Business Analytics

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Business Analytics

  1. 1. Smarter Business Intelligence:Advanced Analytics In Practice Sponsored by:
  2. 2. Webcast Logistics
  3. 3. Today’s PresentersChris MurphyEditor, InformationWeekDavid StodderAnalyst, Research and WriterPerceptive Information Strategies
  4. 4. AgendaSetting the stage• Analytics defined• Momentum• The business imperativeAdvanced Analytics Strategy
  5. 5. Analytics DefinedAnalytics: Prediction, statistical analysis,OptimizationBI: ReportingWhich sounds more exciting?
  6. 6. Analytics Momentum• Democratize Analytics Not just PhDs• Embedded Analytics Oracle, SAP, IBM, etc.• Analytics Templates• Real-Time Data• Competitive Advantage?
  7. 7. Analytics: Business Imperative• No. 1 among BI capabilities• Real-time insight, predictive demand• Real-Time Data
  8. 8. Poll Question 1 Q: We’re most interested in advanced analytics for: •Analyzing customer data •Finding new product opportunities •Understanding our risk •Predicting revenue •Analyzing supply chains •Other
  9. 9. Smarter Business IntelligenceAdvanced Analytics In Practice David Stodder Contributing Editor, TechWeb Perceptive Information Strategies dstodder@gmail.com
  10. 10. Today’s Webcast Discussion Agility: business context for analytics Business intelligence and data warehouse key trends and directions for analytics Analytics, information management and data warehousing Making analytics affordable: templates and pre- configured systems Analytics: predictive analytics and data mining Best practices and conclusion 12/7/2010 Copyright (c) David Stodder
  11. 11. To Be Agile, Be Aware: The Basis ofCompetition Today •Informed and proactive: Can anticipate and react in a coordinated fashion • Competing in real time: The strategic and tactical edge only lasts so long • Need to realize value from every customer, partner and process • Optimize to reduce latency, cut costs and improve performance 12/7/2010 Copyright (c) David Stodder
  12. 12. Agility and Awareness: InformationSystems Support – or Thwart? Danger that traditional information systems can thwart agility Standard, siloed reports can deliver incomplete and inaccurate views of a multi-channel world Known problems, known solutions: What about the unknown? Smart devices, smarter customers: Keeping pace with rising intelligence 12/7/2010 Copyright (c) David Stodder
  13. 13. Business Intelligence and DataWarehouse: Success FactorsHow good is the information? - Accuracy and quality; need it now, but need it right - Comprehensiveness; all relevant sources included? In searchof the single viewWhat can I do with the information? - Timely data is good, but do users understand it? - Think dynamically: Continuous people and processimprovementHow can I profit from (or protect myself with) thisinformation? - Operational intelligence is about business innovation - Risk and regulatory compliance are major drivers 12/7/2010 Copyright (c) David Stodder
  14. 14. Data Scarcity: Not the Problem Many organizations already swimming in an abundance of data Focus on gaining higher value from data Data is already big: “Big Data” focus on dynamic behavior and velocity of information (data at rest, in motion) Info managementchallenge: integratingaccess to internal andexternal data sources 12/7/2010 Copyright (c) David Stodder
  15. 15. Business Analytics: Sharpening FocusOn Desired Business Outcome • When outcome, and course are unknown: Use information to iterate toward clarity • Optimization: Use information to ensure no steps are wasted • Monitor and measure progress using BI and performance management • Example: customer loyalty tracking 12/7/2010 Copyright (c) David Stodder
  16. 16. Business Intelligence: Can it Take UsWhere We Want to Go? BI systems primarily provide quantitative data and tools to manipulate it for analysis and to support decision-making Goal is to deliver comprehensive views of business states and directions Broadening out from traditional base of analysts and power users working with limited data and updating 12/7/2010 Copyright (c) David Stodder
  17. 17. Business Intelligence Expectations Most Important BI Features (3) Reason for Utilizing BI (Top 5) 90%80% 80%70% 70%60% 60%50% 50%40% 40% 30%30% 20%20% 10%10% 0%0% Fast data Ability to collect Ability to predict exploration, query and analyze customer behavior, and analysis operational data in risk or business capabilities real time outcomes - InformationWeek Analytics BI and Information Management Survey, September 2010 12/7/2010 Copyright (c) David Stodder
  18. 18. Business Intelligence Technology:Pushing Past Limitations Historical data is vital, but can limit perspective and “actionability” of data Focus on exceptions, not “data dumps” User requirements always change: Self-service necessary to free users – and IT – of long, often unsatisfying development Search can’t be a stranger: How most people find information Collaboration: Decisions made by teams, not individuals; embed in applications, services 12/7/2010 Copyright (c) David Stodder
  19. 19. “Smarter” BI: Collaborative,Current, Easier to Use and Trusted• Dashboards display BI/perf. mgmt info, quickly andeasily understood; drill down for anomalies• Performance management starts to “template”information around KPIs and metrics• Collaborative potential: Integrating BI andcollaboration (e.g., IBMCognos 10 and LotusConnections)• “Real time” – meaningwhat’s important now(could be real-time data) IBM Cognos 10 dashboard example 12/7/2010 Copyright (c) David Stodder
  20. 20. But Can BI Do Analytics? Getting beyond reporting: Spreadsheets still most widely used tool for analysis Financial analysis #1 reason for utilizing BI: Critical to expand beyond accounting to support strategic and operational analysis (including activity-based costing) Implementation goals for BI (IWK Survey): ◦ Monitor/share metrics: 72% ◦ Analyze customer data to increase sales: 56% ◦ Analyze customer data to retain customers: 53% 12/7/2010 Copyright (c) David Stodder
  21. 21. Polling Question #2Q. What is your top priority with business intelligence?- Giving users self-service capabilities for visualization and drill-down analysis- Accessing real-time data- Using BI to improve data quality and consistency- Enabling performance management KPIs and metrics- Financial reporting and analysis 12/7/2010 Copyright (c) David Stodder
  22. 22. Smarter BI: Nowhere Without InfoManagement Infrastructure The “Hercules” of Business Intelligence Taking advantage of hardware advances (virtualization, very large memory, new chip designs) Information integration Enterprise data warehouse to support BI; rules to facilitate information governance (security, HIPAA, etc.) Single view of the truth: Data quality, profiling, discovery 12/7/2010 Copyright (c) David Stodder
  23. 23. Relevant, Timely and Reliable Data:Challenges Remain 12/7/2010 Copyright (c) David Stodder
  24. 24. From Traditional to Next-Generation Data Warehousing• Serving small, internal user  Real-time analytics to improve communities customer, partner service;• Built around extraction, real-time event alerting transformation and loading  ETL, MDM and federated (ETL) information integration• Historical analysis and  Support for performance reporting management KPIs and• Batch loading at off hours metrics, dashboards and• Different systems for simple scorecards & complex queries (e.g., ODS  Continuous updating and DW)‫‏‬  Deployment of appliances in-• Info delivery not memory analytic apps and synchronized with processes pre-configured systems Little external data  “Cloud” data services 12/7/2010 Copyright (c) David Stodder
  25. 25. Critical Trends in InformationManagement: Templates Rapid development: Using pre-built data warehouse models, often specific to industry or application Models and templates to improve consistency of implementation Example: IBM Delivery Accelerators: e.g., retail- specific template, dashboard and workbench accelerators, data models, development tools, processes, predictive modeling 12/7/2010 Copyright (c) David Stodder
  26. 26. Critical IM Trend: “Semantic”Integration Managed Centrally Relieve BI/analytics tools and users of having to define data types; reduce “what is a customer?” chaos Develop coordinated, accurate and stable business definitions and semantic meaning: Master data management Managing ETL processes more effectively to reduce cost and delay Improving data quality: BI fails without it! 12/7/2010 Copyright (c) David Stodder
  27. 27. Critical IM Trend: Pre-ConfiguredSystems and Appliances “Complex queries” – analytics – the most frequent reason organizations purchasing appliances and specialized databases (e.g., column-oriented) Pre-configured to speed deployment Tight integration Specialized for analytics Scalability IBM Power7 Systems 12/7/2010 Copyright (c) David Stodder
  28. 28. In-Memory, In-Database Analytics:Feeding the Need for Speed In-memory analytics: Bringing more power and flexibility to the user’s workstation In-database analytics: Using the database system to power analytics; e.g., SAS relationship with IBM Real-time “trickle” data feeds and analytics; “ELT” processing Embedding BI/analytics with processes 12/7/2010 Copyright (c) David Stodder
  29. 29. Polling Question #3What is your top priority for information management to support BI/analytics?- Deploying BI/DW appliances and pre-configured systems- Enabling information integration layer (including ETL, ELT, MDM) to support BI/analytics- Taking advantage of better hardware (e.g., virtualization, blades, faster chips)- Moving data warehouse systems to the cloud (public or private infrastructure as a service) 12/7/2010 Copyright (c) David Stodder
  30. 30. Analytics: Improving Outcomes“Simple” Analytics “Advanced” Analytics- BI “what-if” queries - Optimization- Accounting-oriented - Activity-based costing financial analysis modeling and analysis- Performance - Time series analysis management metrics and forecasting- Online analytical - Predictive analytics processing (OLAP) - Whatever those- Stuff nontechnical Ph.D.’s are doing users can do 12/7/2010 Copyright (c) David Stodder
  31. 31. Advanced Analytics: ToppingInformationWeek BI “Wish List” 3.8 on scale of 5 (“extremely interested”); one third rated it 5 Proactive objectives: anticipate demand to adjust pricing, manufacturing forecasts and supply chain planning Know what customers want before they ask for it – or go to a competitor Fraud example: Isolate the bad so that good claims are processed faster 12/7/2010 Copyright (c) David Stodder
  32. 32. Analytics: Case Example Infinity Property and Casualty: auto insurance for drivers who represent higher than normal risks and pay higher rates for comparable coverage Objective of speeding claims process and improve efficiency, while cutting fraud and improving customer satisfaction “Right-tracking”: claims profiled up front and sent to appropriate specialists based on claim characteristics 12/7/2010 Copyright (c) David Stodder
  33. 33. Infinity Property & CasualtyExample, Continued Predictive traits in claims modeled using IBM SPSS Able to address concerns beyond just fraud Six months to develop models and rules to integrate predictions into Infinity’s claim system Benefits: Used to take 40 days for claims to reach specialists; now takes 48 hours Success rate in proving fraud now 87%; company able to discontinue using third-party firm to handle collections ($12 mill/yr) 12/7/2010 Copyright (c) David Stodder
  34. 34. Predictive Analytics Objectives Data mining: discovery of previously undetected patterns and relationships in data Predictive analytics: applying historical patterns to predict future outcomes Statistics (e.g., regression); AI (e.g., neural nets); hybrid (e.g., decision trees); optimization (e.g., Monte Carlo simulation) Acknowledgements to Eric Siegel, Prediction Impact 12/7/2010 Copyright (c) David Stodder
  35. 35. Data Mining: CRISP-DM CycleSource: www.crisp-dm.org 12/7/2010 Copyright (c) David Stodder
  36. 36. Customer Analytics: Priority Use ofPredictive Analytics How to increase margin, not just sheer number of customers? What are the most effective metrics and indicators of customer attrition and acquisition? Predictors are linked directly to business strategy – to desired business outcomes Development of incentives programs aimed at the right customers 12/7/2010 Copyright (c) David Stodder
  37. 37. Analytics: Challenges People and politics: Will they trust the results, or go with the gut? Model development – combining predictors – can be slow, trial-and-error process; models must be kept up to date Structured data only half the story: Adding text analytics and mining to apply quantitative and linguistic analysis to words and sentiment 12/7/2010 Copyright (c) David Stodder
  38. 38. Proactive and Focused on BusinessOutcomes: BI and Analytics Together  Anticipate the future, plan how to act with consistency rather than case-by-case  BI: visualization, alerting and “simple” analytics – backed by more advanced analytics – to make information actionable  Linking performance management to analytics  Spreadsheets: Either making them more useful, or replacing them with better tools 12/7/2010 Copyright (c) David Stodder
  39. 39. Making Analytics Affordable: KeyTrends to Watch Labor and expertise are huge costs: Using templates for analytics and information management Industry models for rapid development Pre-configured appliances and scalable systems enable organizations to reduce time and cost Data services: Analytics in the cloud Embedding analytics in applications and processes 12/7/2010 Copyright (c) David Stodder
  40. 40. Best Practices for Analytics Start with your BI platform: Where are users bumping up against limits for understanding “unknowns”? The information management layer is critical: Analytics thrives on lots of data from multiple sources for correlation and pattern analysis Don’t delay: Predictive analytics becoming more mainstream; key to competitiveness 12/7/2010 Copyright (c) David Stodder
  41. 41. Best Practices: Going Forward Focus modeling on desired business outcomes Be patient with model development, and be prepared to update models continuously; evaluate industry models Customer analytics and fraud detection: where depth of experience is greatest Social network analysis and “Big Data”: valuable external sources, though in the realm of the “experts” for analytics use 12/7/2010 Copyright (c) David Stodder
  42. 42. Questions and AnswersChris MurphyEditor, InformationWeekDavid StodderAnalyst, Research and WriterPerceptive Information Strategies
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