This session will cover issues and and advice for implementing Big Data Analytics in a Research and Development context. In addition to the basics, it will discuss the past, present and future and touch on relevant mathematics, statistics, science, technology, economics, business, history and even some literature. For more information on the Loras College 2014 Business Analytics Symposium, the Loras College MBA in Business Analytics or the Loras College Business Analytics Certificate visit www.loras.edu/mba or www.loras.edu/bigdata.

1. Big Data Analytics, R&D
Robert Andrew Stevens, CFA
John Deere

2. Disclaimer
The information, views, and opinions
contained in this presentation are those of
the author and do not necessarily reflect the
views and opinions of John Deere

3. Outline = Favorite Quotes
1. ―when you cannot express it in numbers, your knowledge is of
a meagre and unsatisfactory kind‖
2. ―it takes all the running you can do, to keep in the same place‖
3. ―The future is already here – it’s just not evenly distributed‖
4. ―The essence of strategy is the timing of the sunk cost
commitment‖
5. ―Americans can always be counted on to do the right thing...‖

4. ―when you cannot express it in numbers, your
knowledge is of a meagre and unsatisfactory kind‖
―I often say that when you can measure what
you are speaking about, and express it in
numbers, you know something about it; but
when you cannot express it in numbers, your
knowledge is of a meagre and unsatisfactory
kind; it may be the beginning of
knowledge, but you have scarcely, in your
thoughts, advanced to the stage of
science, whatever the matter may be.‖
Lecture on ―Electrical Units of Measurement‖ (3 May
1883), published in Popular Lectures Vol. I, p. 73;
quoted in Encyclopaedia of Occupational Health and
Safety (1998) by Jeanne Mager Stellman, p. 1992http://en.wikiquote.org/wiki/William_Thomson
http://en.wikipedia.org/wiki/Lord_Kelvin
William Thomson, 1st Baron Kelvin
1824–1907
a.k.a.: Lord Kelvin
Occupation: mathematical
physicist and engineer

5. What is Analytics?
Turning Data into Decisions
Production, Assembly, Inspection
Distribution
Consumers
Consumer
Research
Design
and
Redesign
Receipt and
Test of
Materials
Tests of Process,
Machines, Methods,
Costs
Suppliers of
Materials and
Equipment
* Deming, W.E. Out of the Crisis,1986 (p. 4)
Production Viewed as a System *
Take Action!

6. The Road to Earlier Discovery and
Shorter Decision Cycles

7. Big Data in R&D at John Deere
Primarily machine data: CAN and GPS
 Volume: immeasurable
 Velocity: fast and furious
 Variety: nothing is the same
 Value: TBD

8. ―it takes all the running you can
do, to keep in the same place‖
The Red Queen's race is an incident that
appears in Lewis Carroll's Through the
Looking-Glass and involves the Red Queen, a
representation of a Queen in chess, and Alice
constantly running but remaining in the same
spot.
―Well, in our country,‖ said Alice, still panting a
little, ―you'd generally get to somewhere else — if
you run very fast for a long time, as we've been
doing.‖
―A slow sort of country!‖ said the Queen.
―Now, here, you see, it takes all the running you can
do, to keep in the same place. If you want to get
somewhere else, you must run at least twice as fast
as that!‖
http://en.wikipedia.org/wiki/Red_Queen's_race
http://en.wikipedia.org/wiki/Lewis_Carroll
Charles Lutwidge
Dodgson
1832–1898
Pen name: Lewis Carroll
Occupation:
Writer, mathematician, Anglic
an cleric, photographer, artist

9. The Problem/Opportunity
Data
generated
Data
analyzed
Data captured and
stored
[Remember: DIKW = Data  Information  Knowledge  Wisdom ?]

10. Ideally, if nothing changes…
Today Transition Vision

11. But the data generated might grow
faster than we can manage
[Ever hear of ―The Internet of Things‖ ?]
Today Transition Vision

12. So, maybe we should try to do
something like this…
[―If you want to get somewhere else, you must run at least twice as fast as that!‖]
Today Transition Vision

13. A Solution: Data Science
• Applies everywhere
• Practical/feasible?
• In R&D?
http://www.dataists.com/2010/09/the-data-science-venn-diagram

14. Data Science in R&D
1. Multidisciplinary Investigations (25%)
2. Models and Methods for Data (20%)
3. Computing with Data (15%)
4. Pedagogy (15%)
5. Tool Evaluation (5%)
6. Theory (20%)
Data Science: An Action Plan for Expanding the Technical Areas of the Field of Statistics , ISI Review, , 69, 21-26. W. S. Cleveland, 2001.
http://www.stat.purdue.edu/~wsc/papers/datascience.pdf

15. ―The future is already here – it’s just not evenly
distributed‖
— William Gibson, quoted in The Economist, December 4, 2003
http://www.economist.com/printedition/2003-12-06
http://en.wikipedia.org/wiki/William_Gibson
William Gibson
1948–

16. CERN: Solving the Mysteries of the
Universe with Big Data
The Large Hadron Collider Computing
Challenge
• Data volume
– High rate large number of channels 4
experiments
– 15 PetaBytes of new data each year  30 PB in
2013
• Overall compute power
– Event complexity Nb. events thousands
users
http://openlab.web.cern.ch/sites/openlab.web.cern.ch/files/presentations/Jarp_Big_Data_Boston_final.pdf (09/12/13)

17. The Scientific Method
1. Formulation of a question
2. Hypothesis
3. Prediction
4. Testing
5. Analysis
http://en.wikipedia.org/wiki/Scientific_method
An 18th-century depiction of early
experimentation in the field
of chemistry

18. ―The essence of strategy is the timing of the sunk
cost commitment‖
Verbal communication during UIUC MBA Strategic Management class
http://www.amazon.com/Economic-Foundations-Strategy-
Organizational-Science/dp/1412905435
http://business.illinois.edu/facultyprofile/faculty_profile.aspx?ID=99
Professor of Business Administration and
Caterpillar Chair of Business
University of Illinois at Urbana-
Joseph T. Mahoney
1958–

19. What happens to Q as P  0?
• Change ―Household‖ to
―Firm‖
• Change ―chocolate‖ to
―software‖
• Now what happens to Q as
P  0?
• How could that happen in a
Big Data Analytics, R&D
context?http://catalog.flatworldknowledge.com/bookhub/reader/2992?e=coopermicro-ch07_s01
Figure 7.1 The Demand Curve of an Individual
Household

20. The One-Day MBA
http://www.engineeringtoolbox.com/cash-flow-diagrams-d_1231.html
http://en.wikipedia.org/wiki/Net_present_value
F0 = Sunk cost investment
• Assuming Ft does not
decrease* for t > 0, what
happens to NPV as F0  0?
• How could that happen in a
Big Data Analytics, R&D
context?
• What are the implications
for strategy?

21. Avoid Sunk Cost Commitments and
Vendor Lock-in with Open Source
• Apache: http://www.apache.org/
– Hadoop, Hive, Mahout, Pig, Spark…
• GRASS GIS: http://grass.osgeo.org/
• Java: http://www.java.com/ + Cassandra
• Julia: http://julialang.org/
• Perl: http://www.perl.org/
• Python: http://www.python.org/
• R: http://cran.us.r-project.org/ + RHIPE
• Scala: http://scala-lang.org/ + Scalding
• SQL:
– http://www.mysql.com/
– http://www.postgresql.org/ + PostGIS

22. ―Americans can always be counted
on to do the right thing...‖
―...after they have exhausted all
other possibilities.‖
Also famous for:
 ―We shall never surrender‖
 ―peace in our time‖
And many others relevant to The War on Data
http://www.quotedb.com/quotes/2313
https://en.wikipedia.org/wiki/Winston_churchill
Sir Winston Churchill
1874–1965
Profession: Member of
Parliament , statesman, soldier,
journalist, historian, author,
painter

23. Tips for winning The War on Data
Teamwork
Statistics
Partner with IT
Learn-Do-Teach
Replenish your toolbox
Math

24. Pop Quiz
What are the 3 most important things in Real Estate?
1. Location
2. Location
3. Location
What are the 3 most important things in Statistics?
1. Look at the data
2. Look at the data
3. Look at the data
… especially for Big Data Analytics:
1. Look at the data before you analyze it: Exploratory Data Analysis (EDA)
2. Look at the data while you analyze it: model diagnostics
3. Look at the data after you analyze it: visualization and communication

25. Other Survival Tips
• Visualization and Communication
– Tools: R & Rmd, Ggobi, Tableau, ArcGIS/GRASS…
– Presentations: Tell them 3X, 5Ws
• Collaboration: working as a team
– File and code version control
– Google's R Style Guide
• Reproducible Research best practices
– Avoid errors by Potti (Duke) and Rogoff & Reinhart (Harvard)
• http://en.wikipedia.org/wiki/Anil_Potti
• http://en.wikipedia.org/wiki/Reinhart-Rogoff

26. Summary = Favorite Quotes
1. ―when you cannot express it in numbers, your knowledge is of
a meagre and unsatisfactory kind‖
2. ―it takes all the running you can do, to keep in the same place‖
3. ―The future is already here – it's just not evenly distributed‖
4. ―The essence of strategy is the timing of the sunk cost
commitment‖
5. ―Americans can always be counted on to do the right thing...‖
―Those who cannot remember the past are condemned to repeat
it.‖
– George Santayana

27. Q & A

28. Contact Information
E-mail:
stevensroberta@johndeere.com (business)
robertandrewstevens@gmail.com (personal)
LinkedIn: http://www.linkedin.com/pub/robert-
andrew-stevens-cfa/6a/a04/315
Twitter: https://twitter.com/RobertAndrewSt3
GitHub: https://github.com/robertandrewstevens