Big data and the bi wild west kognitio hiskey mar 2013

transcript

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

Case Study #1Deep Dive Analytics on Big Media Data - monetize data and gain customer insight

Demographics don’t buy productsDemographics

DIMINISHING EFFECT OF ADVERTISING

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 adds the missing element in the TV buying

and selling system: Consumer Purchase Behavior

TiVo – TRA Clients

ROI + 25%

improved ROI 81%

� 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

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”

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

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Big data and the bi wild west kognitio hiskey mar 2013

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