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On-Line Business Data Mining

On-Line Business Data Mining. David L. Olson University of Nebraska-Lincoln Current demand Our programs New World Order Innovation. Demand for Business Analytics. Capgemini [2012] 9 of 10 business leaders believe data is now the fourth factor of production

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On-Line Business Data Mining

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  1. On-Line Business Data Mining David L. Olson University of Nebraska-Lincoln Current demand Our programs New World Order Innovation 11th INFORMS Workshop on Data Mining & Decision Analytics 2016 Nashville

  2. Demand for Business Analytics • Capgemini [2012] • 9 of 10 business leaders believe data is now the fourth factor of production • As fundamental to business as land, labor, capital • Lee & Stewart [2012] (Deloitte) • Over 90% of Fortune 500 companies will start Big Data projects by the end of 2014 • By 2015 4.4 million IT jobs (globally) will be created to support big data • 1.9 million of these in the US (Gartner [2012]) • Gartner’s Peter Sondergaard • “There is not enough talent in the industry. Our public and private education systems are failing us.” • Data Scientist – IT plus statistics

  3. INFORMS Historical Evolution • Applied Mathematics to business decisions • At least the TIMS side • 1980s – DSS • INTERFACES articles all seemed to claim DSS • MIS might have quibbled • I agreed with INTERFACES • 2000s – Business Analytics • Seems to be THE marketing theme • I think appropriately

  4. Sathi (2012) • InternalCorporate data • Generated by e-mails, logs, blogs, documents • Business process events • ERP • External to firm • Social media • Competitor literature • Customer Web data • Complaints

  5. BIG DATA (Davenport, 2014) • Data too big to fit on single server • Too unstructured to fit in row-and-column database • Too continuously flowing to fit into static data warehouse • THE MOST IMPORTANT ASPECT IS LACK OF STRUCTURE, NOT SIZE • The point is to ANALYZE • Convert data into insights, innovation, business value • Waller & Fawcett (2013) • Shed obsession for causality in exchange for simple correlations • Not knowing why, but only what

  6. The Rise of Big Data: How It’s Changing the Way We Think About the WorldCukier & Mayer-Schoenberger, April 2013 • Massive data scale • After 2000, digitalization explosion • Over 98% of data now digital • Big Data – not just massive scale • Also includes ability to quantify almost everything • Datafication • Location datafied by GPS satellites

  7. Governmental & Non-Profit Examples • European & US food safety regulations • Need to monitor, gather data • Need to analyze • Hospitals • Biological data • Operational data • Insurance data • Schools • Government • Monitor Web site use • Monitor use of apps

  8. Contemporary Big Data Examples • Baseball • Moneyball • Flu detection • Google searches • Wal-Mart disaster relief • Hurricane Katrina • Pop-tarts & water

  9. Business Analytics (MIS view)Hugh Watson, BizEd May/June 2013 • DESCRIPTIVE – what has happened • OLAP • Dashboards • Scorecards • Data visualization • Association rules • Cluster analysis • Link analysis • (DIAGNOSTIC) – computer control • Sensors provide automatic data collection • Preprogrammed automatic control • PREDICTIVE– what will occur • Regression, factor analysis, neural networks • Demand forecasting • Customer segmentation • Fraud detection • PRESCRIPTIVE – what should occur • Revenue optimization

  10. Watson’s view of Business Analytic Jobs • Business Users • Graduating students apply tools • Do their jobs more intelligently • Business Analysts • Specialize in data analysis • Support others within their organizations • Operations research groups / MIS • Data Scientists • Specialize in data mining • R, Enterprise Miner, Intelligent Miner • Mathematical programming / simulation

  11. Recent Growth – Big 10 (as of summer 2014)Summer 2013 some, mostly retread OR classes

  12. MBA Certificate • GRADUATE • Any course can be used as an MBA elective • All four make a Business Analytics Specialization • Four course sequence (in order) for Certificate • Quantitative methods (renamed Business Analytics) • Some revision to refocus • Descriptive/Predictive/Prescriptive • Econometrics • Statistical tools (SAS) • Marketing Analytics • CRM (SAS, R) • Business Data Mining • Typical business applications (Prescriptive) • Standard tools (R, WEKA)

  13. Future Potential Paths • INFORMS Evolution • Extension of applied math/decision support/context of big data • MIS View • Database focus • Vendor view • Turban • Descriptive/Diagnostic/Predictive/Prescriptive paradigm • Statistics Perspective • Focus on econometrics • I remember when data mining was pejorative • Then statisticians got consulting money • Systems View • Decision making focus

  14. US • Great economic changes • Wages too high • Outsourcing • Computer programming (service) to India • Manufacturing to China • Technology • Robotics – no health benefits, no vacations, no complaints • Computers • ERP systems replacing multiple legacy systems • Layoff most human IT people • Business Analytics • BIG DATA

  15. JOBS • Tradable sectors have not been net job generators for 2 decades • Job creation exclusively within the nontradable sector • Wages held down by increasing displaced workers • As work stops chasing cheap labor, it will gravitate toward the final market • To shorten delivery time, reduce inventory, etc.

  16. The Coming Robot Dystopia:All too inhumanIllah Reza Nourbakhsh June 2015 • Transhumanism • Post-evolutionary transformation replacing humans with hybrid of man & machine • The Age of Big Data • Greater access to all kinds of information • Robotic technology • Increasingly more efficient than human labor • Can collect & interpret unprecedented amounts of data about human behavior • Threatens access to information • Threatens freedom of choice

  17. Will Humans Go the Way of Horses?Labor in the Second Machine AgeBrynjolfsson & McAfee, June 2014 • Society in a Labor-Tight Economy • Horses – by 1900 21 million horses & mules in the US • Internal combustion engine – by 1960 3 million horses • We face a similar tipping point • How to share gains • Control capitalist tendency toward greater inequality • While keeping ability to efficiently allocate resources

  18. The Fourth Industrial Revolution: What It Means and How to RespondKlaus Schwab – Executive Chairman – World Economic Forum • First Industrial Revolution • Water & Steam Power – mechanize production • Second • Electric Power – mass production • Third • Digital – fusion of technologies • Fourth • Billions connected by mobile devices • Velocity, scope, systems impact • Internet of Things • Autonomous vehicles • 3-D printing • Nanotechnology • Energy storage • Quantum computing

  19. Impact of Fourth Revolution • Might lead to greater inequality • Particularly in disruption of labor markets • Automation substitutes for labor • Increase gap between returns to capital, labor • Might also result in more safe & rewarding jobs • Demand for highly skilled increasing

  20. Main Effects of Fourth Industrial Revolution • Customer expectations • More focus on pleasing customers • Product enhancement • Enhanced by digital capabilities • More durable & resilient products • Data & analytics transform their maintenance • Collaborative innovation • Organizational forms • More flexible

  21. Erik Brynjolfsson and Andrew McAfee 2011 Digital Frontier Press Race Against The Machine: How the Digital Revolution is Accelerating Innovation, Driving Productivity, and Irreversibly Transforming Employment and the Economy • Computer progress advancing exponentially • AFFECT ON • Jobs • Skills • Wages • The Economy

  22. Big Data Changes • Collect a lot of data • Instead of sampling, use population • Don’t worry about data purity • Mass of data means errors offset • Correlation over Causation • Associations • Patterns • WHAT, not WHY

  23. New World Order:Labor, Capital, and Ideas in the Power Law EconomyBrynjolfsson, McAfee & Spence June 2014 • Technology has sped globalization • Leading to single large global market • Supply chains can move to labor’s location • About 1/3 of goods & services in advanced economies are tradable • This figure is rising • Spilling over to nontradable part of the economy

  24. Transformation of Goods & Services • LOW COST Free • RAPID UBIQUITY Now • PERFECT FIDELITY Perfect • Codification of processes • Leads to digitization • That allows replication at nearly zero cost • IMPACT • Abundance of consumer goods, labor, capital • Returns follow power law (a few make a lot)

  25. The Power of Market Creation:How innovation can spur developmentMezue, Christensen & van Bever Dec 2014 • INNOVATION • Sustaining innovation • Replace old products with new & better • Samsung – improved smartphone replaces older • Toyota - Prius replaces Camry • Keep markets vibrant & competitive • NO NEW JOBS

  26. INNOVATION • Efficiency innovation • Produce more for less • Wal-Mart • Lower prices • ELIMINATE JOBS

  27. INNOVATION 3 Market-creating innovation • Transform products & services cheap enough to REACH NEW POPULATION OF CUSTOMERS • Model T Ford • Personal computer • Smartphone • Online equity trading • Need to hire more people to make, distribute, service • Need new supply chain networks & distribution channels • NEW GROWTH – NEW JOBS

  28. INNOVATION INVESTMENT • Investment in resource industries • Efficiency innovations • Produce more with less • REDUCE EMPLOYMENT • Infrastructure investment • Efficiency • Limited to existing customers • Foreign direct investment • Usually to set up low-cost factory • Migratory

  29. MARKET-CREATING INNOVATIONS • Greater increase in jobs from growth than loss from efficiency gains • Wind energy? • But displaces coal employment • Solar energy? • Sustainable economically? • Moving people from current shorelines to higher ground? • RIGHT NOW WE DON’T KNOW WHAT WILL WORK • That’s what makes it innovative

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