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Business Intelligence De-Mystified

Business Intelligence De-Mystified. Ben Bor NZ Ministry of Health. Ben Bor. Over 20 years in IT, most of it in Information Management Oracle specialist since version 5 Involved in Business Intelligence for over 10 years Consulted the world’s largest corporations

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Business Intelligence De-Mystified

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  1. Business IntelligenceDe-Mystified Ben Bor NZ Ministry of Health

  2. Ben Bor • Over 20 years in IT, most of it in Information Management • Oracle specialist since version 5 • Involved in Business Intelligence for over 10 years • Consulted the world’s largest corporations • Presents regularly on Information Management • Was annual Guest Lecturer at Sussex University

  3. Session Objectives • Understand the need for Business Intelligence and its role in the enterprise information strategy • Understand the role of the various Business Intelligence technologies and tools • Understand the BI components and the importance of Data Quality

  4. Contents • Business Intelligence (BI) – Definition and Examples • Data Warehousing (DW) – Definition and Architecture • BI Challenges • The BI Promise • OLAP • Data Mining • Dashboards • Alerts

  5. Business Intelligence Ingredients • Data Warehousing • Data Marts • OLAP • Data Mining • Data Quality And others

  6. Business Intelligence – Definition ‘Business Intelligence is the art of gaining business advantage from data’ • Who are my best and worst customers? • What parameters affect my sales? • What advantages does my business offer customers? • Analyse my products by any parameter.

  7. Some BI Success Stories How much am I spending? What do I know about Joe Bloggs? Integrated view of Customers & Suppliers

  8. Who Needs Business Intelligence? (Gartner Group) Business Intelligence Quadrants The Captive Customer BI utilized by limited numbers of experts to reduce costs of delivering services to large numbers of customers. No competitive threats exist. Global 2000 BI critical to understand complexity of business, leverage customer and supplier relationships and grasp and exploit new opportunities. Important Essential Volume of Information The “Candy Store” BI capabilities are of limited utility. Decisions made based on personal management observations of customer trends and markets. The “e” Startup Extreme need to understand competition, market and customer trends. BI is pervasive as a competitive weapon. Interesting Business Pace

  9. Data Warehousing – Definition 1 Accepted definition: ‘Subject Oriented, Integrated, Non-volatile, and Time Variant Collection of Data in Support of Management’s Decisions’. Bill Inmon ‘Building a Data Warehouse’, 2nd edition, wiley 1996.

  10. Data Warehousing – Definition 2 My definition: ‘A Data Warehouse is the enterprise single point of access to its data’

  11. Data Mart – Definition A Data Mart is a project that uses Data Warehousing techniques, but covers only a selected part of the enterprise data • Examples: • Accounting Data Mart • Sales Data Mart

  12. Data Warehousing - How a set of technologies: Data Analysis Access Different Data Sources Data Cleansing & Normalising (ETL) Data Storage Presentation

  13. Data Warehouse Architecture Access Tools Layer Oracle BI Discoverer Web-based Reports OLAP Dashboards Semantic Layer Metadata Views Tool-specific Business Model (i.e. BO universes) Federation Data Exploitation Services (DES) Views over CDS IC-maintained Data Marts (Physical) User-maintained Data Marts reconcile reconcile reconcile Core Data Store (CDS) Joining Structures Stand-alone (legacy) schemas Reference data Including person, company, address, household, etc’ Joined-up data External Databases ODS Staging (Data acquisition) Non-Persistent Staging Persistent Staging (with history) Extracted files Oracle Streams Log Mining XML Risk Engine Data Quality Profiling

  14. Inmon and Kimball

  15. Dimensional Modelling A design method that is • Not entity-relationship modelling • Not normalised • Easily understood by users • More efficient for BI

  16. Dimensional Modelling Example Consultants submit timesheets, showing the number of hours, the rate and their expenses per project per day. Managers (AD) are responsible for projects and consultants.

  17. Entity Relationship Design Consultant Rates Manager Client Expense Type Time Sheet Project Expense Project Task Project Staff Project Code

  18. Dimensional Modelling Example(Star Schema) Consultant Dimension Project Dimension Activity Facts Time Dimension Client Dimension Manager Dimension

  19. Dimensional Modelling Example (Snowflake Schema) Sector Division Team Client Consultant Project Activity Facts Time Client Month Manager Industry Quarter Division Year

  20. OLAP On-Line Analytical Processing • A data presentation method that allows the users to interactively change the criteria, the level and the contents • Usually based on a multi-dimensional model • Allows for drill-down, drill-up and drill-across

  21. PRODUCT M A R K E T PROD Regional Mgr. View Product Mgr. View SALES M A R K E T TIME TIME Ad Hoc View Financial Mgr. View OLAP - Multi Dimensional Cube

  22. OLAP Methods ROLAP MOLAP HOLAP Relational OLAP (Business objects) Multi-dimensional OLAP (Hyperion) Hybrid OLAP (Cognos)

  23. OLAP DEMO

  24. Data Mining - Definition A method for automatically deducing knowledge from data: • Patterns, clusters, rules, decision trees etc’

  25. Classification Tree IM for Data Classification Results Interpreting Tree Induction Results age > 35 = age < 35 sex = M sex = F salary > 80000 marital = S bal < 6300 2 classes who purchase luxury cars age < 35 age < 35 sex = M sex = F salary > 80000 marital = S bal > 6300 IBM Software Solutions

  26. Executive Dashboards

  27. The BI Assimilation Lifecycle Alerts C o m p l e x i t y OLAP Exception Reports Bulk Reports Time n months n months n months n months

  28. Balanced Scorecard A method of organisational performance measurement. Performance of an organisation from four perspectives: • Customer perspective(how do customers see us?) • Internal capabilities perspective(what must we excel at?) • Innovation and learning perspective(can we continue to improve and create value?) • Financial perspective(how do our owners/shareholders see us?)

  29. Information Quality Information is Data in context. Information Quality is Data Quality in context with meaning. The ability to trust the information • Data Quality • Reliable and repeatable testing • Metadata

  30. Main Challenges in Business Intelligence Business intelligence projects are • User-oriented • Large • “Complex simplicity” • Continuously evolving • Require deep technical and business knowledge • Stretch all limits: • Time, storage capacity, CPU, machine communications, human communication, human perception, and teamwork. • Data Quality

  31. What’s Happening in the BI world? • BI is becoming a norm • Mature, off-the-shelf tools • Combine structured and non-structured data • A small number of main players • New uses (Data Webhouse) • Real-time • Hosted BI • Open Source BI

  32. Summary - Business Intelligence

  33. Thank you ! I can be contacted atben_bor@moh.govt.nz

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