Business Analytics and Implications for Applied Statistics Education
Sam Woolford from Bentley University discusses the historical context, requirements, and implications of business analytics for applied statistics education, highlighting the use of data, statistical analysis, and predictive modeling in decision-making processes in the business world.
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Presentation Transcript
BUSINESS ANALYTICS AND IMPLICATIONS FOR APPLIED STATISTICS EDUCATION Sam Woolford, Bentley University 2016 Joint Statistical Meetings Chicago, IL 1 August 4, 2016 2016 JSM Chicago, IL
OUTLINE Defining Business Analytics Historical Context Business Analytics Requirements Implications for Applied Statistics Education Discussion 2 August 4, 2016 2016 JSM Chicago, IL
WHAT IS BUSINESS ANALYTICS? Is it Applied statistics? Data science? Statistical engineering? Machine learning? Operations research? Who had heard of Business Analytics ten years ago? 3 August 4, 2016 2016 JSM Chicago, IL
WHAT IS BUSINESS ANALYTICS Wikipedia Business analytics makes extensive use of data, statistical and quantitative analysis, explanatory and predictive modeling to drive decision making. Translation (from a business perspective) What happened? Why is this happening? What if the trend continues? What will happen next? What is the best that can happen? 4 August 4, 2016 2016 JSM Chicago, IL
HISTORICAL CONTEXT Analytics has a long history in business Frederick Taylor in the 18th century Quality Management Focus on process and customer Reengineering Added a systems component to attain higher performance Enterprise data management ERP and CRM systems 5 August 4, 2016 2016 JSM Chicago, IL
HISTORICAL CONTEXT Data, Data, Data Traditional structured data New unstructured data Big data Business issues Complexity of global business Compressed decision time frames The HIPPO is dead Business Analytics 6 August 4, 2016 2016 JSM Chicago, IL
REQUIREMENTS FOR BUSINESS ANALYTICS Problem definition Requires business and context knowledge Requires quantitative frameworks Problem complexity Interdisciplinary Multiple stakeholders Requires multiple interconnected analyses Messy data doesn t conform to assumptions Unknown sources of variation 7 August 4, 2016 2016 JSM Chicago, IL
REQUIREMENTS FOR BUSINESS ANALYTICS Analytics frameworks Also multidisciplinary Belts and suspenders Ancillary issues Team and project management Communications Innovation and creativity 8 August 4, 2016 2016 JSM Chicago, IL
IMPLICATIONS FOR MASTERS PROGRAMS Typical MS in Applied Stat Courses (30-33 hours/10-11 courses) Background courses (Probability and Math Stat) Statistical Methods (Linear models, Multivariate) Statistical computing/data management Topic areas (time series, DOE, Bayesian analysis, stochastic processes) Machine learning Big Data Duration (1.5 to 2 yrs) Shorter time frames being driven from students and competitive pressures 9 August 4, 2016 2016 JSM Chicago, IL
IMPLICATIONS FOR MASTERS PROGRAMS Ancillary skill requirements Business understanding is important Capabilities beyond statistical methodology Multiple analyses required for complex problems New career paths for applied statisticians Data Scientist, Chief Data Officer, VP Big Data, Chief Analytics Officer, Director Decision Science Analytical career paths may more closely parallel existing corporate career paths 10 August 4, 2016 2016 JSM Chicago, IL
IMPLICATIONS FOR MASTERS PROGRAMS No one owns the discipline ASA INFORMS Others (big data) Issues for statistics departments Lack of business orientation Methodology oriented as opposed to case oriented Requires coordination with other departments Opportunity to enhance stature and relevance of statistics 11 August 4, 2016 2016 JSM Chicago, IL
Discussion Contact: swoolford@bentley.edu 12 August 4, 2016 2016 JSM Chicago, IL
