1. Big Data

2. Big Data – How Important is It?
The Economist Intelligence Unit –
“A game-changing asset”
World Economic Forum –
“Big impact”
Forbes –
“Big market and big business”
McKinsey & Company –
“The next frontier for innovation, competition, and productivity”

3. Big Data
Other descriptions of “Big Data”?
Examples of “Big Data”?
What is it?
Very large datasets
Datasets so large: difficult to process and analyze
According to a survey reported by CIO.com, 38% of director-level respondents worldwide don't know what BIG DATA is
Source:

4. Big Data
IOPS – Input / Output Processing per Second
Source:

5. Big Data
How "BIG" is it?

6. Nearly 3 Billion People will be online by 2015
BIG DATA
Comscore was capturing 1.4 trillion digital interactions per month by 2012
Nearly 3 Billion People will be online by 2015
Source: MGI: Big data: The next frontier for innovation, competition, and productivity, McKinsey & Company, 2011.

7. BIG DATA
Walmart stores more than 1 million transactions per hour in its 2.5 petabyte databases
90% of the world's data were created in the last 2 years
750 million Facebook users create an average of 90 pieces of content per month
294 billion s are sent per day
Source: The Economist Intelligence Unit; IBM

8. Business and Big Data
Credit card companies checking for fraudulent transactions - is a purchase legitimate?
Airlines monitoring the data from a plane traveling through the sky – are there any problems with the engine?
Companies monitoring social media for customer complaints and compliments – should we respond to customers on Twitter or Facebook?

9. Volume Velocity Variety
BIG DATA
The Three V’s of Big Data
Volume Velocity Variety

10. Volume One petabyte could hold more than 58,000 movies
Source:

11. Variety unstructured data text audio video click streams log files
Source:

12. Velocity 3
BILLION
Estimated number of phone calls on which the U.S. National Security Agency (NSA) collects data per day
Source:

13. BIG DATA

14. Making Sense of BIG DATA
CHALLENGES
Making Sense of BIG DATA
A visualization of thousands of Wikipedia edits that were made by a single software bot. Each color corresponds to a different page. Image: Fernanda B. Viégas, Martin Wattenberg, and Kate Hollenbach

15. A single version of the truth
CHALLENGES
A Single Version of the Truth

16. RESISTANCE Challenges
Why do some managers resist dealing with Big Data?

17. CHALLENGES

18. Unsupervised Learning
Solutions
Cluster Analysis
Classification
Crowdsourcing
Simulation
Pattern Recognition
Supervised Learning
Neural Networks
Machine Learning
Unsupervised Learning

19. SOLUTIONS
Open Source Software for storing and processing large volumes of unstructured and semi-structured data.
Can process and analyze huge amounts of unstructured data – video, audio, social media postings, images, etc.
Allows large data sets to be processed across clusters of computers.
Uses parallel computing power to analyze data with incredible speed, compared to traditional data analysis.

20. BIG DATA Management SOLUTIONS Evolving Strategic Data Managers
Data Wasters
companies that collect data but severely underutilize it
Data Collectors
companies that collect a large amount of data but do not consistently maximize their value
Aspiring Data Managers
companies that understand the value of data and are marshaling resources to take better advantage of them
Strategic Data Managers
companies that have well-defined data management strategies that focus resources on collecting and analyzing the most valuable data
Source: The Economist Intelligence Unit

21. Next
RFID…