This document discusses the data problem of vast amounts and rapid growth of data being generated daily from various sources. It is estimated that 2.5 quintillion bytes of data are created every day as of 2012. This data includes structured data like social media posts, photos, and videos uploaded, as well as unstructured data. The large volume and speed of data generation presents challenges and opportunities for businesses to gain insights through analytics to improve customer experience, business agility, and drive growth. However, properly addressing this big data problem requires the right technologies and solutions around areas like Hadoop, R, ETL, data warehousing, and driving analytics maturity.

1. What is the Data Problem
Vast Amount of Data
• As of 2012, every day
2.5 quintillion (2.5x108)
bytes of data were
created.
• 40% projected growth
in global data
generated per year vs.
5% growth in global IT
spending.
Speed at which
data is generated
• YouTube users upload 48
hours of new video every
minute of the day.
• According to Twitter’s
2012 research, it sees
roughly 175 million
tweets every day, and has
more than 465 million
accounts.
Types of structured and
unstructured data
• 2 million blog posts
every day , 250 million
photos are uploaded
to Facebook every day,
864,000 hours of video
are uploaded to
YouTube every day.
Quality of Data
•
Poor data can cost
businesses 20%–35%
of their operating
revenue. Bad data or
poor data quality
costs US businesses
$600 billion annually.

2. What is the opportunity with this Problem
Intelligence
Drive new insights
Customer
experience
Drive all Digital asset
behavior based on
segmentation & Machine
learning
Improve Business
Agility
Respond to complex
Business events
Drive Explosive
growth
Use information to run
targeted campaigns to
accelerate click to conversion

3. Myths and Realities about this problem

6. CAPITALIZING MEANS USING “BIG DATA” - BUT
WHAT IS IT?

7. Big Data
Size
Data
Hadoop
R

8. ETL
Data Warehouse

11. Because we loose money

14. c

15. Product
Recommendations
Odds recommendations
Predictions on todays
game

16. Ask the User
Inferences from Users Behavior

17. a
b
c
e
F
d
h
g
i

32. R includes…

35. The alternative to thinking ahead
would be to think backwards . . .
and that’s just remembering
Dr. Sheldon Cooper
The Big Bang Theory

36. Analytics Maturity
Performance
Analytics
Predictive
Analytics
Prescriptive
Analytics
Big Data Solutions drives Analytics Solutions Maturity

37. ETL
Data Warehouse

38. ETL
Data Warehouse
Business Critical