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Data-Ed: Data Warehousing Strategies

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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. This webinar will illustrate that good systems development more often depends on at least three data management disciplines in order to provide a solid foundation.
Find more Data-Ed webinars here: http://www.datablueprint.com/resource-center/webinar-schedule/

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Data-Ed: Data Warehousing Strategies

  1. 1. Data Warehousing Strategies Peter Aiken, Ph.D. & Steven MacLauchlan
  2. 2. 2 Premise Two types of listeners … 1. Interested in how to approach the subject of warehousing data – Need to integrate disparate Copyright 2014 by Data Blueprint data – Need more holistic view of business operations – Management just discovered data warehouses and wants you to "build one" 2. Have complex and/or messy data warehouse practices – Want to improve them
  3. 3. Data Warehousing Strategies 3 Copyright 2014 by Data Blueprint 1. Warehousing data in the context of data management 2. Motivation for integration technologies (reporting->BI->Analytics) 3. Warehouse integration technologies 4. Three warehousing architecture foci 5. The use of meta models 6. Guiding principles & best practices
  4. 4. Maslow's Hierarchiy of Needs 4 Copyright 2014 by Data Blueprint
  5. 5. Data Management Practices Hierarchy You can accomplish Advanced Data Practices without becoming proficient in the Foundational Data Management Practices however this will: • Take longer • Cost more • Deliver less • Present greater risk (with thanks to Tom DeMarco) Technologies Capabilities Advanced Data Practices • MDM • Mining • Big Data • Analytics • Warehousing • SOA Foundational Data Management Practices 5 Copyright 2014 by Data Blueprint Data Governance Data Quality Data Platform/Architecture Data Management Strategy Data Operations
  6. 6. ReUusesses What is data management? 6 Copyright 2014 by Data Blueprint Sources Data Governance Data Engineering Data Delivery Data Storage Specialized Team Skills Understanding the current and future data needs of an enterprise and making that data effective and efficient in supporting business activities Aiken, P, Allen, M. D., Parker, B., Mattia, A., "Measuring Data Management's Maturity: A Community's Self-Assessment" IEEE Computer (research feature April 2007) Data management practices connect data sources and uses in an organized and efficient manner • Storage • Engineering • Delivery • Governance When executed, engineering, storage, and delivery implement governance Note: does not well-depict data reuse
  7. 7. Manage data coherently Maintain fit-for-purpose data, efficiently and effectively DMM℠ Structure of 5 Integrated DM Practice Areas 7 Manage data assets professionally Copyright 2014 by Data Blueprint Data architecture implementation Data engineering implementation Organizational support
  8. 8. Data Management Body of Knowledge 8 Copyright 2014 by Data Blueprint Data Management Functions
  9. 9. DAMA DM BoK & CDMP 9 Copyright 2014 by Data Blueprint • 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
  10. 10. Data Warehousing & Business Intelligence Management 10 Copyright 2014 by Data Blueprint
  11. 11. Warehousing data in the context of data management 11 Copyright 2014 by Data Blueprint Assumes you have • An overarching data strategy • A strategy for becoming familiar with "big data technologies" • Made a decision to not make available (integrating or storing) needed data • Decided to increase (or decrease) the complexity of existing DM practices • Decided to learn more about this DM BoK slice Sources ReUusesses Data Governance Data Engineering Data Delivery Data Storage Specialized Team Skills
  12. 12. Data Warehousing Strategies 12 Copyright 2014 by Data Blueprint 1. Warehousing data in the context of data management 2. Motivation for integration technologies (reporting->BI->Analytics) 3. Warehouse integration technologies 4. Three warehousing architecture foci 5. The use of meta models 6. Guiding principles & best practices
  13. 13. Typical System Evolution Payroll Application Payroll Data (3rd GL) (database) Finance Data (indexed) R & D Data (raw) Mfg. Data R& D Applications (researcher supported, no documentation) (home grown database) Finance Application (3rd GL, batch system, no source) 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) Personnel Data (database) 13 Copyright 2014 by Data Blueprint Multiple Sources of (for example) Customer Data
  14. 14. Payroll Data (database) R & D Data (raw) Finance Data (indexed) Mfg. Data (home grown database) Marketing Data (external database) Personnel Data (database) ... Then Integrate 14 Copyright 2014 by Data Blueprint Organizational Data
  15. 15. ... Then Re-architect Payroll Data (database) R & D Data (raw) Finance Data (indexed) Mfg. Data (home grown database) Marketing Data (external database) Personnel Data (database) 15 Copyright 2014 by Data Blueprint Organizational Data
  16. 16. An organization's integration needs ... ... map between and across software packages 16 Data Architecture Copyright 2014 by Data Blueprint Software Package 1 Software Package 2 Software Package 3 Software Package 4 Software Package 5 Software Package 6
  17. 17. From The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International Defining Data Warehousing, BI/Analytics 17 Copyright 2014 by Data Blueprint • Data Warehousing – A technology solution supporting … business capabilities such as: query, analysis, reporting and development of these capabilities – Analysis of information not previously integrated – Another, often new, set of organizational capabilities • Business Intelligence (aka. decision support) – Dates at least to 1958 – Support better business decision making – Technologies, applications and practices for the collection, integration, analysis, and presentation of business information – Understanding historical patterns in data to improve future performance – Use of mathematics in business • Analytics (aka.) enterprise decision management, marketing analytics, predictive science, strategy science, credit risk analysis. fraud analytics - often based on computational modeling • Reframing the question … – From: what data warehouse should we build? – To: how can data warehouse-based integration address challenges?
  18. 18. Hemophilia Management Analytics 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 18 Copyright 2014 by Data Blueprint
  19. 19. Target Isn't Just Predicting Pregnancies http://rmportal.performedia.com/node/1373 and http://www.predictiveanalyticsworld.com/patimes/target-really-predict-teens-pregnancy-inside-story/ http://rmportal.performedia.com/rm/paw10/gallery_01#1373 19 Copyright 2014 by Data Blueprint
  20. 20. Basics • Summaries to transaction-level detail 20 • Users can "drill" anywhere • Entire collection "cube" is accessible Copyright 2014 by Data Blueprint
  21. 21. Sample questions … 21 Copyright 2014 by Data Blueprint Cancer patient revenue across all facilities Revenue for diseases this year versus last year in the NE region Total costs and revenue at top 10 facilities • Emphasis on the "cube" – N dimensions • Permits different users to "slice and dice" subsets of data • Viewing from different perspectives
  22. 22. Example: Set Analysis 22 Copyright 2014 by Data Blueprint
  23. 23. Portfolio Analysis 23 • Bank accounts are of varying value and risk • Cube by – Social status – Geographical location – Net value, etc. • Strategy or goal: 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 Copyright 2014 by Data Blueprint limited number – Many poor customers, but greater risk • Solution may combine types of analyses – When to lend, interest rate charged
  24. 24. 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 - datablueprint.com CarMax Example Job Posting --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 24 Copyright 2014 by Data Blueprint 24 own an area of the business and will be expected to improve it
  25. 25. Polling Question #1 25 Copyright 2014 by Data Blueprint • Do you have/have you started data warehousing, marts and/or other warehousing forms of integration? a. Last year (2014) b. This year (2015) c. Next Year (2016) d. Nope
  26. 26. Data Warehousing Strategies 26 Copyright 2014 by Data Blueprint 1. Warehousing data in the context of data management 2. Motivation for integration technologies (reporting->BI->Analytics) 3. Warehouse integration technologies 4. Three warehousing architecture foci 5. The use of meta models 6. Guiding principles & best practices
  27. 27. Technology from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International 27 Copyright 2014 by Data Blueprint • 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
  28. 28. Warehousing Definitions 28 • 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 Copyright 2014 by Data Blueprint analysis." • Key concepts focus on: – Subjects – Transactions – Non-volatility – Restructuring
  29. 29. Courtesy of: http://www.infosys.com/industries/healthcare/industryofferings/Pages/healthcare - data-warehousing.aspx Warehousing applied to a specific challenge 29 Copyright 2014 by Data Blueprint
  30. 30. Oracle from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International 30 Copyright 2014 by Data Blueprint
  31. 31. from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International Corporate Information Factory Architecture 31 Copyright 2014 by Data Blueprint
  32. 32. MetaMatrix Integration Example • EII Enterprise Information Integration – between ETL and EAI - delivers tailored views of information to users at the time that it is required 32 Copyright 2014 by Data Blueprint
  33. 33. Linked Data 33 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." Copyright 2014 by Data Blueprint linkeddata.org
  34. 34. Health Care Provider Data Warehouse 34 "A roomful of MBAs can accomplish this analysis faster!" Copyright 2014 by Data Blueprint • 1.8 million members • 1.4 million providers • 800,000 providers no key • 29% prov_ssn ≠ 9 digits • 2.2% prov_number = 9 digits (required) • 1 User • $30 million
  35. 35. Indiana Jones: Raiders Of The Lost Ark 35 Copyright 2014 by Data Blueprint
  36. 36. from The Data Administration Newsletter, www.tdan.com and The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International Causes of Data Warehouse Failure 36 Copyright 2014 by Data Blueprint 1. The project is over budget 2. Slipped schedule 3. Unimplemented functions and capabilities 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 11. Poor quality data 12. Many more values of gender code than (M/F) 13. Incorrectly structured data 14. Provides correct answer to wrong question 15. Bad warehouse design 16. Overly complex
  37. 37. Reframing the question 37 Copyright 2014 by Data Blueprint • From: How shall we build this data warehouse? – (Worse) … What should go into this warehouse? • To: How can warehousing capabilities solve this specific business challenge? – (Better still) … How can warehousing capabilities solve this class of business challenges? • Other examples – Are you ready for a data warehouse? ✓ Foundational practices – Will you get it right the first time? ✓ Is the business environment constantly evolving? ✓ Do you have an agreed upon enterprise-wide vocabulary? – Is your data warehouse intended to be the enterprise audit-able system of record? ✓ Extract, transform and load requirements ✓ Data transformation requirements – How fast do you need results? ✓ Performance of inserts vs reads ✓ Project deliverables
  38. 38. Data Warehousing Strategies 38 Copyright 2014 by Data Blueprint 1. Warehousing data in the context of data management 2. Motivation for integration technologies (reporting->BI->Analytics) 3. Warehouse integration technologies 4. Three warehousing architecture foci 5. The use of meta models 6. Guiding principles & best practices
  39. 39. Copyright 2013 by Data Blueprint Inmon Implementation/3NF 39 OPERATIONAL SYSTEM OPERATIONAL SYSTEM FLAT FILES METADATA SUMMARY DATA RAW DATA PURCHASING SALES INVENTORY ANALYSIS REPORTING MINING
  40. 40. Third Normal Form 40 • Each attribute in the relationship is a fact about a key Copyright 2014 by Data Blueprint – Highly normalized structure • Use Cases – Transactional Systems – Operational Data Stores
  41. 41. Third Normal Form: Pros and Cons 41 Copyright 2014 by Data Blueprint Neo4j.com • Pros – Easily understood by business and end users – Reduced data redundancy – Enforced referential integrity – Indexed attributes/flexible querying • Cons – Joins can be expensive – Does not scale
  42. 42. Kimball Implementation/Dimensional Copyright 2013 by Data Blueprint 42
  43. 43. Star Schema • Comprised of “fact tables” that contain quantitative data, and any number of adjoining “dimension” tables • Optimized for business reporting • Use Cases – OLAP (Online Analytic Processing) – BI 43 Copyright 2014 by Data Blueprint Wikipedia
  44. 44. Star Schema Pros and Cons 44 Copyright 2014 by Data Blueprint • Pros – Simple Design – Fast Queries – Most major DBMS are optimized for Star Schema Designs • Cons – Questions must be built into the design – Data marts are often centralized on one fact table
  45. 45. Copyright 2013 by Data Blueprint Data Vault Implementation 45
  46. 46. Data Vault 46 • Designed to facilitate long-term historical storage, focusing on ease of implementation • Retains data lineage information (source/date) • “All the data, all the time” - hybrid approach of Inmon and Kimball. • Comprised of Hubs (which contain a list of business keys that do not change often), Links (Associations/transactions between hubs), and Satellites (descriptive attributes associated with hubs and links) • Use Cases – Data Warehousing – Complete Audit-ability Copyright 2014 by Data Blueprint Bukhantsov.org
  47. 47. Data Vault Pros and Cons 47 Copyright 2014 by Data Blueprint • Pros – Simple integration – Houses immense amounts of data with excellent performance – Full data lineage captured • Cons – Complication is pushed to the “back end” – Can be difficult to setup for many data workers – No widespread support for ETL tools yet
  48. 48. Comparison 48 Copyright 2014 by Data Blueprint
  49. 49. Polling Question #2 49 Copyright 2014 by Data Blueprint • Do you have? a. A single enterprise data warehouse b. Coordinated data marts c. Both d. Uncoordinated efforts e. None
  50. 50. Data Warehousing Strategies 50 Copyright 2014 by Data Blueprint 1. Warehousing data in the context of data management 2. Motivation for integration technologies (reporting->BI->Analytics) 3. Warehouse integration technologies 4. Three warehousing architecture foci 5. The use of meta models 6. Guiding principles & best practices
  51. 51. Meta Data Models Source:http://dmreview.com/article_sub.cfm?articleID=1000941 used with permission 51 Copyright 2014 by Data Blueprint
  52. 52. 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 PRINTOUT ELEMENT printout element id # data item id # printout element descr. LOCATION location id # information id # printout element id # process id # stored data items id # user type id # location description USER TYPE user type id # data item id # information id # user type description STORED DATA ITEM stored data item id # data item id # location id # stored data description DATA ITEM data item id # data item description 52 Copyright 2014 by Data Blueprint
  53. 53. 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 Analysis Resources 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 53 Copyright 2014 by Data Blueprint
  54. 54. Marco & Jennings's Complete Meta Data Model 54 Copyright 2014 by Data Blueprint Source:http://dmreview.com/article_sub.cfm?articleID=1000941 used with permission
  55. 55. Data Warehousing Strategies 55 Copyright 2014 by Data Blueprint 1. Warehousing data in the context of data management 2. Motivation for integration technologies (reporting->BI->Analytics) 3. Warehouse integration technologies 4. Three warehousing architecture foci 5. The use of meta models 6. Guiding principles & best practices
  56. 56. Guiding Principles 8. Think and architect globally, act and build locally 9. Collaborate with and integrate all other data initiatives, especially those for 56 Copyright 2014 by Data Blueprint 1. Obtain executive commitment and support 2. Secure business SMEs 3. Let the business drive the priorities 4. Demonstrate data quality is essential 5. Provide incremental value 6. Transparency and self service 7. One size does not fit all: Secure the right tools and products for each of your segments 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
  57. 57. Data Reengineering Leverage 57 Copyright 2014 by Data Blueprint Data Management Practices "Warehoused" Data Duplicated but ETLed Data (quality & transformations applied) Learning/ Feedback Marts Analytics Practices
  58. 58. 58 Copyright 2014 by Data Blueprint Data Warehousing Strategies 1. Warehousing data in the context of data management 2. Motivation for integration technologies (reporting->BI->Analytics) 3. Warehouse integration technologies 4. Three warehousing architecture foci 5. The use of meta models 6. Guiding principles & best practices
  59. 59. Data Warehousing & Business Intelligence Management 59 Copyright 2014 by Data Blueprint
  60. 60. Questions? 60 Copyright 2014 by Data Blueprint It’s your turn! Use the chat feature or Twitter (#dataed) to submit your questions to Peter and Steven now.
  61. 61. Upcoming Events January Webinar: Developing a Data-centric Strategy & Roadmap January 13, 2015 @ 2:00 PM – 3:30 PM ET (11:00 AM-12:30 PM PT) February Webinar: Unlocking Business Value through Reference and Master Data Management February 10, 2015 @ 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: 61 Copyright 2014 by Data Blueprint
  62. 62. Appendix 62 Copyright 2014 by Data Blueprint
  63. 63. Goals and Principles from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International 63 Copyright 2014 by Data Blueprint 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
  64. 64. Activities 64 • 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 Copyright 2014 by Data Blueprint
  65. 65. Primary Deliverables from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International 65 • 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 Copyright 2014 by Data Blueprint
  66. 66. Roles and Responsibilities from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International 66 Copyright 2014 by Data Blueprint 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 Consumers: • Application Users • BI and Reporting Users • Application Developers and Architects • Data integration Developers and Architects • BI Vendors and Architects • Vendors, Customers and Partners
  67. 67. 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. 67 http://www.agiledata.org/essays/dataWarehousingBestPractices.html Copyright 2014 by Data Blueprint
  68. 68. from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International Kimball's DW Chess Pieces 68 Copyright 2014 by Data Blueprint
  69. 69. http://www.cio.com/article/150450/Five_Key_Business_Intelligence_Trends_You_Need_to_Know?page=2&taxonomyId=3002 5 Key Business Intelligence Trends 69 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. Copyright 2014 by Data Blueprint
  70. 70. from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International Corporate Information Factory Architecture 70 Copyright 2014 by Data Blueprint
  71. 71. Corporate Information Factory Architecture from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International 71 Copyright 2014 by Data Blueprint
  72. 72. from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International Corporate Information Factory Architecture 72 Copyright 2014 by Data Blueprint
  73. 73. from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International Corporate Information Factory Architecture 73 Copyright 2014 by Data Blueprint
  74. 74. References 74 Copyright 2014 by Data Blueprint
  75. 75. References 75 Copyright 2014 by Data Blueprint
  76. 76. 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- 76 Copyright 2014 by Data Blueprint 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

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