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Business Intelligence vs. Business Analytics – How to Tell the Difference

Business Intelligence vs. Business Analytics – How to Tell the Difference . Kate Schwarz, SAS systems engineer. Agenda. What are BI and BA’s characteristics? What BA offers and why it’s critical for your business Illustrations & success stories.

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Business Intelligence vs. Business Analytics – How to Tell the Difference

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  1. Business Intelligence vs. Business Analytics – How to Tell the Difference

    Kate Schwarz, SAS systems engineer
  2. Agenda What are BI and BA’s characteristics? What BA offers and why it’s critical for your business Illustrations & success stories
  3. Business Intelligence or Business Analytics? What’s the difference? Business Intelligence Rear-view mirror Answers what happened? Tactical/departmental/siloed Pre-defined and canned Comparative and hunch-based Status quo Business Analytics Windshield and beyond Answers what’s next? Strategic/enterprise/holistic Discovery and ad hoc Scientific and research-based Market leading
  4. Do your reports look like this?
  5. Remember this?
  6. Do grade school mathematics run your business?
  7. This is BI
  8. How many organizations define and deploy BI Business Value Reporting / OLAP Data Management How Much? How Many? What Happened? Data Access Information Data Knowledge
  9. Simple yet useful questions… How many new customers did we gain & lose (by time or geography)? How much [product/service] did we sell during [timeframe]? What are most and least active sales channels? When are peak and valley transaction timeframes? Subcategory Week Month Time Year Product Company SKU Brand Country Category All Merchandise Location Region Zone Store
  10. This is business analytics
  11. Data Information Knowledge Intelligence Beyond Traditional Intelligence to Business Analytics Optimization Predictive Modeling What are likely outcomes? What are root causes? What’s the best that can happen? Forecasting Business Value Reporting / OLAP Data Management How Much? How Many? What Happened? Data Access
  12. BA asks more compelling questions How many new customers will we gain during the next [timeframe] by product, channel, location)? Why were more new customers gained/lost this month? How do we optimally communicate with customers? Which customers are likely to respond to a promotion and of those who respond, how much will they spend? Who are our most & least profitable customers? How do we attract and retain profitable customers? How to allocate marketing dollars to maximize profits? What factors affect staffing levels to meet swelling & shrinking customer service demands?
  13. FormulateQuestions MonitorResults Accumulate& Integrate Deploy& Evaluate DataQualityAnalysis Prediction & Analytics Transformand Select Query & Reporting The BA Cycle Team, Technologies, Techniques
  14. BA requires: Diverse data access & integration Comprehensive data assessment and cleansing Scalability
  15. BI Frequency Distribution
  16. BA Pattern Frequency Distribution
  17. BI Frequency Distribution
  18. BA Column/Variable Clustering
  19. BA requires: Going beyond query and reporting Combining operational data with unstructured data
  20. BI – query, reporting, graphing, charting
  21. BA – regression, multivariate, process control, forecasting
  22. BI – report & measure customers’ activities
  23. BI - Unifying Customer Interaction + Demographics
  24. BA - Unifying Customer Interaction + Demographics + Psychographics + Technographics
  25. BA – Social Analytics Identify roles within social groups: Influencers Followers Social Group Value Benefits Improve marketing effectiveness Increase retention Improve new services
  26. BA – Sentiment Analysis
  27. BA requires predictive analytics: Forecasting Segmentation Optimization
  28. Forecasting
  29. BI Line Plot
  30. BA Forecast
  31. Acquisition Provide appropriate benefits/loyalty programs, understand profile, look for similar fans Retention & churnprevention Loworno effortforretention Up-Selling Campaigns to increasespend & makesurefans are profitable BA – Segmentation IlustratedIdentifying high & low value customers, who’s likely to churn? High 5 2 7 Customer LTV 3 6 1 4 Low High Low Probability to Churn
  32. Business Analytics - Optimization Linear programming with constraints Simulation Often used to determine maximum profit, performance, or yield minimum loss, risk, or cost
  33. BI – report & measure customers’ purchases
  34. BA – optimization via price elasticity
  35. Deliver BA via BI Streamlined access to insightful reports Dashboards that tell the whole story Self service and fact-based decision empowerment
  36. This is BI
  37. This is business analytics
  38. Smiles designed and delivered. When we see a rose or a peony or a Peruvian lily, we don’t just see a gorgeous flower—we see an opportunity to help someone express a feeling: appreciation, condolences, gratitude, love. When someone gets our flowers, it puts a smile on their face. Benefits Challenges How? Reducing time and effort to make sure relevant offers are sent to the right customers. Limited analytics staff & skills. Achieve consistency across 15 different brands. predictive analytics, segmentation, sequencing Holistic view of customer’s attributes and behaviors Reduced operating expenses More innovations
  39. BA for your enterprise Supply & Demand Customers Demand Forecasting Satisfaction & Retention Inventory Optimization Cross-Sell/Up-Sell Capacity Planning and Prediction Price Optimization Spend Analysis Marketing Optimization Segmentation & Profiling Utilization Analysis & Reporting Service Operations Giving Program Analysis Performance Measurement & Reporting Service Provider Profitability Advocacy Mobilization Contact Center Analysis Workforce Planning & Management Fraud Analysis Regulatory Reporting & Compliance Green Initiatives/Sustainability Predictive Asset Maintenance
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