Four Analytics Walk Into a Bar …

David F. Rogers

Department of Operations, Business Analytics, and Information Systems

Carl H. Lindner College of Business

University of Cincinnati

CBIG November 15, 2012 2

Prof.Apply.Skeptic.Gadfly.Challenge.Create

BS Math/Business 1978 – Murray State RacersMBA Quantitative Methods 1980 – Murray StatePhD Mgmt. – Quant. Methods & Ops. Mgmt. 1986 –

Krannert School @ Purdue BoilermakersUC Bearcats Lindner College of Business 1985-on.

◦Optimization Modeling /Analysis º Stochastic Modeling◦ Intro. Bus. Analytics & OR º Statistics º

Clustering◦Case Studies in Business Analytics

Toyota DunnHumby LexisNexis Landrum&Brown 5th3rd

OMI FlaggsUSA Merchants Cold Storage Kroger GE Aircraft Engines Kenton Co. Airport Board – CVG …

Research – OR; Applied Optimization

CBIG November 15, 2012 3

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 InterpretationMake a Decision – Like Eating

Mushrooms – Some are Poisonous!

CBIG November 15 2012 4

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?◦Still Need the Experienced(?) Coach.

CBIG November 15 2012 5

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 6

Data vs. Intuition…

CBIG November 15, 2012 7

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

See….

CBIG November 15, 2012 8

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 9

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 10

Be Careful! – Popular Infographics

CBIG November 15, 2012 11

But Be VERY CAREFUL…Recording ErrorsEmployee SabotageComputer GlitchesJaded DataDirty LaundryIncomplete, 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 12

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 13

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 14

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 15

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 1

Building 2 kWh … Usage 93

CBIG November 15, 2012 16

Smart Meter BIG DATAModel MPS Minimize ZMPS

Subject to

CBIG November 15, 2012 17

1-Minute – 1,440 Time Periods

With Smart Meters, BIG DATA is Available and Much Finer than per Hour. 86,400=1Day

Hour Half-Hour Quarter-Hour 10-Min.

CBIG November 15 2012 18

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 Logic. Simulation Models Needed for More Scenarios. Be Careful of Over-Stated Models – Variable Interactions May

Exist. Current Softwares does Not Consider This.

CBIG November 15, 2012 19

Hierarchical PlanningWhat 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 20

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 21

U.C. Master of Science inBusiness 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 PhD in Business Analytics and Information Systems Also Available. Late Afternoon/Evening Classes.

CBIG November 15, 2012 22

CBIG November 15, 2012 23

INFORMS Analytics Magazine http://www.analytics-magazine.org/

CBIG November 15, 2012 24

INFORMS CAP

CBIG November 15, 2012 25

INFORMS Analytics Section

CBIG November 15, 2012 26

INFORMS LocallyCincinnati/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!◦Mostly MSBA-BA Students.◦Great for Meeting Them for Your Hiring

Purposes.

CBIG November 15, 2012 27

How Can We Work Together?

David.Rogers@UC.edu(513)556-7143

Thanks!!!