TCS Innovation Forum 2012 - Kitenga

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…
Distributions
Programmer
Oriented
Traditional Unstructured
Structured Big Data &
RDBMS Content
© 2012 Kitenga Proprietary Analytics

7. KITENGA'S 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 HOOD
Critical 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
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Source: James Kobielus, Feb 23,2012
Senior analyst for data warehousing at Forrester Research.
© 2012 Kitenga Proprietary

14. .
CUSTOMER USE CASES – LIVE DEMOS
Next-gen Business Intelligence – in action