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Big Data and the BI Wild West: Don’t Bring an Elephant to a Gun Fight
March 2013
Michael HiskeyHead Product Evangelist
Big Data and the BI Wild West
Are you Packin’ ?
Got mobile?
200 millionEmployees bring their own
device to work
Nearly halfOf the workforce will be made
up of millennials by 2020
50%Companies BYOD orgs have
had a security breach
1/3Have broken or would break corporate policy on BYOD
BI Wild WestBI Wild WestBI Wild WestBI Wild West
Data ?
Disruptor:
Disruptor: Social Media & Sentiment
Characteristics of Big Data
Respondents were asked to choose up to two descriptions about how their organizations view big data from the choices above. Choices have been abbreviated, and selections have been normalized to equal 100%. n=1144
Source: IBM Institute for Business Value/Said Business School Survey
What? New value comes from your existing data
What has changed?More
connected-users?
More-connected users?
How are you really judged?
• Fast?• Consistent?• All users?
TRA/Virgin Confidential | 13
TRA Confidential Copyright 2012 TiVo Research and Analytics, Inc. 13TRA Confidential Copyright 2012 TiVo Research and Analytics, Inc. 13
Case Study #1Deep Dive Analytics on Big Media Data - monetize data and gain customer insight
TRA/Virgin Confidential | 14
Demographics don’t buy productsDemographics
TRA/Virgin Confidential | 15
DIMINISHING EFFECT OF ADVERTISING
Reach
Cost
TV’s $70 Billion (US) = Advertising Challenge
� Diffused audiences:
� Over 100 Channels access in average home
� Broadcast Network Rating -8% vs Y-Y
� Clutter & Consumer Control:
� >5000 brands on TV
� Fickle Consumers watching on more screens
� +14.7% Watching Timeshifted TV
� +5.9% Watching Video on Internet
TRA/Virgin Confidential | 16
TRA adds the missing element in the TV buying
and selling system: Consumer Purchase Behavior
TRA/Virgin Confidential | 17
TiVo – TRA Clients
ROI + 25%
improved ROI 81%
TRA/Virgin Confidential | 18
� Tens of Billions of interactions/events
� Few opportunities for summarization (demographics, purchaser targets)
� Needed reports to run fast (competitors too slow)
� Performance had to be predictable
� New data sources being added
� Cost: Hardware & Personnel
The Technical Challenge
TRA/Virgin Confidential | 19
Kognitio powers the TRA advantage
� Analytics on tens of billions of events in
seconds withNO DBA
� Massive cross-correlation of data
� 25 data sources and counting
� Continuous growth and innovation
� Partnership from Kognitio Analytics Center of
Excellence
� Bringing big data into context for media
analytics
LOYALTYANALYTICS
Case Study # 2
REVOLUTION IN RETAILING HAS CHANGED THE RELATIONSHIP WITH THE CUSTOMER
Data is the raw material of the modern service economy.
To remain competitive, companies need to:
• Extract data from their operations
• Refine data into insight
• Deliver the insight to where it matters
DATA IS THE NEW OIL
RETAILERS EMBRACE SHOPPER CENTRIC RETAILING
LEVERAGE YOUR SUPPLIERS
SHAPE THE PERSONAL
EXPERIENCE
SHOPPER SEGMENTATION &
STRATEGY
SHAPE THE STORE
EXPERIENCE
MANAGE YOUR MEDIA
SHOPPER DATA
SHOPPER INSIGHT
PROFILING & CROSS SHOPPING
• Focus on key customers
• Provide broad product offer for all customer segments
• Profile customers based on geography, lifestage, and
other segments
• Where to place
product in store
• What to group
into multi-buy
promotions
PRODUCT ASSOCIATION & REPEAT PRUCHASE
• Build bespoke segmentations
based on product
• Determine product loyalty by
customer groups
• Who are the biggest spenders
AIMIA SELF-SERVE IN ACTION
Data Volumes – 100% of transactional data over 2 years
Granular – lowest level data for maximum
flexibility of query
Fast – more than 50 times faster than competitors (average run time of 1 ½ minutes)
Actionable – for business users, not just
analysts, with an easy to use front-end
Scalable – Can handle 100s of reports per hour
with an architecture that supports easy growth
Where it matters
EDW says no or not now!
…and CFO says no big upgrades
And then came…
Hadoop just too slow for
interactive BI!
…loss of train-of-thought
Conclusion
“while hadoop shines as a processingplatform, it is painfully slow as a query tool”
© 20th Century Fox
Lots of these
Not so many of these
Hadoop is…
Hadoop inherently disk oriented
Typically low ratio of CPU to Disk
Pragmatism: Cubes?
…plenty of caching, limit drill
anywhere and add OLAP Cubes
Larger cubes ?
Issues: Time to Populate, Proliferation
Analytics requires CPU,RAM keeps the data close
Alternative - In-memory Processing
Cores do the work!Scale with the data
Happy Trails..
• Embrace LDW
• See Gartner Research Notes on LDW – Merv Adrian, Roxane Edjlali, Mark Beyer, etc.
• THINK about how TODAY’s BIG DATA will *just*
be tomorrow’s “data”
• How can an analytical platform change the way
you look at Big Data Analytics today?
• Bring the data close to ADVANCED ANALYTICS
(differentiate )
– ANNOUCNING – Mssively Parallel R
• Build these concepts into your IT plans
connect
www.kognitio.com
twitter.com/kognitiolinkedin.com/companies/kognitio
tinyurl.com/kognitio youtube.com/kognitio
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EMEA: +44 1344 300 770
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