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Business Analytics

Business Analytics. “The extensive use of data, statistical and quantitative analysis, explanatory and predictive models, and fact-based management to drive decisions and actions .” Davenport and Harris (2007) Competing on Analytics: The New Science of Winning.

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Business Analytics

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  1. Business Analytics

  2. “The extensive use of data, statistical and quantitative analysis, explanatory and predictive models, and fact-based management to drive decisions and actions.” Davenport and Harris (2007) Competing on Analytics: The New Science of Winning

  3. “The essence of analytics lies in the application of logic and mental processes to find meaning in data.” Back in Business, by Ronald K. Klimberg and Virginia Miori, OR/MS Today, Vol 37, No 5, October 2010, [http://www.informs.org/ORMS-Today/Public-Articles/October-Volume-37-Number-5/Back-in-Business]

  4. analytics • Big Data • Big Money • Big Change • Big Benefits • Big Demand

  5. Data Explosion http://tedxtalks.ted.com/video/TEDxPhilly-Robert-J-Moore-The-d

  6. Competing on Analytics by Thomas Davenport, HBR (January 2006)

  7. http://www.moneyball-movie.com/ trailer

  8. http://movies.yahoo.com/feature/moneyball.html Competing on Analytics by Thomas Davenport, HBR (January 2006)

  9. Management Changes in the Analytical Landscape Historically… Models Analytical Modelers Historically, analytics have typically been handled in the “back office,” and information was shared only by a few individuals. SAS, Advanced Business Analytics Course

  10. OPERATIONS CustomerService Retail Logistics Promotions Changes in the Analytical Landscape TARGET Now… Customers Analytical Modelers Proliferation of Models Suppliers Now analytics are being pushed out to the “front office” and are directly impacting company performance. There are clear, tangible benefits that management will track. Data mining is a critical part of business analytics. Employees Stockholders SAS, Advanced Business Analytics Course

  11. The Data Experimental Opportunistic Purpose Research Operational Value Scientific Commercial Generation Actively Passively controlled observed Size Small Massive Hygiene Clean Dirty State Static Dynamic SAS, Advanced Business Analytics Course

  12. The Methodology: What We Learned Not to Do • Prediction is more important than inference. • Metrics are used “because they work,” not based on theory. • p-values are rough guides rather than firm decision cutoffs. • Interpretation of a model might be irrelevant. • The preliminary value of a model is determined by its ability to predict a holdout sample. • Long-term value of a model is determined by its ability to continue to perform well on new data over time. • Models are retired as customer behavior shifts, market trends emerge, and so on. SAS, Advanced Business Analytics Course

  13. Using Analytics Intelligently • Intelligent use of analytics results in the following: • Better understanding of how technological, economic, and marketplace shifts affect business performance • Ability to consistently and reliably distinguish between effective and ineffective interventions • Efficient use of assets, reduced waste in supplies, and better management of time and resources • Risk-reduction via measurable outcomes and reproducible findings • Early detection of market trends hidden in massive data • Continuous improvement in decision making over time SAS, Advanced Business Analytics Course

  14. Decision Optimization What is the best decision? PredictiveModeling What will happen next? Forecasting What if these trends continue? Competitive Advantage Basic Statistical Analysis Why is this happening? Reporting with Early Warning What actions are needed? Dynamic Reporting Where exactly are the problems? Ad Hoc Reporting How many, how often, where? Basic Reporting What happened? SAS, Advanced Business Analytics Course Achieving Success with Analytics Advanced Analytics Basic Analytics Reporting Data Intelligence Information Decision Support Decision Guidance

  15. Hiring Success

  16. QUAN 4630: Business Analytics

  17. Opportunistic Data • Operational data is typically not collected with data analysis in mind. • Multiple business units produce a silo-based data system. • This makes business analytics different from experimental statistics and especially challenging. SAS, Advanced Business Analytics Course

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