1. Big Data and the BI Wild West:
Don’t Bring an Elephant to a Gun Fight
Michael Hiskey
Head Product Evangelist
March 2013

2. Big Data and the BI Wild West

3. Are you Packin’ ?

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

6. BI Wild West

7. Disruptor:
Data ?

8. Disruptor: Social Media & Sentiment

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

10. What has changed?
More
connected-users?
More-connected
users?

11. How are you really judged?
• Fast?
• Consistent?
• All users?

13. Case Study #1
Deep Dive Analytics on Big Media Data - monetize
data and gain customer insight
TRA/Virgin Confidential | 13
TRA Confidential Copyright 2012 TiVo Research and Analytics, Inc. 13

14. Demographics don’t buy products
TRA/Virgin Confidential | 14

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
TRA/Virgin Confidential | 15

16. TRA adds the missing element in the TV buying
and selling system: Consumer Purchase Behavior
TRA/Virgin Confidential | 16

17. TiVo – TRA Clients
ROI + 25%
improved ROI 81%
TRA/Virgin Confidential | 17

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
TRA/Virgin Confidential | 18

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
TRA/Virgin Confidential | 19

20. Case Study # 2
LOYALTY
ANALYTICS

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

26. Where it matters

27. EDW says no or not now!
…and CFO says no big upgrades

28. And then came…

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

30. © 20th Century Fox

31. Hadoop is…
Lots of these
Hadoop inherently disk oriented
Not so many of these
Typically low ratio of CPU to Disk

32. Pragmatism: Cubes?
…plenty of caching, limit drill
anywhere and add OLAP Cubes

33. Larger cubes ?
Issues: Time to Populate, Proliferation

34. Alternative - In-memory Processing
Analyticsdo the work!
Cores requires CPU,
RAM keeps the data close
Scale with the data

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

37. connect
NA: +1 855 KOGNITIO
www.kognitio.com EMEA: +44 1344 300 770
linkedin.com/companies/kognitio twitter.com/kognitio
tinyurl.com/kognitio youtube.com/kognitio