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TCS Innovation Forum 2012 - Kitenga

TCS Innovation Forum 2012 - Kitenga

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TCS Innovation Forum 2012 - Kitenga

  1. 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. 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. 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. 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. 5. THE SOLUTION © 2012 Kitenga Proprietary 5
  6. 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. 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. 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. 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. 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. 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. 12. NEXT-GEN BUSINESS INTELLIGENCE Source: James Kobielus, Feb 23,2012 Senior analyst for data warehousing at Forrester Research. © 2012 Kitenga Proprietary
  13. 13. NEXT-GEN BUSINESS INTELLIGENCE Kitenga √ √ √ √ √ √ √ √ Source: James Kobielus, Feb 23,2012 Senior analyst for data warehousing at Forrester Research. © 2012 Kitenga Proprietary
  14. 14. .CUSTOMER USE CASES – LIVE DEMOSNext-gen Business Intelligence – in action
  • pphilp

    May. 29, 2012

TCS Innovation Forum 2012 - Kitenga

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