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Big data and the bi wild west kognitio hiskey mar 2013

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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 …

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!”

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  • 1. Big Data and the BI Wild West: Don’t Bring an Elephant to a Gun Fight Michael Hiskey Head Product EvangelistMarch 2013
  • 2. Big Data and the BI Wild West
  • 3. Are you Packin’ ?
  • 4. 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
  • 5. BI Wild West
  • 6. Disruptor: Data ?
  • 7. Disruptor: Social Media & Sentiment
  • 8. Characteristics of Big Data What? New value comes from your existing dataRespondents were asked to choose up to two descriptions about how their organizations view big data from the choices above. Choiceshave been abbreviated, and selections have been normalized to equal 100%. n=1144Source: IBM Institute for Business Value/Said Business School Survey
  • 9. What has changed? Moreconnected-users? More-connected users?
  • 10. How are you really judged? • Fast? • Consistent? • All users?
  • 11. Case Study #1Deep Dive Analytics on Big Media Data - monetizedata and gain customer insight TRA/Virgin Confidential | 13 TRA Confidential Copyright 2012 TiVo Research and Analytics, Inc. 13
  • 12. Demographics don’t buy products TRA/Virgin Confidential | 14
  • 13. 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
  • 14. TRA adds the missing element in the TV buyingand selling system: Consumer Purchase Behavior TRA/Virgin Confidential | 16
  • 15. TiVo – TRA Clients ROI + 25% improved ROI 81% TRA/Virgin Confidential | 17
  • 16. 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
  • 17. 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
  • 18. Case Study # 2 LOYALTY ANALYTICS
  • 19. REVOLUTION IN RETAILING HAS CHANGEDTHE 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
  • 20. 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
  • 21. PROFILING & CROSS SHOPPING• Focus on key customers• Provide broad product offerfor all customer segments• Profile customers based ongeography, lifestage, andother segments • Where to place product in store • What to group into multi-buy promotions
  • 22. PRODUCT ASSOCIATION & REPEAT PRUCHASE • Build bespoke segmentations based on product• Determineproduct loyalty bycustomer groups• Who are thebiggest spenders
  • 23. AIMIA SELF-SERVE IN ACTIONData Volumes – 100% of transactional dataover 2 yearsGranular – lowest level data for maximumflexibility of queryFast – more than 50 times faster thancompetitors (average run time of 1 ½ minutes)Actionable – for business users, not justanalysts, with an easy to use front-endScalable – Can handle 100s of reports per hourwith an architecture that supports easy growth
  • 24. Where it matters
  • 25. EDW says no or not now!…and CFO says no big upgrades
  • 26. And then came…
  • 27. 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
  • 28. © 20th Century Fox
  • 29. Hadoop is…Lots of theseHadoop inherently disk orientedNot so many of theseTypically low ratio of CPU to Disk
  • 30. Pragmatism: Cubes? …plenty of caching, limit drillanywhere and add OLAP Cubes
  • 31. Larger cubes ?Issues: Time to Populate, Proliferation
  • 32. Alternative - In-memory Processing Analyticsdo the work! Cores requires CPU, RAM keeps the data close Scale with the data
  • 33. 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
  • 34. connect NA: +1 855 KOGNITIOwww.kognitio.com EMEA: +44 1344 300 770linkedin.com/companies/kognitio twitter.com/kognitiotinyurl.com/kognitio youtube.com/kognitio