This document summarizes a presentation on business analytics and "big data" given by David Rogers. It discusses how descriptive, inquisitive, predictive, and prescriptive analytics are impacted by large datasets. Descriptive and some inquisitive analytics benefit from increased data availability, while predictive analytics and prescriptive/optimization techniques face new challenges in dealing with big data volumes. Formal education in analytics fields is needed to take advantage of opportunities from big data.

1. Four Analytics
Walk Into a Bar …
David F. Rogers
Department of Operations, Business Analytics, and Information
Systems
Carl H. Lindner College of Business

2. 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

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 Interpretation
 Make a Decision – Like Eating Mushrooms
– Some are Poisonous!
CBIG November 15, 2012 3

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?
CBIG November 15 2012 4

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 5

6. Data vs. Intuition…
CBIG November 15, 2012 6

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
CBIG November 15, 2012 7

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 8

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 9

10. Be Careful! – Popular
Infographics
CBIG November 15, 2012 10

11. 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

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 12

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 13

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 14

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 15

16. Smart Meter BIG DATA
 Model MPS Minimize ZMPS
Subject to
CBIG November 15, 2012 16

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 17

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
CBIG November 15 2012 18
Logic.

19. 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

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 20

21. 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

22. CBIG November 15, 2012 22

23. INFORMS Analytics Magazine
http://www.analytics-
magazine.org/
CBIG November 15, 2012 23

24. INFORMS CAP
CBIG November 15, 2012 24

25. INFORMS Analytics Section
CBIG November 15, 2012 25

26. 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

27. How Can We Work
Together?
 David.Rogers@UC.edu
 (513)556-7143
Thanks!!!
CBIG November 15, 2012 27