1 Business Intelligence De-Mystified Ben Bor NZ Ministry of Health 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  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. Business Intelligence is the art of gaining business advantage from data ‘Business Intelligence is the art of gaining business advantage from data’

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

8 Who Needs Business Intelligence? (Gartner Group) Business Pace VolumeofInformation 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. The “e” Startup Extreme need to understand competition, market and customer trends. BI is pervasive as a competitive weapon. Global 2000 BI critical to understand complexity of business, leverage customer and supplier relationships and grasp and exploit new opportunities. The “Candy Store” BI capabilities are of limited utility. Decisions made based on personal management observations of customer trends and markets. Interesting Important Essential Business Intelligence Quadrants

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: Access Different Data Sources Data Cleansing & Normalising (ETL) Data Storage Data Analysis Presentation

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

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

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

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

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 Ad Hoc View Regional Mgr. View Product Mgr. View Financial Mgr. View PROD MARKETMARKET TIME PRODUCT MARKETMARKET TIME SALES 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 age < 35 salary > sex = M sex = F marital = S age > 35 bal < 6300 = 2 classes who purchase luxury cars IBM Software Solutions age < 35 sex = Msex = F salary > 80000marital = S bal > 6300 IM for Data Classification Results Interpreting Tree Induction Results Classification Tree

26 Executive Dashboards

27 The BI Assimilation Lifecycle Time ComplexityComplexity Bulk Reports Exceptio n Reports OLAP Alerts 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

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34 Thank you ! I can be contacted at