11.15.12 CBIG Event - David Rogers Presentation
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11.15.12 CBIG Event - David Rogers Presentation

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"Four Analytics Went Into A Bar..."

"Four Analytics Went Into A Bar..."

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11.15.12 CBIG Event - David Rogers Presentation 11.15.12 CBIG Event - David Rogers Presentation Presentation Transcript

  • Four Analytics Walk Into a Bar …David F. RogersDepartment of Operations, Business Analytics, and InformationSystemsCarl H. Lindner College of Business
  • Prof.Apply.Skeptic.Gadfly.Challenge.Create BS Math/Business 1978 – Murray State Racers MBA Quantitative Methods 1980 – Murray State PhD Mgmt. – Quant. Methods & Ops. Mgmt. 1986 – Krannert School @ Purdue Boilermakers UC Bearcats Lindner College of Business 1985-on. ◦ Optimization Modeling /Analysis º Stochastic Modeling ◦ Intro. Bus. Analytics & OR º Statistics º Clustering CBIG November 15, 2012 2
  • Traditional O.R. – BIG DATA, Big Help! “Life is the Art of Drawing Sufficient Conclusions From Insufficient Premises” Samuel Butler, English Composer, Novelist, & Satiric Author (1835 – 1902) Encounter a Problem or Opportunity… ◦ Qualitative Analysis Based on Management’s Experience and Judgment ◦ Quantitative Analysis Based on Data, Models, Analysis, and Interpretation Make a Decision – Like Eating Mushrooms – Some are Poisonous! CBIG November 15, 2012 3
  • Factual Data, Regardless of How BIG, Can’t Replace Informed Judgment… We Know Where the Crime is, but… ◦ How do We Best Modify Officer Assignments? ◦ How do We Respond to Immediate Changes in the Data? ◦ Still Need the Experienced(?) Captain. Player’s Points Scored. Sounds Simple. But… ◦ Per Game? Per Minute? ◦ Why Scored? Was the Best Point Guard Playing at the Time? CBIG November 15 2012 4
  • BIG DATA, Bigger Problems? Little Bit of Data Gone Awry can Damage Analysis. BIG DATA Collected Similarly Can Exacerbate That! P&G Outsourced Data Collection. ◦ Some Regrets About Losing Control of That. ◦ In-House Collection Can Also Be Problematic…  Data Collection from Dial Tones. CBIG November 15 2012 5
  • Data vs. Intuition… CBIG November 15, 2012 6
  • Four Analytics Walk Into a Bar The Four Analytic Characters … ◦ D – Descriptive Analytics – What Did Happen? ◦ I – Inquisitive Analytics – Why Did it Happen? ◦ P – Predictive Analytics – What Will Happen? ◦ P – Prescriptive Analytics – What Should We Do? D, I, P, and P Sip and Imbibe from … BIG DATA. How Well Do They Walk Out? Let’s CBIG November 15, 2012 7
  • D – Descriptive Analytics – What Happened? Just Give Me the Facts Ma’am… ◦ Frequencies, Minimums, & Maximums ◦ Mean, Medians, Modes, & Percentiles ◦ Standard Deviations & Ranges ◦ Skewness & Kurtosis ◦ Covariance & Correlation ◦ Confidence Intervals ◦ Bar/Pie Chart, DotPlot, Histogram, Ogive, Stem&Leaf, & CrossTabs ◦ Visually Supported Well is Quite Insightful. ◦ Academics Love This Development! CBIG November 15, 2012 8
  • D – Descriptive Analytics – What Happened? D Walks Out of the Bar On Steroids! Like Johnny Fever from WKRP in Cincy. This is Where BIG DATA Rocks. ◦ Computer Advances in Hardware & Software Make it…  Easier to Collect & Store Enormous Amounts.  Easier to Visualize & Present. ◦ Decomposable. ◦ Basic Statistics are More Understandable to the Masses. CBIG November 15, 2012 9
  • Be Careful! – Popular Infographics CBIG November 15, 2012 10
  • But Be VERY CAREFUL… Recording Errors Employee Sabotage Computer Glitches Jaded Data Dirty Laundry Incomplete, Missing, Contradictory, Confidential, and/or Ambiguous. Irrelevant Data: “There are Three Reasons Why I Can’t Do That. The First is That We Have No Money. And the Other Two Don’t Matter.” NYC Mayor Fiorello LaGuardia CBIG November 15, 2012 11
  • I – Inquisitive Analytics – Why Did it Happen? With Overwhelming BIG DATA, Some of these May Become Moot with Population Info. ◦ Sampling ◦ Confidence Interval Estimation ◦ Hypothesis Testing ◦ ANOVA Portion of I that Doesn’t Become Moot Walks Out of the Bar Neatly Tailored… ◦ More Sample Data Readily Available ◦ Higher Confidence Levels for Results CBIG November 15, 2012 12
  • P – Predictive Analytics – What Will Happen? P also Walks Out of the Bar Neatly Tailored. ◦ Regression Analysis & Prediction ◦ Forecasting Models ◦ Conjoint Analysis ◦ More Data to Choose From for More Various Model Choices. CBIG November 15, 2012 13
  • P – Prescriptive Analytics – What Should We Do? BIG DATA Can be Overwhelming & P Does Not Walk Out of the Bar! ◦ Optimization Routines Can Grind to a Halt. ◦ Linear Programming w/ Continuous Variables is OK. ◦ Integer Linear Programming – Mission Control We Have a Problem! ◦ Integer Nonlinear –Whoaaaa!!!! We are Often Grappling in the Dark! ◦ Challenges for Researchers  Better Algorithmic Methods  Better Computer Hardware CBIG November 15, 2012 14
  • Optimization Analysis … Problem Size & Solution Difficulty was Already Problematic Before BIG DATA Advent. After, It is More Pronounced… Example – Duke Provided Data & Wants to Cluster Time Periods for Differential Pricing. Hour 1 2 3 … 24 1Building 2 kWh … Usage 93 CBIG November 15 2012 15
  • Smart Meter BIG DATA Model MPS Minimize ZMPS Subject to CBIG November 15, 2012 16
  • 1-Minute – 1,440 Time PeriodsWith Smart Meters, BIG DATA is Available and Much Finer than per Hour. 86,400=1DayHour Half-Hour Quarter-Hour 10-Min. CBIG November 15, 2012 17
  • Simulation … AKA, “Anti-Statistics” … ◦ Statistics – BIG DATA Summarized with Few Numbers. ◦ Simulation – Few Input Nos. & Generates BIG DATA. Response to a Lack of BIG DATA – Generate it. BIG DATA Implications for Simulation … ◦ More Accurate Input Parameters.  Natural Increased Confidence Levels with BIG DATA.  Better Detailed Databases from Which to Choose Parameters. ◦ More Appropriate and Sophisticated Models.  Data Visualization Revelations Appended to Simulation CBIG November 15 2012 18 Logic.
  • Hierarchical Planning What Level of Data is Needed? ◦ Strategic – Corporate Level ◦ Tactical – Regional Level ◦ Operational – Plant Level ◦ Aggregation/Disaggregation Methods MIT Work … ◦ Hax and Meal, etc…. CBIG November 15, 2012 19
  • Formal Education is Needed! 2011 Study by McKinsey Global Institute Predicts a Shortfall of 140,000 to 190,000 “Deep Analytical Positions” in the United States by 2018. CBIG November 15, 2012 20
  • U.C. Master of Science in Business Administration (MSBA) Business Analytics Concentration ◦ Statistics º Simulation º Optimization ◦ Visual Basic, SAS, AMPL, GAMS, Arena, Matlab, … ◦ Capstone Experience is an Individual Project. Information Systems Concentration ◦ Data Visualization º Business Intelligence Project Management ◦ DataBase Design º Data Warehousing º Data Mining ◦ Text Mining º Enterprise Resource Planning (ERP) ◦ IBM SPSS Data Modeler, ERWin for Dimensional Modeling, SAP ◦ Capstone Experience is a Co-Op with Industry. Certificate in Business Analytics – Started Fall 2012- 13 http://business.uc.edu/future-students/graduate.html 2012 CBIG November 15, 21
  • CBIG November 15, 2012 22
  • INFORMS Analytics Magazine http://www.analytics- magazine.org/ CBIG November 15, 2012 23
  • INFORMS CAP CBIG November 15, 2012 24
  • INFORMS Analytics Section CBIG November 15, 2012 25
  • INFORMS Locally Cincinnati/Dayton Chapter of INFORMS ◦ Three+ Activities/Year  Summer Picnic at West Chester, OH  Autumn Speaker & Business Meeting  Spring Arnoff Lecture & Business Meeting at UC  Joining INFORMS? Please Join the Cin/Day Chapter Also! UC INFORMS Student Chapter ◦ We Want You to Come Speak to Our Students! CBIG November 15, 2012 26
  • How Can We Work Together? David.Rogers@UC.edu (513)556-7143Thanks!!! CBIG November 15, 2012 27