Presented by Daniel Ling and Magnus Ebbesson at Findability Day, 14th of June 2012 in Stockholm.

1. WHAT IS BIG DATA ?
Findability Day 2012, Stockholm 14th of June, by Daniel Ling and Magnus Ebbesson
© FINDWISE 2012

2. BIG DATA by Findwise!
• VOLUME
!!
• Sift through the noise to identify the right data to improve
business insight
• VELOCITY
• Analyse more data in less time to facilitate faster more
responsive business decision making
• VARIETY
• Identify, mine and capitalize on new data sources and integrate
them with existing data for deeper insights
• VISUALIZATION
• Present data in a meaningful and user friendly way to drive
better business decision across your organization

3. Big Data Dimensions !!

4. Big Data and Search!
Database Big Data tools Search!

5. Findability Usage!
Enterprise Search
Application or Nische
Info Hub and Big Data
• Findability
within
• Search
within
specific
• Indexing
and
Enterprise
Content.
applica=ons.
processing
of
internal
• Generic
search,
• Applica=on
may
be
and
external
data.
Intranets
etc.
desktop
client,
nische
• Search
and
aggregate.
portal
etc.
• Informa=on
hubs.
• Big
Data

6. Why Big Data – because of growth?"
• An""
estimated 90% of the world’s data (from the WWW and
machine generated data from network nodes and applications)
has been created over the past two year
• The data is doubling every two years and global annual data
creation is set to leap from 1.2 zettabytes in 2012 to 35
zettabytes in 2020 (IDC’s2011 Digital Universe Report)
• Walmart handles more than 1 million customer transactions
every hour
• Every day, we create 2.5 quintillion bytes of data
• Unstructured information is growing 15 times the rate of
structured information

11. DATA
TOOLS
VALUE

12. Big Data strategy – extract business value"
• Data as an asset - evaluate how the right data strategy will make
your business more agile, competitive and profitable
• Identify the business drivers in your data assets
• Start with a plan – understand the importance of devising a
viable and workable roadmap for your big datajourney
• Clarify your priorities – determine where big data analysis is
most needed now in your organisation
• Planning future success – using insights from big data to
increase the value of predictive analytics.

13. Big Data strategy – choose the right tools"
• Define which technology strategy will enable scalable, accurate,
and powerful analysis of the data
• Find out how to select the best big data solutions for your
specific business needs
• Discuss the key questions you need to be asking when
evaluating technology partners
• Determine what you want to get out of your big data
investments and how to communicate this to potential vendors

14. Use case: Insurance Industry"
• Analyzing both internal information in claims and databases,
combining it with external data from social media and third
parties etc.
• Processing both structured and semi-structured data in large
scale to find patterns.
• Example 1: A prospective policyholder with numerous speeding
tickets is more likely than a safer driver to end up with a sports
injury.
• Example 2: Publicly available social data will be increasingly
useful in helping insurers distinguish clients.
• Example 3: Mining Facebook and Twitter for promising sales
leads, example: a woman proud of her pregnancy might want to
buy life insurance.

15. Use case: Banking"
• Analyzing the customers
transaction data,
enabling visualizing and
search on the big data
sets.
• Enriching the
information: with geo
coordinates, transaction
category and other
metadata.

16. June 15, 2012