Data-Ed: Data Systems Integration & Business Value Pt. 3: Warehousing

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Integrating data across systems has been a perpetual challenge. Unfortunately, the current technology-focused solutions have not helped IT to improve its dismal project success statistics. Data warehouses, BI implementations, and general analytical efforts achieve the same levels of success as other IT projects – approximately 1/3rd are considered successes when measured against price, schedule, or functionality objectives. The first step is determining the appropriate analysis approach to the data system integration challenge. The second step is understanding the strengths and weaknesses of various approaches. Turns out that proper analysis at this stage makes actual technology selection far more accurate. Only when these are accomplished can proper matching between problem and capabilities be achieved as the third step and true business value be delivered.

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Data-Ed: Data Systems Integration & Business Value Pt. 3: Warehousing

  1. 1. Copyright 2013 by Data Blueprint Data Systems Integration & Business Value Part 3: Warehousing Certain systems are more data focused than others. Usually their primary focus is on accomplishing integration of disparate data. In these cases, failure is most often attributable to the adoption of a single pillar (silver bullet). The three webinars in the Data Systems Integration and Business Value series are designed to illustrate that good systems development more often depends on at least three DM disciplines (pie wedges) in order to provide a solid foundation. Integrating data across systems has been a perpetual challenge. Unfortunately, the current technology-focused solutions have not helped IT to improve its dismal project success statistics. Data warehouses, BI implementations, and general analytical efforts achieve the same levels of success as other IT projects – approximately 1/3rd are considered successes when measured against price, schedule, or functionality objectives. The first step is determining the appropriate analysis approach to the data system integration challenge. The second step is understanding the strengths and weaknesses of various approaches. Turns out that proper analysis at this stage makes actual technology selection far more accurate. Only when these are accomplished can proper matching between problem and capabilities be achieved as the third step and true business value be delivered. Date: September 10, 2013 Time: 2:00 PM ET/11:00 AM PT Presenter: Peter Aiken, Ph.D. 1
  2. 2. Copyright 2013 by Data Blueprint Commonly Asked Questions 1) Will I get copies of the slides after the event? 2) Is this being recorded so I can view it afterwards? 2
  3. 3. Copyright 2013 by Data Blueprint Get Social With Us! Live Twitter Feed Join the conversation! Follow us: @datablueprint @paiken Ask questions and submit your comments: #dataed Like Us on Facebook www.facebook.com/datablueprint Post questions and comments Find industry news, insightful content and event updates. Join the Group Data Management & Business Intelligence Ask questions, gain insights and collaborate with fellow data management professionals 3
  4. 4. Copyright 2013 by Data Blueprint 4 Peter Aiken, PhD • 25+ years of experience in data management • Multiple international awards & recognition • Founder, Data Blueprint (datablueprint.com) • Associate Professor of IS, VCU (vcu.edu) • President, DAMA International (dama.org) • 8 books and dozens of articles • Experienced w/ 500+ data management practices in 20 countries • Multi-year immersions with organizations as diverse as the US DoD, Nokia, Deutsche Bank, Wells Fargo, and the Commonwealth of Virginia 2
  5. 5. Data Systems Integration & Business Value Part 3: Warehousing Presented by Peter Aiken, Ph.D.
  6. 6. Copyright 2013 by Data Blueprint 1. Data management overview 2. Motivation for warehousing integration technologies (reporting->BI->Analytics) 3. What are warehousing integration technologies? 4. Warehousing and architecture focus 5. The use of meta models 6. Guiding principles & best practices 7. Take aways, references and Q&A Tweeting now: #dataed Data Systems Integration & BV Part 3: Warehousing 6 1. Data management overview 2. Motivation for warehousing integration technologies (reporting->BI->Analytics) 3. What are warehousing integration technologies? 4. Warehousing and architecture focus 5. The use of meta models 6. Guiding principles & best practices 7. Take aways, references and Q&A
  7. 7. Copyright 2013 by Data Blueprint 1. Data management overview 2. Motivation for warehousing integration technologies (reporting->BI->Analytics) 3. What are warehousing integration technologies? 4. Warehousing and architecture focus 5. The use of meta models 6. Guiding principles & best practices 7. Take aways, references and Q&A Tweeting now: #dataed Data Systems Integration & BV Part 3: Warehousing 7
  8. 8. Data Program Coordination Feedback Data Development Copyright 2013 by Data Blueprint Standard Data Five Integrated DM Practice Areas Organizational Strategies Goals Business Data Business Value Application Models & Designs Implementation Direction Guidance 8 Organizational Data Integration Data Stewardship Data Support Operations Data Asset Use Integrated Models Leverage data in organizational activities Data management processes and infrastructure Combining multiple assets to produce extra value Organizational-entity subject area data integration Provide reliable data access Achieve sharing of data within a business area
  9. 9. Copyright 2013 by Data Blueprint Five Integrated DM Practice Areas Manage data coherently. Share data across boundaries. Assign responsibilities for data. Engineer data delivery systems. Maintain data availability. Data Program Coordination Organizational Data Integration Data Stewardship Data Development Data Support Operations 9
  10. 10. Copyright 2013 by Data Blueprint Hierarchy of Data Management Practices (after Maslow) • 5 Data management practices areas / data management basics ... • ... are necessary but insufficient prerequisites to organizational data leveraging applications that is self actualizing data or advanced data practices Basic Data Management Practices – Data Program Management – Organizational Data Integration – Data Stewardship – Data Development – Data Support Operations http://3.bp.blogspot.com/-ptl-9mAieuQ/T-idBt1YFmI/AAAAAAAABgw/Ib-nVkMmMEQ/s1600/maslows_hierarchy_of_needs.png Advanced Data Practices • MDM • Mining • Big Data • Analytics • Warehousing • SOA W arehousing
  11. 11. • Data Management Body of Knowledge (DMBOK) – Published by DAMA International, the professional association for Data Managers (40 chapters worldwide) – Organized around primary data management functions focused around data delivery to the organization and several environmental elements • Certified Data Management Professional (CDMP) – Series of 3 exams by DAMA International and ICCP – Membership in a distinct group of fellow professionals – Recognition for specialized knowledge in a choice of 17 specialty areas – For more information, please visit: • www.dama.org, www.iccp.org Copyright 2013 by Data Blueprint DAMA DM BoK & CDMP 11
  12. 12. Copyright 2013 by Data Blueprint Series Context • Certain systems are more data focused than others. Usually their primary focus is on accomplishing integration of disparate data. In these cases, failure is most often attributable to the adoption of a single technological pillar (silver bullet). The three webinars in the Data Systems Integration and Business Value series are designed to illustrate that good systems development more often depends on at least three DM disciplines (pie wedges) in order to provide a solid foundation. • Data Systems Integration & Business Value – Pt. 1: Metadata Practices – Pt. 2: Cloud-based Integration – Pt. 3: Warehousing, et al. 12
  13. 13. Uses Copyright 2013 by Data Blueprint Part 1: Metadata Take Aways • Metadata unlocks the value of data, and therefore requires management attention [Gartner 2011] • Metadata is the language of data governance • Metadata defines the essence of integration challenges Sources Metadata Governance Metadata Engineering Metadata Delivery Metadata Practices Metadata Storage 13 Specialized Team Skills
  14. 14. Copyright 2013 by Data Blueprint Part 2: Take Aways • Data governance, architecture, quality, development maturity are necessary but insufficient prerequisites to successful data cloud implementation • A variety of cloud options will influence cloud and data architectures in general – You must understand your architecture and strategy in order to evaluate the options • Data must be reengineered to be – Less – Better quality – More shareable – for the cloud • Failure to do these will result in more business value for the cloud vendors/ service providers and less for your organization
  15. 15. Copyright 2013 by Data Blueprint Summary: Data Warehousing & Business Intelligence Management 15
  16. 16. Copyright 2013 by Data Blueprint 1. Data management overview 2. Motivation for warehousing integration technologies (reporting->BI->Analytics) 3. What are warehousing integration technologies? 4. Warehousing and architecture focus 5. The use of meta models 6. Guiding principles & best practices 7. Take aways, references and Q&A Tweeting now: #dataed Data Systems Integration & BV Part 3: Warehousing 16
  17. 17. Copyright 2013 by Data Blueprint 1. Data management overview 2. Motivation for warehousing integration technologies (reporting->BI->Analytics) 3. What are warehousing integration technologies? 4. Warehousing and architecture focus 5. The use of meta models 6. Guiding principles & best practices 7. Take aways, references and Q&A Tweeting now: #dataed Data Systems Integration & BV Part 3: Warehousing 17
  18. 18. Copyright 2013 by Data Blueprint • 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 18 • 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 Example: Portfolio Analysis
  19. 19. Copyright 2013 by Data Blueprint Target Isn't Just Predicting Pregnancies 19 http://rmportal.performedia.com/node/1373
  20. 20. Copyright 2013 by Data Blueprint 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 Best Companies 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 --developing the fundamental skills for a rewarding business career If 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 of whom are recent college graduates from top schools with a variety of academic backgrounds (computer science, economics, English, engineering, journalism, math, political science). 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, what should 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 recruit used 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 months with 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 the fundamental 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 Strategy 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 have chosen 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 retailer in 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, is bigger 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 and achieve $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 talented associates, 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 such as scholarships, awards, honor societies -- Passion for business and desire to develop into a strong business leader We encourage you to apply. For more information, please visit us at the career fair, on our website (www.carmax.com/collegerecruiting), or email us at college_recruiting@carmax.com. http://www.seas.virginia.edu/careerdevelopment/index.php?option=com_careerfairstudent&task=detailView&employerId=216&eventId=3 - datablueprint.com CarMax Example Job Posting 24 own an area of the business and will be expected to improve it --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 --developing the fundamental skills for a rewarding business career
  21. 21. Copyright 2013 by Data Blueprint 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 21 From The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International Data Warehousing • Operational extract, cleansing, transformation, load, and associated control processes for integrating disparate data into a single conceptual database
  22. 22. Copyright 2013 by Data Blueprint 22 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.
  23. 23. Copyright 2013 by Data Blueprint 23 from MicroStrategy, Better Business Decisions Every Day: Integrating Business Reporting & Analysis Example: Set Analysis
  24. 24. Copyright 2013 by Data Blueprint Polling Question #1 Do you have start data warehouse, data marts and/or other warehousing forms of integration? a) Last year (2012) b) This year (2013) c) Next Year (2014) d) Nope 24
  25. 25. Copyright 2013 by Data Blueprint 1. Data management overview 2. Motivation for warehousing integration technologies (reporting->BI->Analytics) 3. What are warehousing integration technologies? 4. Warehousing and architecture focus 5. The use of meta models 6. Guiding principles & best practices 7. Take aways, references and Q&A Tweeting now: #dataed Data Systems Integration & BV Part 3: Warehousing 25
  26. 26. Copyright 2013 by Data Blueprint 1. Data management overview 2. Motivation for warehousing integration technologies (reporting->BI->Analytics) 3. What are warehousing integration technologies? 4. Warehousing and architecture focus 5. The use of meta models 6. Guiding principles & best practices 7. Take aways, references and Q&A Tweeting now: #dataed Data Systems Integration & BV Part 3: Warehousing 26
  27. 27. Copyright 2013 by Data Blueprint • 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 Warehousing Definitions 27
  28. 28. Copyright 2013 by Data Blueprint 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 28 from The Data Administration Newsletter, www.tdan.com
  29. 29. Copyright 2013 by Data Blueprint 29 Basic Data Warehouse Analysis • 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
  30. 30. Copyright 2013 by Data Blueprint 30 Warehouse Analysis • Users can "drill" anywhere • Entire collection is accessible • Summaries to transaction-level detail from MicroStrategy, Better Business Decisions Every Day: Integrating Business Reporting & Analysis
  31. 31. Copyright 2013 by Data Blueprint Oracle 31 from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
  32. 32. Copyright 2013 by Data Blueprint R& D Applications (researcher supported, no documentation) Finance Application (3rd GL, batch system, no source) Payroll Application (3rd GL) Payroll Data (database) Finance Data (indexed) Personnel Data (database) R & D Data (raw) Mfg. Data (home grown database) Mfg. Applications (contractor supported) Marketing Application (4rd GL, query facilities, no reporting, very large) Marketing Data (external database) Personnel App. (20 years old, un-normalized data) 32 Multiple Sources of (for example) Customer Data
  33. 33. Copyright 2013 by Data Blueprint Corporate Information Factory Architecture 33 from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
  34. 34. Copyright 2013 by Data Blueprint Corporate Information Factory Architecture 34 from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
  35. 35. Copyright 2013 by Data Blueprint 35 from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International Corporate Information Factory Architecture
  36. 36. Copyright 2013 by Data Blueprint 36 from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International Corporate Information Factory Architecture
  37. 37. Copyright 2013 by Data Blueprint MetaMatrix Integration Example 37 • EII Enterprise Information Integration – between ETL and EAI - delivers tailored views of information to users at the time that it is required
  38. 38. Copyright 2013 by Data Blueprint Linked Data 38 Linked Data is about using the Web to connect related data that wasn't 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.org
  39. 39. Copyright 2013 by Data Blueprint 1. Data management overview 2. Motivation for warehousing integration technologies (reporting->BI->Analytics) 3. What are warehousing integration technologies? 4. Warehousing and architecture focus 5. The use of meta models 6. Guiding principles & best practices 7. Take aways, references and Q&A Tweeting now: #dataed Data Systems Integration & BV Part 3: Warehousing 39
  40. 40. Copyright 2013 by Data Blueprint 1. Data management overview 2. Motivation for warehousing integration technologies (reporting->BI->Analytics) 3. What are warehousing integration technologies? 4. Warehousing and architecture focus 5. The use of meta models 6. Guiding principles & best practices 7. Take aways, references and Q&A Tweeting now: #dataed Data Systems Integration & BV Part 3: Warehousing 40
  41. 41. Copyright 2013 by Data Blueprint Kimball's DW Chess Pieces 41 from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
  42. 42. Copyright 2013 by Data Blueprint 3 Courtesy of: http://www.infosys.com/industries/healthcare/industryofferings/Pages/healthcare -data-warehousing.aspx Data Warehousing
  43. 43. Copyright 2013 by Data Blueprint 3 Descriptive Ask: What happened? What is happening? Find: Structured data Show: Profiles, Bar/Pie charts, Narrative Predictive Ask: What will happen? Why will it happen? Find: Structured/unstructured data Show: Risk Profiles, Pros/Cons, Care Recs Prescriptive Ask: What should I do? Why should I do it? Find: Unstructured/structured data Show: Strategic Goals, Support Recs u Organization-wide u Volume and Noise u Utility u Meaningful scoring u Actionable recs u Realistic goals u Support u Manage & measure Analytics in Health Care
  44. 44. Copyright 2013 by Data Blueprint 3 Descriptive Ask: What happened? What is happening? Find: Structured data Show: Profiles, Bar/pie charts, Narrative Predictive Ask: What will happen? Why will it happen? Find: Structured/unstructured data Show: Risk Profiles, Pros/Cons, Care Recs Prescriptive Ask: What should I do? Why should I do it? Find: Unstructured/structured data Show: Strategic Goals, Support Recs BioMarin Licenses Factor VIII Gene Therapy Program for Hemophilia Novel Gene Therapy Approach to Hemophilia B Sangamo BioSciences Receives $6.4 Million Strategic Partnership Award From California Institute for Regenerative Medicine to Develop ZFP Therapeutic® Treating Hemophilia in the 2010s Hemophilia Management
  45. 45. Copyright 2013 by Data Blueprint 45 Styles of Business Intelligence from MicroStrategy, Better Business Decisions Every Day: Integrating Business Reporting & Analysis
  46. 46. Copyright 2013 by Data Blueprint 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 46 "I can take a roomful of MBAs and accomplish this analysis faster!"
  47. 47. Copyright 2013 by Data Blueprint 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 47 from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
  48. 48. Copyright 2013 by Data Blueprint Indiana Jones: Raiders Of The Lost Ark 48
  49. 49. Copyright 2013 by Data Blueprint 49 Business Intelligence Features Problematic Data Quality
  50. 50. Copyright 2013 by Data Blueprint 5 Key Business Intelligence Trends 1. There's 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. 50 http://www.cio.com/article/150450/Five_Key_Business_Intelligence_Trends_You_Need_to_Know?page=2&taxonomyId=3002
  51. 51. Copyright 2013 by Data Blueprint Polling Question #2 Do you have? a) A single enterprise data warehouse b) Coordinated data marts c) Both d) Uncoordinated efforts e) None 51
  52. 52. Copyright 2013 by Data Blueprint 1. Data management overview 2. Motivation for warehousing integration technologies (reporting->BI->Analytics) 3. What are warehousing integration technologies? 4. Warehousing and architecture focus 5. The use of meta models 6. Guiding principles & best practices 7. Take aways, references and Q&A Tweeting now: #dataed Data Systems Integration & BV Part 3: Warehousing 52
  53. 53. Copyright 2013 by Data Blueprint 1. Data management overview 2. Motivation for warehousing integration technologies (reporting->BI->Analytics) 3. What are warehousing integration technologies? 4. Warehousing and architecture focus 5. The use of meta models 6. Guiding principles & best practices 7. Take aways, references and Q&A Tweeting now: #dataed Data Systems Integration & BV Part 3: Warehousing 53
  54. 54. Copyright 2013 by Data Blueprint Source:http://dmreview.com/article_sub.cfm?articleID=1000941 used with permission Meta Data Models 54
  55. 55. Copyright 2013 by Data Blueprint Metadata Data Model SCREEN ELEMENT screen element id # data item id # screen element descr. INTERFACE ELEMENT interface element id # data item id # interface element descr. INPUT ELEMENT input element id # data item id # input element descr. OUTPUT ELEMENT output element id # data item id # output element descr. MODEL VIEW model view element id # data item id # model view element des. DEPENDENCY dependency elem id # data item id # process id # dependency description CODE code id # data item id # stored data item # code location INFORMATION information id # data item id # information descr. information request PROCESS process id # data item id # process description USER TYPE user type id # data item id # information id # user type description LOCATION location id # information id # printout element id # process id # stored data items id # user type id # location description PRINTOUT ELEMENT printout element id # data item id # printout element descr. STORED DATA ITEM stored data item id # data item id # location id # stored data description DATA ITEM data item id # data item description 55
  56. 56. Copyright 2013 by Data Blueprint Warehouse Process Warehouse Opera-on Transforma-on XML Record-­‐ Oriented Mul- Dimensional Rela-onal Business Informa-on So?ware Deployment ObjectModel (Core,  Behavioral,  Rela-onships,  Instance) Warehouse Management Resources Analysis Object-­‐ Oriented (ObjectModel) Foundation OLAP Data   Mining Informa-on Visualiza-on Business Nomenclature Data Types Expressions Keys Index Type Mapping Overview of CWM Metamodel http://www.omg.org/technology/documents/modeling_spec_catalog.htm 56
  57. 57. Copyright 2013 by Data Blueprint Marco & Jennings's Complete Meta Data Model Source:http://dmreview.com/article_sub.cfm?articleID=1000941 used with permission 57
  58. 58. Copyright 2013 by Data Blueprint 1. Data management overview 2. Motivation for warehousing integration technologies (reporting->BI->Analytics) 3. What are warehousing integration technologies? 4. Warehousing and architecture focus 5. The use of meta models 6. Guiding principles & best practices 7. Take aways, references and Q&A Tweeting now: #dataed Data Systems Integration & BV Part 3: Warehousing 58
  59. 59. Copyright 2013 by Data Blueprint 1. Data management overview 2. Motivation for warehousing integration technologies (reporting->BI->Analytics) 3. What are warehousing integration technologies? 4. Warehousing and architecture focus 5. The use of meta models 6. Guiding principles & best practices 7. Take aways, references and Q&A Tweeting now: #dataed Data Systems Integration & BV Part 3: Warehousing 59
  60. 60. Copyright 2013 by Data Blueprint from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International 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 60
  61. 61. Copyright 2013 by Data Blueprint • 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 61 from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International Activities
  62. 62. Copyright 2013 by Data Blueprint 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 62 from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
  63. 63. Copyright 2013 by Data Blueprint 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 Participants: • Executives/managers • Data Stewards • Subject Matter Experts • Data Architects • Data Analysts • Application Architects • Data Governance Council • Data Providers • Other BI Professionals 63
  64. 64. Copyright 2013 by Data Blueprint 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 64
  65. 65. Copyright 2013 by Data Blueprint 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. 65 from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International 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.
  66. 66. Copyright 2013 by Data Blueprint 6 Best Practices for Data Warehousing 66 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
  67. 67. Copyright 2013 by Data Blueprint Polling Question #3 Do you have a separate data warehouse department, sub- department, or group? a) Yes b)No 67
  68. 68. Copyright 2013 by Data Blueprint 1. Data management overview 2. Motivation for warehousing integration technologies (reporting->BI->Analytics) 3. What are warehousing integration technologies? 4. Warehousing and architecture focus 5. The use of meta models 6. Guiding principles & best practices 7. Take aways, references and Q&A Tweeting now: #dataed Data Systems Integration & BV Part 3: Warehousing 68
  69. 69. Copyright 2013 by Data Blueprint 1. Data management overview 2. Motivation for warehousing integration technologies (reporting->BI->Analytics) 3. What are warehousing integration technologies? 4. Warehousing and architecture focus 5. The use of meta models 6. Guiding principles & best practices 7. Take aways, references and Q&A Tweeting now: #dataed Data Systems Integration & BV Part 3: Warehousing 69
  70. 70. Copyright 2013 by Data Blueprint Summary: Data Warehousing & Business Intelligence Management 70 from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
  71. 71. Copyright 2013 by Data Blueprint Series Take Aways 71 • Metadata – Metadata unlocks the value of data, and therefore requires management attention [Gartner 2011] – Metadata is the language of data governance – Metadata defines the essence of integration challenges • Cloud – Data governance, architecture, quality, development maturity are necessary but insufficient prerequisites to successful data cloud implementation – A variety of cloud options will influence cloud and data architectures in general – You must understand your architecture and strategy in order to evaluate the options – Data must be reengineered to be: less; better quality; more shareable – Failure to do these will result in more business value for the cloud vendors/ service providers and less for your organization • Warehousing – Business value must precede technical design
  72. 72. Copyright 2013 by Data Blueprint References 72
  73. 73. Copyright 2013 by Data Blueprint References 73
  74. 74. Copyright 2013 by Data Blueprint 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 74
  75. 75. Copyright 2013 by Data Blueprint Questions? It’s your turn! Use the chat feature or Twitter (#dataed) to submit your questions to Peter now. 75 + =
  76. 76. Copyright 2013 by Data Blueprint Upcoming Events 76 October Webinar: SHOW ME THE MONEY: MONETIZING DATA MANAGEMENT October 8, 2013 @ 2:00 PM – 3:30 PM ET (11:00 AM-12:30 PM PT) November Webinar: UNLOCK BUSINESS VALUE THROUGH REFERENCE & MDM November 12, 2013 @ 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:

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