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TCS Innovation Forum 2012 - Kitenga
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TCS Innovation Forum 2012 - Kitenga

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TCS Innovation Forum 2012 - Kitenga

TCS Innovation Forum 2012 - Kitenga

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  • 1. REVOLUTIONARY BIG DATA INSIGHT ENGINE Anil Uberoi, Co-Founder & CEO www.kitenga.com 510.507.3399 anil@kitenga.com Kitenga: Maori, n. A perception; A view
  • 2. KITENGA BIG DATA ANALYTICS PLATFORM First and only “Big Data” content analytics platform with integrated search, information modeling & visualization An entirely new kind of Big Data Insight Engine © 2012 Kitenga Proprietary 2
  • 3. THE PROBLEM: INFORMATION OVERLOAD 3rd Party (Licensed) Public data Data (Web,news, blogsphere, social media) Internal Data (Enterprise data) Unstructured Data Structured Data © 2012 Kitenga Proprietary
  • 4. BIG DATA= MORE THAN JUST VOLUME Volume Newer Analytics DBs Kitenga Traditional DB-based BI Transform Multi-dimensional Velocity Big Data into Variety Actionable Intelligence © 2012 Kitenga Proprietary 4
  • 5. THE SOLUTION © 2012 Kitenga Proprietary 5
  • 6. BIG DATA ( ) LANDSCAPE Solution Oriented BI Solutions Karmasphere Analyst Aster Data/Teradata GreenPlum/EMC Datameer Vertica/HP VoltDB… -based Solutions (Analytics DBs) Hadoop Connectors Hadoop Connectors Karmasphere, Javamation… (Dev tools) Cloudera, EMC, MapR, IBM, Hortonworks… DistributionsProgrammer Oriented Traditional Unstructured Structured Big Data & RDBMS Content © 2012 Kitenga Proprietary Analytics
  • 7. KITENGAS UNIQUENESS Unstructured “Big Data” content analytics  Volume, variety/complexity, and velocity  In addition to structured(DB) AND semi-structured data (log files) Business user  Designed for non-programming/ information analyst  Interactive information modeling and visualization Computational linguistics  Machine learning, NLP, multi-lingual text analytics Two (Big Data related) US Patents filed © 2012 Kitenga Proprietary 7
  • 8. KITENGA SWEET SPOT Need to exploit/monetize diverse content  Unstructured, semi/structured content  More than traditional BI on structured data Need to reduce raw data-to-insight latency  Sophisticated/ad-hoc content analytics for non-programmer analysts Big Data = Volume*Variety *Velocity © 2012 Kitenga Proprietary
  • 9. Kitenga Analyst Kitenga Kitenga ZettaViz ZettaSearch Visualize, Model, Facetted Search, Interact Visualization Kitenga Analytical ZettaVox Analytical Producer Crawl, Extract, NLP, Consumer(Information Analyst) Machine Learning, (Business Users) Transform, Index © 2012 © 2011 Proprietary Kitenga Kitenga Proprietary
  • 10. BIG DATA ANALYSIS Transform Big Data into Actionable Intelligence  Aggregate  Count  Extract  Transform  Chart  Graph  Model  Visualize  Search  Predict © 2012 Kitenga Proprietary 10
  • 11. UNDER THE HOODCritical elements include  Natural Language Processing  Computational Linguistics  Machine Learning  Document format cracking  Segmentation  Tokenization  Lemmatization  Parts-of-speech tagging  Gazetteer lookup .  Initial pattern recognition  Pattern grouping  Group disambiguation  Etc. © 2012 Kitenga Proprietary 11
  • 12. NEXT-GEN BUSINESS INTELLIGENCE Source: James Kobielus, Feb 23,2012 Senior analyst for data warehousing at Forrester Research. © 2012 Kitenga Proprietary
  • 13. NEXT-GEN BUSINESS INTELLIGENCE Kitenga √ √ √ √ √ √ √ √ Source: James Kobielus, Feb 23,2012 Senior analyst for data warehousing at Forrester Research. © 2012 Kitenga Proprietary
  • 14. .CUSTOMER USE CASES – LIVE DEMOSNext-gen Business Intelligence – in action