The document discusses big data and data mining tools. It provides an agenda for a session that will explore big data tools and demonstrate their use in identifying patterns and predicting risks. The session aims to show how these analytics can help organizations identify areas of elevated risk and help plan mitigation strategies. A live demo is presented analyzing patterns in Facebook likes to predict attributes about users. The document concludes that big data tools are now widely available and can provide organizations a competitive advantage through predictive analytics.

1. Big Data
What does it mean?
How do we mine it?
May 2013

2. Session Objective
Who saw that coming!
Identifying patterns of risk performance is critical to anticipating and planning
for the unexpected.
•
•
Powerful tools are emerging to help identify patterns and make predictions of potential risks.
These predictive analytics will allow companies to focus on the real trouble spots and
develop the right conclusions.
This session will explore and demonstrate these tools while identifying potential
applications and solutions in our day-to-day lives.
At the conclusion of the session participants will understand
•
•
The role that analytics can play in our organizations.
How these tools can identify elevated risk and help plan effective mitigation strategies.
© 2013 Agile Technologies, LLC

3. Agenda
 Big Data Explained
 A Real Live Demo of a Mining
Process
 Why Are These Solutions
Emerging Now ?
 Questions
 Key Players in the Market
 Wrap - Up
 Some Common and Not-so
Common Applications in our
Businesses
 The Steps in the Mining Process
© 2013 Agile Technologies, LLC

4. But First… a Poll Question!
 Which of the following is NOT associated with having a
high IQ?
A. Curly Fries
B.
The Colbert Report
C.
Lord of the Rings
D.
Science
E.
VW Jettas
© 2013 Agile Technologies, LLC

5. But First… a Poll Question!
 Which of the following is NOT associated with having a
high IQ?
A. Curly Fries  Top Answer
B.
The Colbert Report
C.
Lord of the Rings
D.
Science
E.
VW Jettas  Correct Answer!
© 2013 Agile Technologies, LLC

6. A Definition ?
“Big data refers to things one can do at a large scale that
cannot be done at a smaller one, to extract new insights or
create new forms of value in ways that change markets,
organizations, the relationship between citizens and
governments, and more”
Viktor Mayer-Schonberger
Kenneth Cukier
Big Data
© 2013 Agile Technologies, LLC

7. A Definition ?
“Big data refers to things one can do at a large scale that
cannot be done at a smaller one, to extract new insights or
create new forms of value in ways that change markets,
organizations, the relationship between citizens and
governments, and more”
Viktor Mayer-Schonberger
Kenneth Cukier
Big Data
© 2013 Agile Technologies, LLC

8. Big Data Explained
RF Tagging
Unstructured Data
Healthcare Monitoring
Variety
Sensor Data
> Peta? Zetta
Velocity
Voice of the Customer
Real Time Data
Text Analysis
Volume
IASA May 2013 | 8
© 2013 Agile Technologies, LLC

9. Transactions, Interactions & Observations
IASA May 2013 | 9
© 2013 Agile Technologies, LLC

10. Images of Horses Can be Instructive
By changing the amount of data we change the essence of
what we see.
© 2013 Agile Technologies, LLC

11. Another Poll Question!
 Which of the following is NOT associated with being in a
relationship?
A. Weight Watchers
B.
Scrapbooking
C.
Huggies
D.
I love My Husband
E.
Golden Retrievers
© 2013 Agile Technologies, LLC

12. Another Poll Question!
 Which of the following is NOT associated with being in a
relationship?
A. Weight Watchers  Top Answer
B.
Scrapbooking
C.
Huggies
D.
I love My Husband
E.
Golden Retrievers  Correct Answer
© 2013 Agile Technologies, LLC

13. Real World Examples
 Google and the Flu
• Google receives 3billion searches a day.
• They took the 50m most common search terms people
type, and compared that to CDC data on the spread of
the flu.
• Their solution compared search terms to where the CDC
said the flu was spreading.
• They identified 45 search terms that when their use
spiked it reliably predicted the flu in an area. In real
time.
© 2013 Agile Technologies, LLC

14. Big Data in the Gartner Hype Cycle
IASA May 2013 | 14
© 2013 Agile Technologies, LLC

15. There are Less Scientific Hype Indicators
IASA May 2013 | 15
© 2013 Agile Technologies, LLC

16. Why Now?
• Chances are, the tools you need are the tools you have.*
• At long last, the data that you need is the data that you have.
• The processing power that you need is the processing power that
you have.*
* And if you don’t already have them, they are
readily available with very reasonable ROI
IASA May 2013 | 16
© 2013 Agile Technologies, LLC

17. Another Poll Question!
 Which of the following is NOT associated with being
single?
A. Applebee’s
B.
Hunger Games
C.
Usain Bolt
D.
Maria Sharapova
E.
Sonny with a Chance
© 2013 Agile Technologies, LLC

18. Another Poll Question!
 Which of the following is NOT associated with being
single?
A. Applebee’s  Top Answer & Correct Answer
B.
Hunger Games
C.
Usain Bolt
D.
Maria Sharapova
E.
Sonny with a Chance
© 2013 Agile Technologies, LLC

19. Data Is Increasingly A Differentiator
High
Predictive Analytics
Differentiation
Operational Analytics
Data Discovery
Interactive Dashboards
Production Reporting
Low
Low
Sophistication
High
IASA May 2013 | 19
© 2013 Agile Technologies, LLC

20. What is with the Poll Questions?
 Earlier this year the Wall Street Journal ran a story about a study
reported in the Proceedings of the National Academy of Sciences.
“Patterns of “Likes” posted by people on Facebook can unintentionally
expose their political and religious views, drug use, divorce and sexual
orientation.”
 58,000 people offered to participate in the study and share their
demographic information and their “Likes.”
 Which leads us to our demonstration!
© 2013 Agile Technologies, LLC

21. Demonstration
 The reporters used big data tools to find out the patterns of Facebook
“Likes.”
 We are using a smaller dataset, but a similar set of tools. These tools
are available to any Facebook user.
 We are going to look at three sets of data, and see what relationships
exist among the “Likes” of my friends (anonymously of course).
© 2013 Agile Technologies, LLC

22. Some Patterns
© 2013 Agile Technologies, LLC

23. Some Patterns
© 2013 Agile Technologies, LLC

24. Some Patterns
© 2013 Agile Technologies, LLC

25. Questions ?
© 2013 Agile Technologies, LLC

26. In conclusion…
 Big Data Explained
 A Real Live Demo of a Mining
Process
 Why Are These Solutions
Emerging Now ?
 Questions
 Key Players in the Market
 Some Common and Not-so
Common Applications in our
Businesses
 The Steps in the Mining Process
© 2013 Agile Technologies, LLC

27. In conclusion…
© 2013 Agile Technologies, LLC

28. Contacts
John Johansen
Partner
Agile Technologies, LLC
685 Route 202/206
Bridgewater, NJ 08807
jjohansen@agiletech.com
www.agiletech.com
© 2013 Agile Technologies, LLC