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

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  • 1. Welcome! TITLE Practical Applications for Data Warehousing, Analytics, BI, and Meta-Integration Technologies 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 107/06/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • 2. 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. Turns out that proper analysis at this stage makes actual technology selection far more accurate. PRODUCED  BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 207/06/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • 3. TITLE Live Twitter Feed & Facebook Updates 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 06/12/12 306/06/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • 4. 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 407/06/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • 5. 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 507/06/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • 6. Data Warehousing, Analytics, BI, Meta-Integration TechnologiesData Warehousing, Analytics, BI, Meta-Integration Technologies n/a 7/10/2012
  • 7. 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 707/06/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • 8. 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 807/06/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • 9. 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 907/06/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • 10. TITLE Data Management PRODUCED  BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 1007/06/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • 11. TITLE Data Management Manage data coherently. Data  Program   Coordina;on Share data across boundaries. Organiza;onal   Data  Integra;on Data   Data   Stewardship Development Assign responsibilities for data. Engineer data delivery systems. Data  Support   Opera;ons Maintain data availability. PRODUCED  BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 1107/06/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • 12. TITLE Data Management PRODUCED  BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 1207/06/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • 13. 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 1307/06/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • 14. 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 1407/06/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • 15. 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 1507/06/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • 16. 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 1607/06/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • 17. 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 1707/06/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • 18. 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 1807/06/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • 19. 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 1907/06/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • 20. TITLE15 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--not only discovering new insights, but successfully implementing them--making a significant mark on a growing company CarMax Example Job Posting--developing the fundamental skills for a rewarding business career --solving original, wide-ranging, and open-ended business problemsIf 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, political --not only discovering new insights, but successfully implementing themscience). 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? --making a significant mark on a growing company-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? --developing the fundamental skills for a rewarding business career-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 of own an area of the business and will be expected to improve itStrategy 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. PRODUCED  BY CLASSIFICATIONhttp://www.seas.virginia.edu/careerdevelopment/index.php?option=com_careerfairstudent&task=detailView&employerId=216&eventId=3 DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 24 - datablueprint.com07/06/12 © Copyright this and previous years by Data Blueprint - all rights reserved! 8/2/2010 © Copyright this and previous years by Data Blueprint - all rights reserved! 20
  • 21. 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 2107/06/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • 22. 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 2207/06/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • 23. 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 2307/06/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • 24. 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 2407/06/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • 25. TITLE Top 10 Data Warehouse Failure Causes 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.tdan.com PRODUCED  BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 2507/06/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • 26. 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 2607/06/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • 27. 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!" PRODUCED  BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 2707/06/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • 28. 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 2807/06/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • 29. 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 2907/06/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • 30. from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International TITLE Oracle PRODUCED  BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 3007/06/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • 31. 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 3107/06/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • 32. 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 3207/06/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • 33. 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 3307/06/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • 34. 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 3407/06/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • 35. 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 3507/06/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • 36. MetaMatrix Integration Example • EII Enterprise Information Integration – between ETL and EAI - delivers tailored views of information to users at the time that it is required36 - datablueprint.com 7/13/2012 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • 37. Linked Data Linked Data is about using the Web to connect related data that wasnt previously linked, or using the Web to lower the barriers to linking data currently linked using other methods. More specifically, Wikipedia defines Linked Data as "a term used to describe a recommended best practice for exposing, sharing, and connecting pieces of data, information, and knowledge on the Semantic Web using URIs and RDF." linkeddata.org37 - datablueprint.com 7/13/2012 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • 38. TITLE Multiple Sources of (for example) Customer Data Finance  Applica.on (3rd  GL,  batch   Payroll  Data system,  no  source) (database) Payroll  Applica.on Finance (3rd  GL) Data (indexed) Marke.ng  Data Marke.ng  Applica.on (external  database) (4rd  GL,  query  facili.es,   no  repor.ng,  very  large) Personnel  Data (database) Personnel  App. (20  years  old, un-­‐normalized  data) Mfg.  Data R  &  D Data (home  grown database) Mfg.  Applica.ons (raw) (contractor  supported) R&  D  Applica.ons (researcher  supported,  no  documenta.on) PRODUCED  BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 3807/06/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • 39. 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 3907/06/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • 40. 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 4007/06/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • 41. TITLE Business Intelligence Features Problema)c  Data  Quality PRODUCED  BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 4107/06/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • 42. hOp://www.cio.com/ar.cle/150450/Five_Key_Business_Intelligence_Trends_You_Need_to_Know?page=2&taxonomyId=3002   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. PRODUCED  BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 4207/06/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • 43. 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 4307/06/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • 44. 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 4407/06/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • 45. Overview of CWM Metamodel TITLE Warehouse Warehouse Warehouse Management Process Opera.on Analysis Data   Informa.on Business Transforma.on OLAP Mining Visualiza.on Nomenclature Resources Object-­‐ Record-­‐ Mul. Oriented Rela.onal XML (ObjectModel) Oriented Dimensional Foundation Business Data Keys Type So`ware Expressions Informa.on Types Index Mapping Deployment ObjectModel (Core,  Behavioral,  Rela.onships,  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 4507/06/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • 46. 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 4607/06/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • 47. TITLE Data Warehousing, Analytics, BI, Meta-Integration Technologies ü ü ü ü ü ü ü ü ü ü ü ü ü ü ü ü ü ü ü ü ü ü ü ü ü ü ü ü ü ü ü ü ü ü ü ü ü ü ü ü ü ü 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 4707/06/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • 48. 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 4807/06/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • 49. 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 4907/06/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • 50. 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 5007/06/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • 51. TITLE Roles and Responsibilities Suppliers: Consumers: • Executives/managers • Application Users • Subject Matter Experts • BI and Reporting • Data governance council Users • Information consumers • Application • Data producers Developers and 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 5107/06/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • 52. from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International 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 PRODUCED  BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 5207/06/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • 53. 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 5307/06/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • 54. 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 5407/06/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • 55. 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. hEp://www.agiledata.org/essays/dataWarehousingBestPrac;ces.html PRODUCED  BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 5507/06/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • 56. 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 5607/06/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • 57. 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 5707/06/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • 58. TITLE References PRODUCED  BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 5807/06/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • 59. TITLE References PRODUCED  BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 5907/06/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • 60. 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 6007/06/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • 61. 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 6107/06/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • 62. 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 6207/06/12 © Copyright this and previous years by Data Blueprint - all rights reserved!