveness. The blog offers a step-by-step narration of the data analysis methods that will help you to comprehend the fundamentals of an analytics project.
Data analytics courses are educational programs designed to teach individuals the skills and techniques needed to work with data, analyze it, and extract meaningful insights.
For More Details: https://datamites.com/data-analytics-certification-course-training-pune/
Data Analytics Certification in Pune-JanuaryDataMites
A data analytics course is an educational program designed to teach individuals the skills and techniques necessary for analyzing and interpreting data to extract meaningful insights.
For more details visit: https://datamites.com/data-analytics-certification-course-training-pune/
A data analytics course is an educational program designed to teach individuals the skills and techniques necessary for analyzing and interpreting data to extract meaningful insights.
For more details visit: https://datamites.com/data-analytics-certification-course-training-chennai/
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Introductions to Business Analytics
1. Analytics
• Analytics can range from a simple exploration into how many sales of a particular
product were made last year to a complex neural network model predicting which
customers to target for next year’s marketing campaign.
• In ‘Competing on analytics’, Thomas Davenport defines analytics as “the extensive use of
data, statistical and quantitative analysis, exploratory, predictive models, and fact-based
management to drive decisions and actions.”
• In layman’s terms it can be defined as “the analysis of data to draw hidden insights to
aid decision making”.
• There is a little bit of analyst in everyone. Analytics is an integral part of most
businesses.
• You do not need to be an “analyst” to do analysis. Analytics is an essential skill for
running any kind of business successfully.
• Common applications of analytics include the study of business data using statistical
analysis in order to discover and understand historical patterns with an eye to
predicting and improving business performance in the future.
2. Data: Basic Definition
• Data: Data is a set of values of qualitative or quantitative variables. It
is information in raw or unorganized form. It may be a fact, figure,
characters, symbols etc.
• Information: Meaningful or organized data is information.
• Analytics: Analytics is the discovery , interpretation, and
communication of meaningful patterns or summery in data.
• Data Analytics (DA) is the process of examining data sets in order to
draw conclusion about the information it contains.
• Analytics is not a tool or technology, rather it is the way of thinking
and acting on data.
3.
4.
5. Types of analytics
1. Descriptive Analytics (“What has happened?”) (Data aggregation,
summary, data mining)
2. Predictive Analytics (“What might happen?”) (Regression, LSE,MLE)
3. Prescriptive Analytics (“What should we do?”) (Optimization,
Recommendation)
7. Role of Data Analytics:
• Gather Hidden Insights – Hidden insights from data are gathered and then
analyzed with respect to business requirements.
• Generate Reports – Reports are generated from the data and are passed
on to the respective teams and individuals to deal with further actions for a
high rise in business.
• Perform Market Analysis – Market Analysis can be performed to
understand the strengths and weaknesses of competitors.
• Improve Business Requirement – Analysis of Data allows
improving Business to customer requirements and experience.
8.
9. Analytics Life Cycle
• 1. Problem Identification
• 2. Hypothesis formulation
• 3. Data Collection
• 4. Data Exploration/preparation
• 5. Model Building
• 6. Model Validation and Evaluation
10. Analytics Life Cycle(Cont..)
• 1. Problem Identification
• The problem is a situation which is judged to be corrected or solved
• Problem can be identified through
• 1. Comparative/benchmarking studies
• 2. Performance Reporting
• 3. Asking some basic questions
a) Who are affected by the problem?
b) What will happen if problem is not solved?
c) When and where does the problem occur?
d) Why is the problem occurring
e) How are the people currently handling the problem?
11. Analytics Life Cycle(Cont..)
2. Hypothesis formulation
1. Frame the questions which need to be answered.
2. Develop a comprehensive list of all possible issues related to the problem.
3. Reduce the list by eliminating duplicates and combining overlapping issues.
4. Using consensus building get down to a major issue list.
3. Data Collection
Data collection techniques are
1. Using data that is already collected by others
2. Systematically selecting and watching characteristics of people, objects, and events.
3. Oral questioning respondents either individually or as a group
4. Collecting data based on answers provided by the respondents in written format.
12. Analytics Life Cycle(Cont..)
4. Data Exploration
1. Importing data
2. Variable Identification
3. Data Cleaning
4. Summarizing data
5. Selecting subset of data
5. Model Building
• Building a Model is a very iterative process because there is no such thing
as final and perfect solution.
• Many of the machine learning and statistical techniques are available in
traditional technology platform
13. Analytics Life Cycle(Cont..)
• 6. Model validation and Evaluation
• Like model building the process of validating model is also a iterative process.
There are so many ways …
• Confusion Matrix.
• Confidence Interval.
• ROC curve
• Chi Square.
• Root Mean Square Error
• Gain and Lift Chart.
14. Data Analytics
• Data Analytics refers to the techniques used to analyze data to
enhance productivity and business gain.
• Data is extracted from various sources and is cleaned and categorized
to analyze various behavioral patterns.
• The techniques and the tools used vary according to the organization
or individual.
15. Role of Data Analytics
• Gather Hidden Insights – Hidden insights from data are gathered and
then analyzed with respect to business requirements.
• Generate Reports – Reports are generated from the data and are
passed on to the respective teams and individuals to deal with further
actions for a high rise in business.
• Perform Market Analysis – Market Analysis can be performed to
understand the strengths and weaknesses of competitors.
• Improve Business Requirement – Analysis of Data allows
improving Business to customer requirements and experience.
•
16. Data analyst vs. data scientist
• While data analysts and data scientists may be commingled on analytics
teams, their roles differ considerably.
• Data analysts seek to describe the current state of reality for their
organizations by translating data into information accessible to the
business.
• They collect, analyze, and report on data to meet business needs. The role
includes identifying new sources of data and methods to improve data
collection, analysis, and reporting.
• Data scientists, on the other hand, are often engaged in long-term
research and prediction, while data analysts seek to support business
leaders in making tactical decisions through reporting and ad hoc queries.
18. Evolution of Business Analytics
• However, the modern evolution of analytics began with the
introduction of computers in the late 1940s and their development
through the 1960s and beyond
• Early computers provided the ability to store and analyze data in ways
that were either very difficult or impossible to do so manually.
• This facilitated the collection, management, analysis, and reporting of
data, which is often called business intelligence (BI), a term that was
coined in 1958 by an IBM researcher, Hans Peter Luhn.
19. Business intelligence software can answer
• “How many units did we sell last month?”
• “What products did customers buy and how much did they spend?”
“How many credit card transactions were completed yesterday?”
20. Conti….
• Statistics has a long and rich history, yet only rather recently has it been
recognized as an important element of business, driven to a large extent by
the massive growth of data in today’s world.
• Statistical methods allow us to gain a richer understanding of data that
goes beyond business intelligence reporting by not only summarizing data
succinctly but also finding unknown and interesting relationships among
the data.
• Statistical methods include the basic tools of description, exploration,
estimation, and inference, as well as more advanced techniques like
regression, forecasting, and data mining.
21. Conti
• Much of modern business analytics stems from the analysis and
solution of complex decision problems using mathematical or
computer-based models—a discipline
• known as operations research, or management science. Operations
research (OR) was born from efforts to improve military operations
prior to and during World War II.
• After the war, scientists recognized that the mathematical tools and
techniques developed for military applications could be applied
successfully to problems in business and industry.
22. Conti….
• A significant amount of research was carried on in public and private
think tanks during the late 1940s and through the 1950s
• As the focus on business applications expanded, the term
management science (MS) became more prevalent. Many people use
the terms operations research and management science
interchangeably, and the field became known as Operations
Research/Management Science (OR/MS).
23. Conti….
• Many OR/MS applications use modeling and optimization—
techniques for translating real problems into mathematics,
spreadsheets, or other computer languages, and using them to find
the best (“optimal”) solutions and decisions.
• INFORMS, the Institute for Operations Research and the
Management Sciences, is the leading professional society devoted to
OR/MS and analytics, and publishes a bimonthly magazine called
Analytics
24. Conti…
• Decision support systems (DSS) began to evolve in the 1960s by
combining busines intelligence concepts with OR/MS models to
create analytical-based computer systems to support decision
making. DSSs include three components:
1. Data management. The data management component includes databases
for
storing data and allows the user to input, retrieve, update, and manipulate data.
2. Model management. The model management component consists of various
statistical tools and management science models and allows the user to easily
build, manipulate, analyze, and solve models.
3. Communication system. The communication system component provides the
interface necessary for the user to interact with the data and model
management
components
25. DSSs have been used for many applications,
including
• Pension fund management,
• Portfolio management,
• Work-shift scheduling,
• Global manufacturing and
• Facility location,
• Advertising-budget allocation,
• Media planning,
• Distribution planning,
• Airline operations planning,
• Inventory control, library
management,
• Classroom assignment,
• Nurse scheduling,
• Blood distribution,
• Water pollution control,
• Energy planning
26.
27. Conti…
• Modern business analytics can be viewed as an integration of BI/IS,
statistics,and modeling and optimization
• data mining is focused on better understanding characteristics and
patterns among variables in large databases using a variety of statistical
and analytical tools
• Simulation and risk analysis relies on spreadsheet models and statistical
analysis to examine the impacts of uncertainty in the estimates and their
potential interaction with one another on the output variable of interest.
28. • what-if analysis—how specific combinations of inputs that reflect key
assumptions will affect model outputs.
• What-if analysis is also used to assess the sensitivity of optimization
models to changes in data inputs and provide better insight for
making good decisions.
• Visualizing data and results of analyses provide a way of easily
communicating data at all levels of a business and can reveal
surprising patterns and relationships
29. • The power of analytics in a personal computing environment was
noted some 20 years ago
• By business consultants Michael Hammer and James Champy, who
said, “When accessible data is combined with easy-to-use analysis
and modeling tools, frontline workers
• Although many good analytics software packages are available to
professionals, we use Microsoft Excel and a powerful add-in called
Analytic Solver Platform
30. Business Analytics Is Important ?
1. Enhance Customer Experience
2. Make Informed Decisions
3. Reduce Employee Turnover
4. Improve Efficiency
5. Identify Frauds
31. Business Analytics Is Important ?
6. Cut Manufacturing Costs
7. Make The Most Of Your Investment
8. Improved Advertising
9. Better Product Management
10. Tackle Problems
11. Accelerate Through Uncertainty
12. Conduct A Competitor Analysis