This session reviews “Big Data” case studies from media analysis, retail analytics and customer loyalty that go beyond the data warehouse and Hadoop. Disruption from the “Facebook generation,” armed with iPads, Droid Phones and netbooks brings a melee of new tools, devices and data sources. An analytical platform is the ‘Golden Spike’ to hitch stable, proven, and mature BI solutions with the data frontier—deep analytics, predictive modeling, sentiment analysis, etc. to enable competitive advantage.
-or- “Big Data and the BI Wild West: Don’t Bring an Elephant to a Gun Fight!”
-or- “Big Data and the BI Wild West: Don’t Bring an Elephant to a Gun Fight!”
5. Got mobile?
200 million 50%
Employees bring their own Companies BYOD orgs have
device to work had a security breach
1/3 Nearly half
Have broken or would break Of the workforce will be made
corporate policy on BYOD up of millennials by 2020
9. Characteristics of Big Data
What?
New value comes from your existing 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
11. How are you really judged?
• Fast?
• Consistent?
• All users?
12.
13. Case Study #1
Deep Dive Analytics on Big Media Data - monetize
data and gain customer insight
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TRA Confidential Copyright 2012 TiVo Research and Analytics, Inc. 13
15. TV’s $70 Billion (US) =
Advertising Challenge
Diffused audiences:
Over 100 Channels access in average home
Broadcast Network Rating -8% vs Y-Y
Reach
Clutter & Consumer Control:
>5000 brands on TV Cost
Fickle Consumers watching on more screens
+14.7% Watching Timeshifted TV
+5.9% Watching Video on Internet
DIMINISHING EFFECT OF ADVERTISING
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16. TRA adds the missing element in the TV buying
and selling system: Consumer Purchase Behavior
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17. TiVo – TRA Clients
ROI + 25%
improved ROI 81%
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18. The Technical Challenge
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
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19. Kognitio powers the TRA advantage
Analytics on tens of billions of events in
seconds with
NO 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
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21. REVOLUTION IN RETAILING HAS CHANGED
THE RELATIONSHIP WITH THE CUSTOMER
DATA IS THE NEW OIL
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
22. RETAILERS EMBRACE SHOPPER CENTRIC RETAILING
SHOPPER
SEGMENTATION &
STRATEGY
SHAPE THE
LEVERAGE STORE
YOUR SUPPLIERS EXPERIENCE
SHOPPER DATA
SHOPPER
INSIGHT
SHAPE THE
MANAGE
PERSONAL
YOUR MEDIA
EXPERIENCE
23. 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
24. PRODUCT ASSOCIATION & REPEAT PRUCHASE
• Build bespoke
segmentations
based on product
• Determine
product loyalty by
customer groups
• Who are the
biggest spenders
25. 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
29. Conclusion
Hadoop just too
slow for
interactive BI!
“while hadoop shines as a processing
platform, it is painfully slow as a query tool”
…loss of train-
of-thought
34. Alternative - In-memory Processing
Analyticsdo the work!
Cores requires CPU,
RAM keeps the data close
Scale with the data
35.
36. 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