Big Data Mining and Predictive Analytics

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

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

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


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

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!

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


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

Transactions, Interactions & Observations

IASA May 2013 | 9

Images of Horses Can be Instructive

By changing the amount of data we change the essence of
what we see.

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

 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

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.


Big Data in the Gartner Hype Cycle

IASA May 2013 | 14

There are Less Scientific Hype Indicators

IASA May 2013 | 15

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

 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

 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

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

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!


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).


Some Patterns


Questions ?


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


In conclusion…


Contacts

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


Big Data Mining and Predictive Analytics

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