The document discusses key topics from IBM's Business Analytics Summit in Toronto in 2013. It outlines the four dimensions of big data: volume, velocity, variety, and veracity. It also discusses challenges organizations face in managing big data and key shifts driving the need for smarter analytics. Additionally, it provides examples of how leading organizations are using analytics to gain insights from data and outperform competitors. Finally, it briefly describes several IBM products for big data analytics.

1. What I learned from IBM Business
Analytics Summit, Toronto, April 2013
Yuxin Tu, Ph.D.
It’s All About Big Data

2. The Era of Big Data
Four dimensions of big data
 1) Volume
 2) Velocity
 3) Variety
 4)Veracity

3. 1) Volume
 Retail companies collect customer feedback
through online survey to improve their services
 Energy companies covert billions of annual meter
reading to better predict power consumption
 Banks, mobile phone companies are using social
media data to understand consumers’ behavior
patterns and factors influencing consumers to
make a purchase.

4. 2) Velocity
 Companies need to process data with a much
faster speed. For time-sensitive processes such
as catching fraud, credit card companies have to
react immediately to avoid possible loss.

5. 3) Variety
 Big data is any type of data - structured and
unstructured data such as text, audio, video, click
streams, log files and more. New insights are
found when analyzing these data types together.
 It requires analysts to be well-grounded in both
quantitative and qualitative analysis as well as
tech-savvy to analyze data from images, video,
and internet data such as Google analytics.

6. 4)Veracity
 1 in 3 business leaders don’t trust the information
they use to make decisions.
 How can you act upon information if you don’t
trust it?
 Establishing trust in big data presents a huge
challenge as the variety and number of sources
grows.

7. Big Data Challenges
57%
50%
45%
37%
22%
19%
0% 10% 20% 30% 40% 50% 60%
Lack of skills/expertise needed to run
analysis on all the data
Too difficult to access all data and
make available to users for analysis
Not effectively using our most
valuable data to drive decisions
Too difficult to analyze and understand
all of the data
Too difficult to share information and
insights with others
Running queries and reports takes too
long
SOURCE: IDG RESEARCH SERVICES, AUGUST 2012

8. Key Shifts are Fueling the Urgency
for Smarter Analytics
 The emergency of big data analytics
65% of business are not using big data for business
advantage
 Increasing consumer expectations
84% of consumer rely on social networks for purchase
decisions
 Accelerating pressure to do more with less
organizations using advanced analytics enjoy 32%
higher return on invested capital

9. “When it comes to benefits of big
data, not all industries are created
equal.”
Source: McKinsey Global Institute

10. Leading organizations are seizing the
opportunity
26%
26%
21%
54%
54%
57%
Access to data
Draw insights from data
Translate insight into action
Outperformers Underperfomers
Percent of CEOs who believe their
organization is good at driving value from
information
Source: IBM CEO
Study, 2012

11. Outperformers have a higher
analytics quotientStep1
Novice
Step2
Builder
Step3
Leader
Step4
Master
5% 64% 22%
9%
Manual, slow, erro
r
prone, cumbersom
e, fragmented
Data quality
concerns
Automated, instan
t, accurate, seamle
ss, converged
Data governance
is in place

12. IBM Products
 IBM Congnos Family (Demo: Congnos Enterprise
dashboards)
Interact with and explore data by creating customizable
dashboards
 IBM Social Media Analytics (Demo: Transform
Market research)
Gain powerful customer insights by uncovering customer
sentiment dispersed across countless online sources
 IBM SPSS Advanced Statistics/Modeler (Demo:
Student Retention)
Make smarter decisions, solve problems and improve
outcomes by predicting what will happen next