1. Business Intelligence Lecture 25

2. What is Business Intelligence (BI) Definitions: Business Intelligence (BI) refers to skills, processes, technologies, applications and practices used to support decision making. Systems that provide directed background data and reporting tools to support and improve the decision-making process. A popularized, umbrella term used to describe a set of concepts and methods to improve business decision making by using fact-based support systems. The term is sometimes used interchangeably with briefing books and executive information systems. Business Intelligence is a broad category of applications and technologies for gathering, storing, analyzing, and providing access to data to help clients make better business decisions. A system that collects, integrates, analyses and presents business information to support better business decision making. Business Intelligence is an environment in which business users receive information that is reliable, secure, consistent, understandable, easily manipulated and timely...facilitating more informed decision making

3. What is BI (continued) © 2008 Accenture. All Rights Reserved. Improving organizations by providing business insights to all employees leading to better, faster, more relevant decisions

4. What is Business Intelligence? Business Intelligence enables the business to make intelligent, fact-based decisions Aggregate Data Database, Data Mart, Data Warehouse, ETL Tools, Integration Tools Present Data Enrich Data Inform a Decision Reporting Tools, Dashboards, Static Reports, Mobile Reporting, OLAP Cubes Add Context to Create Information, Descriptive Statistics, Benchmarks, Variance to Plan or LY Decisions are Fact-based and Data-driven

5. Content  The business determines the “what”, BI enables the “how” Performance  Minimize report creation and collection times (near zero) Usability  Delivery Method  Push vs Pull  Medium  Excel, PDF, Dashboard, Cube, Mobile Device  Enhance Digestion  “A-ha” is readily apparent, fewer clicks  Tell a Story  Trend, Context, Related Metrics, Multiple Views CPU – Content, Performance, Usability

6. Core Capabilities of BI OLAP (online analytical processing) enables a user to easily and selectively extract and view data from different points-of- view.

7. Why do companies need BI? Tactical / Strategic BI What’s the best that can happen? What will happen next? What if these trends continue? Why is this happening? What actions are needed? Where exactly is the problem? How many, how often, where? What happened? Sophistication of Intelligence Operational BI Optimization Predictive Modeling Forecasting/extrapolation Statistical analysis Alerts Query/drill down Ad hoc reports Standard reports Competitive Advantage © 2008 Accenture. All Rights Reserved.

8. How Important is BI? Top 10 Business and Technology Priorities for 2011: 1. Cloud computing 2. Virtualization 3. Mobile technologies 4. IT Management 5. Business Intelligence 6. Networking, voice and data communications 7. Enterprise applications 8. Collaboration technologies 9. Infrastructure 10. Web 2.0 Source: Gartner’s 2011 CIO Agenda (aka “Reimagining IT: The 2011 CIO Agenda”).Reimagining IT: The 2011 CIO Agenda”).

9. The July 2010 Forrester report “Technology Trends That Retail CIOs Must Tap to Drive Growth” identified the following technologies that retail CIOs should be considering as part of an overall architecture strategy: Mobile Cloud Social Computing Supply Chain Micropayments Business Intelligence/Analytics

10. Why is Business Intelligence So Important? Time With Business Intelligence, we can get data to you in a timely manner. Making Business Decisions is a Balance Data Opinion (aka Best Professional Judgment) In the absence of data, business decisions are often made by the HiPPO.

11. Benefits of Business Intelligence Improve Management Processes – planning, controlling, measuring and/or changing resulting in increased revenues and reduced costs Improve Operational Processes – fraud detection, order processing, purchasing.. resulting in increased revenues and reduced costs Predict the Future

12. Examples - EMC 1998: Revenue $2.5b 1998: HW (90%) + SVCS (10%) + SW (0%) Strategic BI: predictive modelling => decision made HW (10%) + SVCS (10%) + SW (80%) 2010: Revenue $16b

13. 0 100 200 300 400 500 600 700 12345678910111213 Fiscal Week $ Millions Factories ship ≈40% of quarterly revenue in last week! Build to Stock for orders in last two days! Typical Activity by Week ($M) (Storage Products) Bookings Factory Shipments EMC Quarter Activity

14. EMC Order Life Cycle ProspectLeadOpptyConfigurePriceQuoteOrderProduceShipInvoiceCollect Commissions Account Planning Project Accounting Service Suspect Channel Integration Quota

15. Examples - Walmart Average daily sales of American Flags = 6,000 September 11 th 2001 All competitors ran out of flags Nearest rival sold 20,000 Walmart sold 116,000 flags on that day alone

16. Further examples Call centres – e.g. Top Agent awarded bonus -> competition leading to performance improvements Banks – jettison walk in customers to encourage online only Criminal Minds – Information gathered on previous actions of serial killers allows the team to predict the actions of future serial killers Revenue Service – who has the yacht but cannot afford it Plagiarism detection in colleges Customer Loyalty Programs Twitter analysis for public mood Dell Healthcare – predicting infection in rural parts of third world

17. BI Golden Rules Data Quality & Accuracy Data Consistency Data Timeliness “Get the right information to the right people at the right time”

18. Gartner BI Maturity Model

19. Major BI Trends Mobile Cloud Social Media Advanced Analytics

20. What BI technologies will be the most important to your organization in the next 3 years? 1. Predictive Analytics 2. Visualization/Dashboards 3. Master Data Management 4. The Cloud 5. Analytic Databases 6. Mobile BI 7. Open Source 8. Text Analytics TDWI Executive Summit – August 2010

21. Advanced Analytics / Predictive Analytics Data Mining Regression Monte Carlo Simulation “Statistically Significant” Predicting Customer Behavior  Churn/Attrition  Purchases  Profiling

22. BI Today vs Tomorrow “BI today is like reading the newspaper”  BI reporting tool on top of a data warehouse that loads nightly and produces historical reporting BI tomorrow will focus more on real-time events and predicting tomorrow’s headlines

23. Collegiate Admissions Criteria Test Scores: SAT, ACT, AP Exams Grade Point Average Class Rank High School “Strength” Extracurricular Activities: Band/Choir, Clubs, Sports Non-School Activities: Work, Volunteer, Community Groups Area of Focus – Intended Major Family legacy Home State or Country Regression Outcome = Graduation (binary) + GPA (linear)

24. Retail Analytics Market Basket Analytics Text Analytics Customer Segmentation/Clustering Tailored Product Assortments Inventory Forecasting

25. 25 Amazon.com and NetFlix Collaborative Filtering tries to predict other items a customer may want to purchase based on what’s in their shopping cart and the purchasing behaviors of other customers

26. 26 What Is Text Analytics? …turning unstructured customer comments into actionable insights …finding nuggets of insight in text data that will improve our business From Wikipedia: … a set of linguistic, statistical, and machine learning techniques that model and structure the information content of textual sources for business intelligence, exploratory data analysis, research, or investigation

27. 27 Customer Sat Survey Comments Unstructured Text Processing Facebook Page Blogs Competitors’ Facebook Pages Public Web Sites, Discussion Boards, Product Reviews Alerts, Real-time Action Twitter Page Services QualityCostFriendliness Email Adhoc Feedback Call Center Notes, Voice

31. What is Information Governance? Information Governance Data Stewardship Data Quality Data Governance Master Data Management Data Stewards for Master Data “Hubs” Customer, Vendor, Product, Location, Employee, G/L Accounts PREVENTS Garbage In Garbage Out BY ENCOMPASSING Report Governance Metric Governance 31 CREATING SIGNIFICANT BUSINESS VALUE

32. BI Technologies Analytic Databases BI is a consolidating industry  Oracle: Siebel, Hyperion, Brio, Sun  SAP: Business Objects, Sybase  IBM: Cognos, SPSS, Coremetrics, Unica, Netezza  EMC: Greenplum  HP: Vertica  Teradata: Aster Data Independent vendors: MicroStrategy, Informatica, SAS Reporting standards determined mainly by Microsoft, Apple and Adobe Teradata Netezza DB2 Oracle SQL Server Vertica Aster Data Par Accel Greenplum Semantic Databases (TIDE)

33. Why do companies need BI? ANALYTICS (Tactical & Strategic) What’s the best that can happen? What will happen next? What if these trends continue? Why is this happening? What actions are needed? Where exactly is the problem? How many, how often, where? What happened? Sophistication of Intelligence DATA ACCESS & REPORTING (Operational) Optimization Predictive Modeling Forecasting/extrapolation Statistical analysis Alerts Query/drill down Ad hoc reports Standard reports Competitive Advantage © 2008 Accenture. All Rights Reserved.

34. Summary We covered Today  Knowledge and Knowledge Management  Business Intelligence overview