Data-Ed Online: Practical Applications for Data Warehousing, Analytics, BI, and Meta-Integration Technologies
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Data-Ed Online: Practical Applications for Data Warehousing, Analytics, BI, and Meta-Integration Technologies

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This webinar aired originally on Tuesday, July 10, 2012. It is part of Data Blueprint’s ongoing webinar series on data management with Dr. Peter Aiken. ...

This webinar aired originally on Tuesday, July 10, 2012. It is part of Data Blueprint’s ongoing webinar series on data management with Dr. Peter Aiken.

Sign up for future sessions at http://www.datablueprint.com/webinar-schedule.

Abstract
Meta-integration is considered data warehousing by some, while others describe it as data virtualization. This presentation provides an overview of meta-integration starting with organizational requirements. We will discuss how meta-models can be used to jump-start organizational efforts. Participants will understand the strengths and weaknesses of various technological capabilities, and the key role of data quality in all of them.

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  • 1977-2010=33 years\n
  • 1977-2010=33 years\n
  • 1977-2010=33 years\n
  • 1977-2010=33 years\n
  • 1977-2010=33 years\n
  • 1977-2010=33 years\n
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  • Britain introduced the convoy system to reduce shipping losses, but while the principle of using warships to accompany merchant ships was generally accepted, it was unclear whether it was better for convoys to be small or large. Convoys travel at the speed of the slowest member, so small convoys can travel faster. It was also argued that small convoys would be harder for German U-boats to detect. On the other hand, large convoys could deploy more warships against an attacker. Blackett's staff showed that the losses suffered by convoys depended largely on the number of escort vessels present, rather than on the overall size of the convoy. Their conclusion, therefore, was that a few large convoys are more defensible than many small ones.\n
  • Britain introduced the convoy system to reduce shipping losses, but while the principle of using warships to accompany merchant ships was generally accepted, it was unclear whether it was better for convoys to be small or large. Convoys travel at the speed of the slowest member, so small convoys can travel faster. It was also argued that small convoys would be harder for German U-boats to detect. On the other hand, large convoys could deploy more warships against an attacker. Blackett's staff showed that the losses suffered by convoys depended largely on the number of escort vessels present, rather than on the overall size of the convoy. Their conclusion, therefore, was that a few large convoys are more defensible than many small ones.\n
  • Britain introduced the convoy system to reduce shipping losses, but while the principle of using warships to accompany merchant ships was generally accepted, it was unclear whether it was better for convoys to be small or large. Convoys travel at the speed of the slowest member, so small convoys can travel faster. It was also argued that small convoys would be harder for German U-boats to detect. On the other hand, large convoys could deploy more warships against an attacker. Blackett's staff showed that the losses suffered by convoys depended largely on the number of escort vessels present, rather than on the overall size of the convoy. Their conclusion, therefore, was that a few large convoys are more defensible than many small ones.\n
  • Britain introduced the convoy system to reduce shipping losses, but while the principle of using warships to accompany merchant ships was generally accepted, it was unclear whether it was better for convoys to be small or large. Convoys travel at the speed of the slowest member, so small convoys can travel faster. It was also argued that small convoys would be harder for German U-boats to detect. On the other hand, large convoys could deploy more warships against an attacker. Blackett's staff showed that the losses suffered by convoys depended largely on the number of escort vessels present, rather than on the overall size of the convoy. Their conclusion, therefore, was that a few large convoys are more defensible than many small ones.\n
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Data-Ed Online: Practical Applications for Data Warehousing, Analytics, BI, and Meta-Integration Technologies Data-Ed Online: Practical Applications for Data Warehousing, Analytics, BI, and Meta-Integration Technologies Presentation Transcript

  • Welcome! TITLE Data Warehousing, Analytics, BI and Meta-Integration Technologies Webinar Date: July 10, 2012 Time: 2:00 PM ET Presented by: Dr. Peter Aiken PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 107/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE Commonly Asked Questions 1) Will I get copies of the slides after the event? YES 2) Is this being recorded so I can view it afterwards? YES PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 207/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE Live Twitter Feed & Follow Us on Facebook Join the conversation on Twitter! www.facebook.com/datablueprint Follow us @datablueprint and Post questions and comments @paiken Find industry news, insightful Ask questions and submit your content comments: #dataed and event updates PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/12 306/06/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • LinkedIn Group: Join the Discussion TITLE New Group: Data Management & Business Intelligence PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 407/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE Meet Your Presenter: Dr. Peter Aiken • Internationally recognized thought-leader in the data management field with more than 30 years of experience • Recipient of the 2010 International Stevens Award • Founding Director of Data Blueprint (http://datablueprint.com) • Associate Professor of Information Systems at Virginia Commonwealth University (http://vcu.edu) • President of DAMA International (http://dama.org) • DoD Computer Scientist, Reverse Engineering Program Manager/ Office of the Chief Information Officer • Visiting Scientist, Software Engineering Institute/Carnegie Mellon University • 7 books and dozens of articles • Experienced w/ 500+ data management practices in 20 countries #dataed PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 507/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • Data Warehousing, Analytics, BI, Meta-Integration TechnologiesData Warehousing, Analytics, BI, Meta-Integration Technologies 7/10/2012
  • Data Warehousing, Analytics, BI, Meta-Integration Technologies 7/10/2012
  • Data Warehousing, Analytics, BI, Meta-Integration TechnologiesData Warehousing, Analytics, BI, Meta-Integration Technologies 7/10/2012
  • Data Warehousing, Analytics, BI, Meta-Integration TechnologiesData Warehousing, Analytics, BI, Meta-Integration Technologies 7/10/2012
  • TITLE Abstract: DW, Analytics, BI, Meta-Integration Technologies Meta-integration is considered data warehousing by some, while others describe it as data virtualization. This presentation provides an overview of meta- integration starting with organizational requirements. We will discuss how meta-models can be used to jump- start organizational efforts. Participants will understand the strengths and weaknesses of various technological capabilities, and the key role of data quality in all of them. PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 707/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE Outline 1. Data management overview 2. What are DW, analytics, BI and meta- integration technologies and why are they important? 3. Top 10 causes of data warehouse failures 4. DW & architecture focus 5. Business intelligence focus 6. The use of meta models 7. DW, analytics & BI building blocks 8. Guiding principles & best practices Tweeting now: 9. Take aways, references and Q&A #dataed PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 807/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE Outline 1. Data management overview 2. What are DW, analytics, BI and meta- integration technologies and why are they important? 3. Top 10 causes of data warehouse failures 4. DW & architecture focus 5. Business intelligence focus 6. The use of meta models 7. DW, analytics & BI building blocks 8. Guiding principles & best practices Tweeting now: 9. Take aways, references and Q&A #dataed PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 807/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE The DAMA Guide to the Data Management Body of Knowledge Data Management Functions PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 907/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE The DAMA Guide to the Data Management Body of Knowledge Published by DAMA International Data Management Functions PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 907/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE The DAMA Guide to the Data Management Body of Knowledge Published by DAMA International • The professional association for Data Managers (40 chapters worldwide) Data Management Functions PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 907/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE The DAMA Guide to the Data Management Body of Knowledge Published by DAMA International • The professional association for Data Managers (40 chapters worldwide) DMBoK organized around Data Management Functions PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 907/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE The DAMA Guide to the Data Management Body of Knowledge Published by DAMA International • The professional association for Data Managers (40 chapters worldwide) DMBoK organized around • Primary data management functions focused around data delivery to the organization Data Management Functions PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 907/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE The DAMA Guide to the Data Management Body of Knowledge Published by DAMA International • The professional association for Data Managers (40 chapters worldwide) DMBoK organized around • Primary data management functions focused around data delivery to the organization • Organized around several environmental elements Data Management Functions PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 907/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE The DAMA Guide to the Data Management Body of Knowledge Published by DAMA International • The professional association for Data Managers (40 chapters worldwide) DMBoK organized around • Primary data management functions focused around data delivery to the organization • Organized around several environmental elements Data Management Functions PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 907/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE The DAMA Guide to the Data Management Body of Knowledge PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 1007/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE The DAMA Guide to the Data Management Body of Knowledge Environmental Elements PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 1007/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE The DAMA Guide to the Data Management Body of Knowledge Amazon: http:// www.amazon.com/ DAMA-Guide- Management- Knowledge-DAMA- DMBOK/dp/ 0977140083 Or enter the terms "dama dm bok" at the Amazon search engine Environmental Elements PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 1007/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE Data Management PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 1107/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE Data Management Data Program Coordination Organizational Data Integration Data Data Stewardship Development Data Support Operations PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 1207/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE Data Management Manage data coherently. Data Program Coordination Organizational Data Integration Data Data Stewardship Development Data Support Operations PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 1207/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE Data Management Manage data coherently. Data Program Coordination Share data across boundaries. Organizational Data Integration Data Data Stewardship Development Data Support Operations PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 1207/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE Data Management Manage data coherently. Data Program Coordination Share data across boundaries. Organizational Data Integration Data Data Stewardship Development Assign responsibilities for data. Data Support Operations PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 1207/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE Data Management Manage data coherently. Data Program Coordination Share data across boundaries. Organizational Data Integration Data Data Stewardship Development Assign responsibilities for data. Engineer data delivery systems. Data Support Operations PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 1207/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE Data Management Manage data coherently. Data Program Coordination Share data across boundaries. Organizational Data Integration Data Data Stewardship Development Assign responsibilities for data. Engineer data delivery systems. Data Support Operations Maintain data availability. PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 1207/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE Data Management PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 1307/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE Summary: Data Warehousing & Business Intelligence Management PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 1407/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE Outline 1. Data management overview 2. What are DW, analytics, BI and meta- integration technologies and why are they important? 3. Top 10 causes of data warehouse failures 4. DW & architecture focus 5. Business intelligence focus 6. The use of meta models 7. DW, analytics & BI building blocks 8. Guiding principles & best practices Tweeting now: 9. Take aways, references and Q&A #dataed PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 1507/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE Outline 1. Data management overview 2. What are DW, analytics, BI and meta- integration technologies and why are they important? 3. Top 10 causes of data warehouse failures 4. DW & architecture focus 5. Business intelligence focus 6. The use of meta models 7. DW, analytics & BI building blocks 8. Guiding principles & best practices Tweeting now: 9. Take aways, references and Q&A #dataed PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 1507/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE DW, Analytics, BI, Meta-Integration Technologies from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 1607/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE DW, Analytics, BI, Meta-Integration Technologies Definitions from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 1607/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE DW, Analytics, BI, Meta-Integration Technologies Definitions • Beyond the nuts and bolts of data management from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 1607/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE DW, Analytics, BI, Meta-Integration Technologies Definitions • Beyond the nuts and bolts of data management • Analysis of information that had not been integrated previously from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 1607/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE DW, Analytics, BI, Meta-Integration Technologies Definitions • Beyond the nuts and bolts of data management • Analysis of information that had not been integrated previously Business Intelligence from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 1607/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE DW, Analytics, BI, Meta-Integration Technologies Definitions • Beyond the nuts and bolts of data management • Analysis of information that had not been integrated previously Business Intelligence • Dates at least to 1958 from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 1607/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE DW, Analytics, BI, Meta-Integration Technologies Definitions • Beyond the nuts and bolts of data management • Analysis of information that had not been integrated previously Business Intelligence • Dates at least to 1958 • Support better business decision making from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 1607/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE DW, Analytics, BI, Meta-Integration Technologies Definitions • Beyond the nuts and bolts of data management • Analysis of information that had not been integrated previously Business Intelligence • Dates at least to 1958 • Support better business decision making • Technologies, applications and practices for the collection, integration, analysis, and presentation of business information from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 1607/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE DW, Analytics, BI, Meta-Integration Technologies Definitions • Beyond the nuts and bolts of data management • Analysis of information that had not been integrated previously Business Intelligence • Dates at least to 1958 • Support better business decision making • Technologies, applications and practices for the collection, integration, analysis, and presentation of business information • Also described as decision support from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 1607/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE DW, Analytics, BI, Meta-Integration Technologies Definitions • Beyond the nuts and bolts of data management • Analysis of information that had not been integrated previously Business Intelligence • Dates at least to 1958 • Support better business decision making • Technologies, applications and practices for the collection, integration, analysis, and Data Warehousing presentation of business • Operational extract, cleansing, information • Also described as decision transformation, load, and support associated control processes for integrating disparate data into a single conceptual database from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 1607/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE Definitions, cont’d PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 1707/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE Definitions, cont’d • Study of data to discover and understand historical patterns to improve future performance PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 1707/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE Definitions, cont’d • Study of data to discover and understand historical patterns to improve future performance • Use of mathematics in business PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 1707/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE Definitions, cont’d • Study of data to discover and understand historical patterns to improve future performance • Use of mathematics in business • Analytics closely resembles statistical analysis and data mining – based on modeling involving extensive computation. PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 1707/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE Definitions, cont’d • Study of data to discover and understand historical patterns to improve future performance • Use of mathematics in business • Analytics closely resembles statistical analysis and data mining – based on modeling involving extensive computation. • Some fields within the area of analytics are – enterprise decision management, marketing analytics, predictive science, strategy science, credit risk analysis and fraud analytics. PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 1707/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE Warehousing Definitions PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 1807/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE Warehousing Definitions • Inmon: – "A subject oriented, integrated, time variant, and non-volatile collection of summary and detailed historical data used to support the strategic decision-making processes of the organization." PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 1807/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE Warehousing Definitions • Inmon: – "A subject oriented, integrated, time variant, and non-volatile collection of summary and detailed historical data used to support the strategic decision-making processes of the organization." • Kimball: – "A copy of transaction data specifically structured for query and analysis." PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 1807/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE Warehousing Definitions • Inmon: – "A subject oriented, integrated, time variant, and non-volatile collection of summary and detailed historical data used to support the strategic decision-making processes of the organization." • Kimball: – "A copy of transaction data specifically structured for query and analysis." • Key concepts focus on: – Subjects – Transactions – Non-volatility – Restructuring PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 1807/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE Example: Portfolio Analysis PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 1907/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE Example: Portfolio Analysis • Bank accounts are of varying value and risk PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 1907/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE Example: Portfolio Analysis • Bank accounts are of varying value and risk • Cube by – Social status – Geographical location – Net value, etc. PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 1907/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE Example: Portfolio Analysis • Bank accounts are of varying value and risk • Cube by – Social status – Geographical location – Net value, etc. • Balance return on the loan with risk of default PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 1907/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE Example: Portfolio Analysis • Bank accounts are of varying value and risk • Cube by – Social status – Geographical location – Net value, etc. • Balance return on the loan with risk of default • How to evaluate the portfolio as a whole? – Least risk loan may be to the very wealthy, but there are a very limited number – Many poor customers, but greater risk PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 1907/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE Example: Portfolio Analysis • Bank accounts are of varying value and risk • Cube by – Social status – Geographical location – Net value, etc. • Balance return on the loan with risk of default • How to evaluate the portfolio as a whole? – Least risk loan may be to the very wealthy, but there are a very limited number – Many poor customers, but greater risk • Solution may combine types of analyses – When to lend, interest rate charged PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 1907/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE Example: Set Analysis from MicroStrategy, Better Business Decisions Every Day: Integrating Business Reporting & Analysis PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 2007/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE Example: Set Analysis from MicroStrategy, Better Business Decisions Every Day: Integrating Business Reporting & Analysis PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 2107/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE Example: Set Analysis from MicroStrategy, Better Business Decisions Every Day: Integrating Business Reporting & Analysis PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 2107/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE Example: Set Analysis from MicroStrategy, Better Business Decisions Every Day: Integrating Business Reporting & Analysis PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 2107/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE Example: Set Analysis from MicroStrategy, Better Business Decisions Every Day: Integrating Business Reporting & Analysis PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 2107/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE Example: Set Analysis from MicroStrategy, Better Business Decisions Every Day: Integrating Business Reporting & Analysis PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 2107/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE Example: Set Analysis from MicroStrategy, Better Business Decisions Every Day: Integrating Business Reporting & Analysis PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 2107/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • 15 years ago, CarMax started as a way to make the car buying experience simple, fair, and fun. Today CarMax is a FORTUNE 500 retailer and one of FORTUNE’s “100 BestCompanies to Work For.” And we are hiring talented individuals who are interested in:--solving original, wide-ranging, and open-ended business problems TITLE--not only discovering new insights, but successfully implementing them--making a significant mark on a growing company--developing the fundamental skills for a rewarding business career CarMax Example Job PostingIf that sounds like you, the Strategy Analyst position is the unique opportunity you’ve been looking for. The strategy team at CarMax currently consists of over 40 analysts, many ofwhom are recent college graduates from top schools with a variety of academic backgrounds (computer science, economics, English, engineering, journalism, math, politicalscience). These analysts lead advances and decisions in several key business areas:-Inventory and pricing—what is the optimal selection of inventory, how do we acquire it, whatshould we pay for it, what should we price it for?-Expansion planning—which markets should we enter and how do we store those markets? Will each $10-30 million store investment generate a sufficient economic return?-Credit strategy—how can our bank (CarMax Auto Finance) approve more customers for loans and convert more approvals to sales?-Marketing and consumer insight—how do we reach our customers, increase traffic to our stores, and best use the internet to drive sales and build our brand-Industry and competitive research—what middle- and long-term risks are we exposed to, and how best do we prepare to respond?-Production—how do we increase vehicle reconditioning quality while reducing cost and production time?-Sales process and workforce—what is the best way to serve customers in our stores, and how do we manage, motivate and compensate our sales team?Even early in your career at CarMax, you will have the responsibility to own an area of the business and will be expected to improve it. For example, one undergraduate recruitused data analysis to reformulate our retail pricing strategy, pitched and sold his idea to the senior executive team, and implemented a new system nationwide in his first 6 monthswith the company. That is the kind of impact you can make at CarMax. And as you do this, you will work closely with the senior executives and analytical managers to develop thefundamental and advanced skills that underpin a successful career in business. In fact, most of our managers in the strategy group started at CarMax as analysts, and our VP ofStrategy and Analysis started his career here through our undergraduate recruiting program. While an MBA is not required to advance or contribute at CarMax, analysts who havechosen to pursue a business degree have enjoyed superior acceptance rates at their first choice schools, including Harvard, Chicago, UVa, Columbia, and Duke.Your opportunities to develop, contribute, and lead as an analyst at CarMax are as great as the company’s opportunity to grow. While CarMax is already the largest used car retailerin the country (with over $8 billion in sales and over 90 superstores across the country), we have only 2% of the 1 to 6-year-old used car market, which, at $280 billion annually, isbigger than the home improvement or consumer electronics industries. CarMax is already growing at 15% a year, and over the next 10 years plans to have 250-300 stores andachieve $25+ billion in annual sales. As an analyst, you can be an integral part of that growth, all while enjoying a casual and friendly environment, a diverse group of talentedassociates, a healthy work-life balance, and excellent compensation and benefits.An ideal candidate will have--Demonstrated top caliber analytic and problem solving skills --History of achievement demonstrated by top 15% GPA, with a quantitative major(s), and/or other recognition suchas scholarships, awards, honor societies-- Passion for business and desire to develop into a strong business leaderWe encourage you to apply. For more information, please visit us at the career fair, on our website (www.carmax.com/collegerecruiting), or email us atcollege_recruiting@carmax.com.http://www.seas.virginia.edu/careerdevelopment/index.php?option=com_careerfairstudent&task=detailView&employerId=216&eventId=3 PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 22 - datablueprint.com 8/2/2010 © Copyright this and previous years by Data Blueprint - all rights reserved!07/10/12 24 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • 15 years ago, CarMax started as a way to make the car buying experience simple, fair, and fun. Today CarMax is a FORTUNE 500 retailer and one of FORTUNE’s “100 BestCompanies to Work For.” And we are hiring talented individuals who are interested in:--solving original, wide-ranging, and open-ended business problems TITLE--not only discovering new insights, but successfully implementing them--making a significant mark on a growing company--developing the fundamental skills for a rewarding business career CarMax Example Job PostingIf that sounds like you, the Strategy Analyst position is the unique opportunity you’ve been looking for. The strategy team at CarMax currently consists of over 40 analysts, many ofwhom are recent college graduates from top schools with a variety of academic backgrounds (computer science, economics, English, engineering, journalism, math, politicalscience). These analysts lead advances and decisions in several key business areas:-Inventory and pricing—what is the optimal selection of inventory, how do we acquire it, whatshould we pay for it, what should we price it for? --solving original, wide-ranging, and open-ended business problems-Expansion planning—which markets should we enter and how do we store those markets? Will each $10-30 million store investment generate a sufficient economic return?-Credit strategy—how can our bank (CarMax Auto Finance) approve more customers for loans and convert more approvals to sales? --not only discovering new insights, but successfully implementing them-Marketing and consumer insight—how do we reach our customers, increase traffic to our stores, and best use the internet to drive sales and build our brand-Industry and competitive research—what middle- and long-term risks are we exposed to, and how best do we prepare to respond? --making a significant mark on a growing company-Production—how do we increase vehicle reconditioning quality while reducing cost and production time?-Sales process and workforce—what is the best way to serve customers in our stores, and how do we manage, motivate and compensate our sales team?Even early in your career --developing the fundamental skills for a rewarding business career at CarMax, you will have the responsibility to own an area of the business and will be expected to improve it. For example, one undergraduate recruitused data analysis to reformulate our retail pricing strategy, pitched and sold his idea to the senior executive team, and implemented a new system nationwide in his first 6 monthswith the company. That is the kind of impact you can make at CarMax. And as you do this, you will work closely with the senior executives and analytical managers to develop thefundamental and advanced skills that underpin a successful career in business. In fact, most of our managers in the strategy group started at CarMax as analysts, and our VP ofStrategy and Analysis started his career here through our undergraduate recruiting program. While an MBA is not required to advance or contribute at CarMax, analysts who havechosen to pursue a business degree have enjoyed superior acceptance rates at their first choice schools, including Harvard, Chicago, UVa, Columbia, and Duke.Your opportunities to develop, contribute, and lead as an analyst at CarMax are as great as the company’s opportunity to grow. While CarMax is already the largest used car retailerin the country (with over $8 billion in sales and over 90 superstores across the country), we have only 2% of the 1 to 6-year-old used car market, which, at $280 billion annually, isbigger than the home improvement or consumer electronics industries. CarMax is already growing at 15% a year, and over the next 10 years plans to have 250-300 stores andachieve $25+ billion in annual sales. As an analyst, you can be an integral part of that growth, all while enjoying a casual and friendly environment, a diverse group of talentedassociates, a healthy work-life balance, and excellent compensation and benefits.An ideal candidate will have--Demonstrated top caliber analytic and problem solving skills --History of achievement demonstrated by top 15% GPA, with a quantitative major(s), and/or other recognition suchas scholarships, awards, honor societies-- Passion for business and desire to develop into a strong business leaderWe encourage you to apply. For more information, please visit us at the career fair, on our website (www.carmax.com/collegerecruiting), or email us atcollege_recruiting@carmax.com.http://www.seas.virginia.edu/careerdevelopment/index.php?option=com_careerfairstudent&task=detailView&employerId=216&eventId=3 PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 22 - datablueprint.com 8/2/2010 © Copyright this and previous years by Data Blueprint - all rights reserved!07/10/12 24 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • 15 years ago, CarMax started as a way to make the car buying experience simple, fair, and fun. Today CarMax is a FORTUNE 500 retailer and one of FORTUNE’s “100 BestCompanies to Work For.” And we are hiring talented individuals who are interested in:--solving original, wide-ranging, and open-ended business problems TITLE--not only discovering new insights, but successfully implementing them--making a significant mark on a growing company--developing the fundamental skills for a rewarding business career CarMax Example Job PostingIf that sounds like you, the Strategy Analyst position is the unique opportunity you’ve been looking for. The strategy team at CarMax currently consists of over 40 analysts, many ofwhom are recent college graduates from top schools with a variety of academic backgrounds (computer science, economics, English, engineering, journalism, math, politicalscience). These analysts lead advances and decisions in several key business areas:-Inventory and pricing—what is the optimal selection of inventory, how do we acquire it, whatshould we pay for it, what should we price it for? --solving original, wide-ranging, and open-ended business problems-Expansion planning—which markets should we enter and how do we store those markets? Will each $10-30 million store investment generate a sufficient economic return?-Credit strategy—how can our bank (CarMax Auto Finance) approve more customers for loans and convert more approvals to sales? --not only discovering new insights, but successfully implementing them-Marketing and consumer insight—how do we reach our customers, increase traffic to our stores, and best use the internet to drive sales and build our brand-Industry and competitive research—what middle- and long-term risks are we exposed to, and how best do we prepare to respond? --making a significant mark on a growing company-Production—how do we increase vehicle reconditioning quality while reducing cost and production time?-Sales process and workforce—what is the best way to serve customers in our stores, and how do we manage, motivate and compensate our sales team?Even early in your career --developing the fundamental skills for a rewarding business career at CarMax, you will have the responsibility to own an area of the business and will be expected to improve it. For example, one undergraduate recruitused data analysis to reformulate our retail pricing strategy, pitched and sold his idea to the senior executive team, and implemented a new system nationwide in his first 6 monthswith the company. That is the kind of impact you can make at CarMax. And as you do this, you will work closely with the senior executives and analytical managers to develop thefundamental and advanced skills that underpin a successful career in business. In fact, most of our managers in the strategy group started at CarMax as analysts, and our VP ofStrategy and Analysis started his career here through our undergraduate recruiting program. While an MBA is not required to advance or contribute at CarMax, analysts who havechosen to pursue a business degree have enjoyed superior acceptance rates at their first choice schools, including Harvard, Chicago, UVa, Columbia, and Duke. own an area of the business and will be expected to improve itYour opportunities to develop, contribute, and lead as an analyst at CarMax are as great as the company’s opportunity to grow. While CarMax is already the largest used car retailerin the country (with over $8 billion in sales and over 90 superstores across the country), we have only 2% of the 1 to 6-year-old used car market, which, at $280 billion annually, isbigger than the home improvement or consumer electronics industries. CarMax is already growing at 15% a year, and over the next 10 years plans to have 250-300 stores andachieve $25+ billion in annual sales. As an analyst, you can be an integral part of that growth, all while enjoying a casual and friendly environment, a diverse group of talentedassociates, a healthy work-life balance, and excellent compensation and benefits.An ideal candidate will have--Demonstrated top caliber analytic and problem solving skills --History of achievement demonstrated by top 15% GPA, with a quantitative major(s), and/or other recognition suchas scholarships, awards, honor societies-- Passion for business and desire to develop into a strong business leaderWe encourage you to apply. For more information, please visit us at the career fair, on our website (www.carmax.com/collegerecruiting), or email us atcollege_recruiting@carmax.com.http://www.seas.virginia.edu/careerdevelopment/index.php?option=com_careerfairstudent&task=detailView&employerId=216&eventId=3 PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 22 - datablueprint.com 8/2/2010 © Copyright this and previous years by Data Blueprint - all rights reserved!07/10/12 24 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • Operations Research TITLE PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 2307/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • Operations Research TITLE • Interdisciplinary branch of applied mathematics and formal science PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 2307/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • Operations Research TITLE • Interdisciplinary branch of applied mathematics and formal science • Uses methods such as mathematical modeling, statistics, and algorithms to arrive at optimal or near optimal solutions PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 2307/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • Operations Research TITLE • Interdisciplinary branch of applied mathematics and formal science • Uses methods such as mathematical modeling, statistics, and algorithms to arrive at optimal or near optimal solutions • Typically concerned with optimizing the maxima (profit, assembly line performance, crop yield, bandwidth, etc) or minima (loss, risk, etc.) of some objective function PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 2307/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • Operations Research TITLE • Interdisciplinary branch of applied mathematics and formal science • Uses methods such as mathematical modeling, statistics, and algorithms to arrive at optimal or near optimal solutions • Typically concerned with optimizing the maxima (profit, assembly line performance, crop yield, bandwidth, etc) or minima (loss, risk, etc.) of some objective function • Operations research helps management achieve its goals using scientific methods http://en.wikipedia.org/wiki/Operations_research PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 2307/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE Indiana Jones: Raiders Of The Lost Ark PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 2407/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE Outline 1. Data management overview 2. What are DW, analytics, BI and meta- integration technologies and why are they important? 3. Top 10 causes of data warehouse failures 4. DW & architecture focus 5. Business intelligence focus 6. The use of meta models 7. DW, analytics & BI building blocks 8. Guiding principles & best practices Tweeting now: 9. Take aways, references and Q&A #dataed PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 2507/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE Outline 1. Data management overview 2. What are DW, analytics, BI and meta- integration technologies and why are they important? 3. Top 10 causes of data warehouse failures 4. DW & architecture focus 5. Business intelligence focus 6. The use of meta models 7. DW, analytics & BI building blocks 8. Guiding principles & best practices Tweeting now: 9. Take aways, references and Q&A #dataed PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 2507/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE Top Causes of Data Warehouse Failure from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 2607/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE Top Causes of Data Warehouse Failure • Poor Quality Data – Many more values of gender code than (M/F) from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 2607/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE Top Causes of Data Warehouse Failure • Poor Quality Data – Many more values of gender code than (M/F) • Incorrectly Structured Data – Providing the correct answer to the wrong question from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 2607/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE Top Causes of Data Warehouse Failure • Poor Quality Data – Many more values of gender code than (M/F) • Incorrectly Structured Data – Providing the correct answer to the wrong question • Bad Warehouse Design – Overly complex from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 2607/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE Polling Question #1 What is the #1 reason why Data Warehouses Fail? 1. Functions and capabilities not implemented 2. The project is over budget 3. Inability to expand 4. Too complicated for users PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 2707/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE Top 10 Data Warehouse Failures from The Data Administration Newsletter, www.dtdan.com PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 2807/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE Top 10 Data Warehouse Failures 1. The project is over budget from The Data Administration Newsletter, www.dtdan.com PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 2807/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE Top 10 Data Warehouse Failures 1. The project is over budget 2. Slipped schedule from The Data Administration Newsletter, www.dtdan.com PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 2807/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE Top 10 Data Warehouse Failures 1. The project is over budget 2. Slipped schedule 3. Functions and capabilities not implemented from The Data Administration Newsletter, www.dtdan.com PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 2807/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE Top 10 Data Warehouse Failures 1. The project is over budget 2. Slipped schedule 3. Functions and capabilities not implemented 4. Unhappy users from The Data Administration Newsletter, www.dtdan.com PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 2807/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE Top 10 Data Warehouse Failures 1. The project is over budget 2. Slipped schedule 3. Functions and capabilities not implemented 4. Unhappy users 5. Unacceptable performance from The Data Administration Newsletter, www.dtdan.com PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 2807/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE Top 10 Data Warehouse Failures 1. The project is over budget 2. Slipped schedule 3. Functions and capabilities not implemented 4. Unhappy users 5. Unacceptable performance 6. Poor availability from The Data Administration Newsletter, www.dtdan.com PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 2807/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE Top 10 Data Warehouse Failures 1. The project is over budget 2. Slipped schedule 3. Functions and capabilities not implemented 4. Unhappy users 5. Unacceptable performance 6. Poor availability 7. Inability to expand from The Data Administration Newsletter, www.dtdan.com PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 2807/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE Top 10 Data Warehouse Failures 1. The project is over budget 2. Slipped schedule 3. Functions and capabilities not implemented 4. Unhappy users 5. Unacceptable performance 6. Poor availability 7. Inability to expand 8. Poor quality data/reports from The Data Administration Newsletter, www.dtdan.com PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 2807/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE Top 10 Data Warehouse Failures 1. The project is over budget 2. Slipped schedule 3. Functions and capabilities not implemented 4. Unhappy users 5. Unacceptable performance 6. Poor availability 7. Inability to expand 8. Poor quality data/reports 9. Too complicated for users from The Data Administration Newsletter, www.dtdan.com PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 2807/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE Top 10 Data Warehouse Failures 1. The project is over budget 2. Slipped schedule 3. Functions and capabilities not implemented 4. Unhappy users 5. Unacceptable performance 6. Poor availability 7. Inability to expand 8. Poor quality data/reports 9. Too complicated for users 10. Project not cost justified from The Data Administration Newsletter, www.dtdan.com PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 2807/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE Outline 1. Data management overview 2. What are DW, analytics, BI and meta- integration technologies and why are they important? 3. Top 10 causes of data warehouse failures 4. DW & architecture focus 5. Business intelligence focus 6. The use of meta models 7. DW, analytics & BI building blocks 8. Guiding principles & best practices Tweeting now: 9. Take aways, references and Q&A #dataed PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 2907/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE Outline 1. Data management overview 2. What are DW, analytics, BI and meta- integration technologies and why are they important? 3. Top 10 causes of data warehouse failures 4. DW & architecture focus 5. Business intelligence focus 6. The use of meta models 7. DW, analytics & BI building blocks 8. Guiding principles & best practices Tweeting now: 9. Take aways, references and Q&A #dataed PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 2907/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE Health Care Provider Data Warehouse PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 3007/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE Health Care Provider Data Warehouse • 1.8 million members PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 3007/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE Health Care Provider Data Warehouse • 1.8 million members • 1.4 million providers PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 3007/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE Health Care Provider Data Warehouse • 1.8 million members • 1.4 million providers • 800,000 providers no key PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 3007/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE Health Care Provider Data Warehouse • 1.8 million members • 1.4 million providers • 800,000 providers no key • 2.2% prov_number = 9 digits (required) PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 3007/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE Health Care Provider Data Warehouse • 1.8 million members • 1.4 million providers • 800,000 providers no key • 2.2% prov_number = 9 digits (required) • 29% prov_ssn ≠ 9 digits PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 3007/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE Health Care Provider Data Warehouse • 1.8 million members • 1.4 million providers • 800,000 providers no key • 2.2% prov_number = 9 digits (required) • 29% prov_ssn ≠ 9 digits • 1 User PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 3007/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE Health Care Provider Data Warehouse • 1.8 million members • 1.4 million providers • 800,000 providers no key • 2.2% prov_number = 9 digits (required) • 29% prov_ssn ≠ 9 digits • 1 User "I can take a roomful of MBAs and accomplish this analysis faster!" CLASSIFICATION DATE SLIDE PRODUCED BY DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 3007/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • Basic Data Warehouse Analysis TITLE from MicroStrategy, Better Business Decisions Every Day: Integrating Business Reporting & Analysis PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 3107/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • Basic Data Warehouse Analysis TITLE • Emphasis on the cube from MicroStrategy, Better Business Decisions Every Day: Integrating Business Reporting & Analysis PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 3107/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • Basic Data Warehouse Analysis TITLE • Emphasis on the cube • Permits different users to "slice and dice" subsets of data from MicroStrategy, Better Business Decisions Every Day: Integrating Business Reporting & Analysis PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 3107/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • Basic Data Warehouse Analysis TITLE • Emphasis on the cube • Permits different users to "slice and dice" subsets of data • Viewing from different perspectives from MicroStrategy, Better Business Decisions Every Day: Integrating Business Reporting & Analysis PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 3107/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • Warehouse Analysis TITLE from MicroStrategy, Better Business Decisions Every Day: Integrating Business Reporting & Analysis PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 3207/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • Warehouse Analysis TITLE • Users can "drill" anywhere from MicroStrategy, Better Business Decisions Every Day: Integrating Business Reporting & Analysis PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 3207/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • Warehouse Analysis TITLE • Users can "drill" anywhere • Entire collection is accessible from MicroStrategy, Better Business Decisions Every Day: Integrating Business Reporting & Analysis PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 3207/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • Warehouse Analysis TITLE • Users can "drill" anywhere • Entire collection is accessible • Summaries to transaction-level detail from MicroStrategy, Better Business Decisions Every Day: Integrating Business Reporting & Analysis PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 3207/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE Corporate Information Factory Architecture from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 3307/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE Oracle PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 3407/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE Corporate Information Factory Architecture from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 3507/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International TITLE Corporate Information Factory Architecture PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 3607/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International TITLE Corporate Information Factory Architecture PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 3707/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International TITLE Kimballs DW Chess Pieces PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 3807/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE Multiple Sources of (for example) Customer Data R& D Applications (researcher supported, no documentation) PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 3907/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE Multiple Sources of (for example) Customer Data Finance Application (3rd GL, batch Payroll Data system, no source) (database) Payroll Application Finance (3rd GL) Data (indexed) Marketing Data Marketing Application (external database) (4rd GL, query facilities, no reporting, very large) Personnel Data (database) Personnel App. (20 years old, un-normalized data) Mfg. Data R&D Data (home grown (raw) database) Mfg. Applications (contractor supported) R& D Applications (researcher supported, no documentation) PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 3907/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE Multiple Sources of (for example) Customer Data Finance Application (3rd GL, batch Payroll Data system, no source) (database) Payroll Application Finance (3rd GL) Data (indexed) Marketing Data Marketing Application (external database) (4rd GL, query facilities, no reporting, very large) Personnel Data (database) Personnel App. (20 years old, un-normalized data) Mfg. Data R&D Data (home grown (raw) database) Mfg. Applications (contractor supported) R& D Applications (researcher supported, no documentation) PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 3907/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE Multiple Sources of (for example) Customer Data Finance Application (3rd GL, batch Payroll Data system, no source) (database) Payroll Application Finance (3rd GL) Data (indexed) Marketing Data Marketing Application (external database) (4rd GL, query facilities, no reporting, very large) Personnel Data (database) Personnel App. (20 years old, un-normalized data) Mfg. Data R&D Data (home grown (raw) database) Mfg. Applications (contractor supported) R& D Applications (researcher supported, no documentation) PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 3907/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE Outline 1. Data management overview 2. What are DW, analytics, BI and meta- integration technologies and why are they important? 3. Top 10 causes of data warehouse failures 4. DW & architecture focus 5. Business intelligence focus 6. The use of meta models 7. DW, analytics & BI building blocks 8. Guiding principles & best practices Tweeting now: 9. Take aways, references and Q&A #dataed PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 4007/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE Outline 1. Data management overview 2. What are DW, analytics, BI and meta- integration technologies and why are they important? 3. Top 10 causes of data warehouse failures 4. DW & architecture focus 5. Business intelligence focus 6. The use of meta models 7. DW, analytics & BI building blocks 8. Guiding principles & best practices Tweeting now: 9. Take aways, references and Q&A #dataed PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 4007/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE Styles of Business Intelligence from MicroStrategy, Better Business Decisions Every Day: Integrating Business Reporting & Analysis PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 4107/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE Business Intelligence Features PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 4207/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE Business Intelligence Features Problematic Data Quality PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 4207/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE Polling Question #2 Which is a key business intelligence trend? 1. Market Consolidation Means a lot more Choices for Business Intelligence Users. 2. Theres so much data, and too much insight. 3. The Convergence of Structured and Unstructured Data Will Create Better Business Intelligence. 4. Applications Will Not Provide New Views of Business Intelligence Data. PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 4307/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • 5 Key Business Intelligence Trends TITLE 1. Theres so much data, but too little insight. More data translates to a greater need to manage it and make it actionable. 2. Market Consolidation Means Fewer Choices for Business Intelligence Users. 3. Business Intelligence Expands from the Board Room to the Front Lines. Increasingly, business intelligence tools will be available at all levels of the corporation 4. The Convergence of Structured and Unstructured Data Will Create Better Business Intelligence. 5. Applications Will Provide New Views of Business Intelligence Data. The next generation of business intelligence applications is moving beyond the pie charts and bar charts into more visual depictions of data and trends. http://www.cio.com/article/150450/ Five_Key_Business_Intelligence_Trends_You_Need_to_Know? page=2&taxonomyId=3002 PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 4407/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE Outline 1. Data management overview 2. What are DW, analytics, BI and meta- integration technologies and why are they important? 3. Top 10 causes of data warehouse failures 4. DW & architecture focus 5. Business intelligence focus 6. The use of meta models 7. DW, analytics & BI building blocks 8. Guiding principles & best practices Tweeting now: 9. Take aways, references and Q&A #dataed PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 4507/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE Outline 1. Data management overview 2. What are DW, analytics, BI and meta- integration technologies and why are they important? 3. Top 10 causes of data warehouse failures 4. DW & architecture focus 5. Business intelligence focus 6. The use of meta models 7. DW, analytics & BI building blocks 8. Guiding principles & best practices Tweeting now: 9. Take aways, references and Q&A #dataed PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 4507/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE Meta Data Models Source:http://dmreview.com/article_sub.cfm?articleID=1000941 used with permission PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 4607/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • Overview of CWM Metamodel TITLE http://www.omg.org/technology/documents/modeling_spec_catalog.htm PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 4707/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • Overview of CWM Metamodel TITLE Warehouse Warehouse Warehouse Management Process Operation Analysis Data Information Business Transformation OLAP Mining Visualization Nomenclature Resources Object- Record- Multi Oriented Relational XML Oriented Dimensional (ObjectModel) Foundation Business Data Keys Type Software Expressions Index Mapping Deployment Information Types ObjectModel (Core, Behavioral, Relationships, Instance) http://www.omg.org/technology/documents/modeling_spec_catalog.htm PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 4707/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE Outline 1. Data management overview 2. What are DW, analytics, BI and meta- integration technologies and why are they important? 3. Top 10 causes of data warehouse failures 4. DW & architecture focus 5. Business intelligence focus 6. The use of meta models 7. DW, analytics & BI building blocks 8. Guiding principles & best practices Tweeting now: 9. Take aways, references and Q&A #dataed PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 4807/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE Outline 1. Data management overview 2. What are DW, analytics, BI and meta- integration technologies and why are they important? 3. Top 10 causes of data warehouse failures 4. DW & architecture focus 5. Business intelligence focus 6. The use of meta models 7. DW, analytics & BI building blocks 8. Guiding principles & best practices Tweeting now: 9. Take aways, references and Q&A #dataed PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 4807/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE Data Warehousing, Analytics, BI, Meta-Integration Technologies                                           from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 4907/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE Goals and Principles from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 5007/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE Goals and Principles 1. To support and enable effective business analysis and decision making by knowledgeable workers from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 5007/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE Goals and Principles 1. To support and enable effective business analysis and decision making by knowledgeable workers 2. To build and maintain the environment/infrastructure to support business intelligence activities, specifically leveraging all the other data management functions to cost effectively deliver consistent integrated data for all BI activities from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 5007/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE Activities from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 5107/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE Activities • Understand BI information needs from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 5107/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE Activities • Understand BI information needs • Define and maintain the DW/BI architecture from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 5107/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE Activities • Understand BI information needs • Define and maintain the DW/BI architecture • Process data for BI from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 5107/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE Activities • Understand BI information needs • Define and maintain the DW/BI architecture • Process data for BI • Implement data warehouse/data marts from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 5107/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE Activities • Understand BI information needs • Define and maintain the DW/BI architecture • Process data for BI • Implement data warehouse/data marts • Implement BI tools and user interfaces from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 5107/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE Activities • Understand BI information needs • Define and maintain the DW/BI architecture • Process data for BI • Implement data warehouse/data marts • Implement BI tools and user interfaces • Monitor and tune DW processes from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 5107/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE Activities • Understand BI information needs • Define and maintain the DW/BI architecture • Process data for BI • Implement data warehouse/data marts • Implement BI tools and user interfaces • Monitor and tune DW processes • Monitor and tune BI activities and performance from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 5107/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE Primary Deliverables from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 5207/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE Primary Deliverables • DW/BI Architecture from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 5207/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE Primary Deliverables • DW/BI Architecture • Data warehouses, marts, cubes etc. from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 5207/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE Primary Deliverables • DW/BI Architecture • Data warehouses, marts, cubes etc. • Dashboards-scorecards from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 5207/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE Primary Deliverables • DW/BI Architecture • Data warehouses, marts, cubes etc. • Dashboards-scorecards • Analytic applications from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 5207/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE Primary Deliverables • DW/BI Architecture • Data warehouses, marts, cubes etc. • Dashboards-scorecards • Analytic applications • Files extracts (for data mining, etc.) from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 5207/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE Primary Deliverables • DW/BI Architecture • Data warehouses, marts, cubes etc. • Dashboards-scorecards • Analytic applications • Files extracts (for data mining, etc.) • BI tools and user environments from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 5207/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE Primary Deliverables • DW/BI Architecture • Data warehouses, marts, cubes etc. • Dashboards-scorecards • Analytic applications • Files extracts (for data mining, etc.) • BI tools and user environments • Data quality feedback mechanism/loop from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 5207/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE Roles and Responsibilities from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 5307/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE Roles and Responsibilities Suppliers: • Executives/managers • Subject Matter Experts • Data governance council • Information consumers • Data producers • Data architects/analysts from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 5307/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE Roles and Responsibilities Suppliers: • Executives/managers • Subject Matter Experts • Data governance council • Information consumers • Data producers • Data architects/analysts Participants: • Executives/managers • Data Stewards • Subject Matter Experts • Data Architects • Data Analysts • Application Architects • Data Governance Council • Data Providers • Other BI Professionals from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 5307/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE Roles and Responsibilities Suppliers: Consumers: • Executives/managers • Application Users • Subject Matter Experts • BI and Reporting • Data governance council Users • Information consumers • Application Developers and • Data producers Architects • Data architects/analysts • Data integration Participants: Developers and • Executives/managers Architects • Data Stewards • BI Vendors and • Subject Matter Experts Architects • Data Architects • Vendors, Customers • Data Analysts and Partners • Application Architects • Data Governance Council • Data Providers • Other BI Professionals from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 5307/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE Technology from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 5407/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE Technology • ETL from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 5407/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE Technology • ETL • Change Management Tools from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 5407/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE Technology • ETL • Change Management Tools • Data Modeling Tools from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 5407/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE Technology • ETL • Change Management Tools • Data Modeling Tools • Data Profiling Tools from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 5407/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE Technology • ETL • Change Management Tools • Data Modeling Tools • Data Profiling Tools • Data Cleansing Tools from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 5407/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE Technology • ETL • Change Management Tools • Data Modeling Tools • Data Profiling Tools • Data Cleansing Tools • Data Integration Tools from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 5407/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE Technology • ETL • Change Management Tools • Data Modeling Tools • Data Profiling Tools • Data Cleansing Tools • Data Integration Tools • Reference Data Management Applications from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 5407/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE Technology • ETL • Change Management Tools • Data Modeling Tools • Data Profiling Tools • Data Cleansing Tools • Data Integration Tools • Reference Data Management Applications • Master Data Management Applications from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 5407/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE Technology • ETL • Change Management Tools • Data Modeling Tools • Data Profiling Tools • Data Cleansing Tools • Data Integration Tools • Reference Data Management Applications • Master Data Management Applications • Process Modeling Tools from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 5407/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE Technology • ETL • Change Management Tools • Data Modeling Tools • Data Profiling Tools • Data Cleansing Tools • Data Integration Tools • Reference Data Management Applications • Master Data Management Applications • Process Modeling Tools • Meta-data Repositories from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 5407/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE Technology • ETL • Change Management Tools • Data Modeling Tools • Data Profiling Tools • Data Cleansing Tools • Data Integration Tools • Reference Data Management Applications • Master Data Management Applications • Process Modeling Tools • Meta-data Repositories • Business Process and Rule Engines from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 5407/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE Outline 1. Data management overview 2. What are DW, analytics, BI and meta- integration technologies and why are they important? 3. Top 10 causes of data warehouse failures 4. DW & architecture focus 5. Business intelligence focus 6. The use of meta models 7. DW, analytics & BI building blocks 8. Guiding principles & best practices Tweeting now: 9. Take aways, references and Q&A #dataed PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 5507/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE Outline 1. Data management overview 2. What are DW, analytics, BI and meta- integration technologies and why are they important? 3. Top 10 causes of data warehouse failures 4. DW & architecture focus 5. Business intelligence focus 6. The use of meta models 7. DW, analytics & BI building blocks 8. Guiding principles & best practices Tweeting now: 9. Take aways, references and Q&A #dataed PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 5507/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE Guiding Principles from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 5607/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE Guiding Principles 1. Obtain executive commitment and support. from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 5607/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE Guiding Principles 1. Obtain executive commitment and support. 2. Secure business SMEs. from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 5607/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE Guiding Principles 1. Obtain executive commitment and support. 2. Secure business SMEs. 3. Be business focused and driven. Let the business drive the prioritization. from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 5607/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE Guiding Principles 1. Obtain executive commitment and support. 2. Secure business SMEs. 3. Be business focused and driven. Let the business drive the prioritization. 4. Demonstrate data quality is essential. from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 5607/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE Guiding Principles 1. Obtain executive commitment and support. 2. Secure business SMEs. 3. Be business focused and driven. Let the business drive the prioritization. 4. Demonstrate data quality is essential. 5. Provide incremental value. from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 5607/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE Guiding Principles 1. Obtain executive commitment and support. 2. Secure business SMEs. 3. Be business focused and driven. Let the business drive the prioritization. 4. Demonstrate data quality is essential. 5. Provide incremental value. 6. Transparency and self service. from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 5607/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE Guiding Principles 1. Obtain executive commitment and support. 2. Secure business SMEs. 3. Be business focused and driven. Let the business drive the prioritization. 4. Demonstrate data quality is essential. 5. Provide incremental value. 6. Transparency and self service. 7. One size does not fit all: Find the right tools and products for each of your segments. from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 5607/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE Guiding Principles 1. Obtain executive commitment and support. 2. Secure business SMEs. 3. Be business focused and driven. Let the business drive the prioritization. 4. Demonstrate data quality is essential. 5. Provide incremental value. 6. Transparency and self service. 7. One size does not fit all: Find the right tools and products for each of your segments. 8. Think and architect globally, act and build locally. from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 5607/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE Guiding Principles 1. Obtain executive commitment and support. 2. Secure business SMEs. 3. Be business focused and driven. Let the business drive the prioritization. 4. Demonstrate data quality is essential. 5. Provide incremental value. 6. Transparency and self service. 7. One size does not fit all: Find the right tools and products for each of your segments. 8. Think and architect globally, act and build locally. 9. Collaborate with and integrate all other data initiatives, especially those for data governance, data quality and metadata. from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 5607/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE Guiding Principles 1. Obtain executive commitment and support. 2. Secure business SMEs. 3. Be business focused and driven. Let the business drive the prioritization. 4. Demonstrate data quality is essential. 5. Provide incremental value. 6. Transparency and self service. 7. One size does not fit all: Find the right tools and products for each of your segments. 8. Think and architect globally, act and build locally. 9. Collaborate with and integrate all other data initiatives, especially those for data governance, data quality and metadata. 10. Start with the end in mind. from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 5607/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE Guiding Principles 1. Obtain executive commitment and support. 2. Secure business SMEs. 3. Be business focused and driven. Let the business drive the prioritization. 4. Demonstrate data quality is essential. 5. Provide incremental value. 6. Transparency and self service. 7. One size does not fit all: Find the right tools and products for each of your segments. 8. Think and architect globally, act and build locally. 9. Collaborate with and integrate all other data initiatives, especially those for data governance, data quality and metadata. 10. Start with the end in mind. 11. Summarize and optimize last, not first. from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 5607/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE 6 Best Practices for Data Warehousing 1. Do some initial architecture envisioning. 2. Model the details just in time (JIT). 3. Prove the architecture early. 4. Focus on usage. 5. Organize your work by requirements. 6. Active stakeholder participation. http://www.agiledata.org/essays/ dataWarehousingBestPractices.html PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 5707/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE Outline 1. Data management overview 2. What are DW, analytics, BI and meta- integration technologies and why are they important? 3. Top 10 causes of data warehouse failures 4. DW & architecture focus 5. Business intelligence focus 6. The use of meta models 7. DW, analytics & BI building blocks 8. Guiding principles & best practices Tweeting now: 9. Take aways, references and Q&A #dataed PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 5807/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE Outline 1. Data management overview 2. What are DW, analytics, BI and meta- integration technologies and why are they important? 3. Top 10 causes of data warehouse failures 4. DW & architecture focus 5. Business intelligence focus 6. The use of meta models 7. DW, analytics & BI building blocks 8. Guiding principles & best practices Tweeting now: 9. Take aways, references and Q&A #dataed PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 5807/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE Summary: Data Warehousing & Business Intelligence Management from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 5907/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE References PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 6007/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE References PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 6107/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE Additional References • http://www.information-management.com/infodirect/20050909/1036703-1.html • http://www.agiledata.org/essays/dataWarehousingBestPractices.html • http://www.cio.com/article/150450/ Five_Key_Business_Intelligence_Trends_You_Need_to_Know? page=2&taxonomyId=3002 • http://www.computerworld.com/s/article/9228736/ Business_Intelligence_and_analytics_Conquering_Big_Data?taxonomyId=9 • http://www.enterpriseirregulars.com/5706/the-top-10-trends-for-2010-in-analytics- business-intelligence-and-performance-management/ • http://www.itbusinessedge.com/cm/blogs/vizard/taking-the-analytics-pressure-off-the- data-warehouse/?cs=50698 • http://www.informationweek.com/news/software/bi/240001922 PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 6207/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE Questions? + = It’s your turn! Use the chat feature or Twitter (#dataed) to submit your questions to Peter now. PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 6307/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • TITLE Upcoming Events August Webinar: Your Documents and Other Content: Managing Unstructured Data August 14, 2012 @ 2:00 PM – 3:30 PM ET (11:00 AM-12:30 PM PT) September Webinar: Let’s Talk Metadata: Strategies and Successes September 11, 2012 @ 2:00 PM – 3:30 PM ET (11:00 AM-12:30 PM PT) Sign up here: • www.datablueprint.com/webinar-schedule • www.Dataversity.net Brought to you by: PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 6407/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!