If insights are available from mass amounts of data, you require enormous agility across business units to act on these. Understand how your peers tackle such problems and what new approaches are available to businesses.

5. 643 Companies (83% > $1B), 12 Industries

6. 643 Companies (83% > $1B), 12 Industries

7. 643 Companies (83% > $1B), 12 Industries

8. 
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9. Drive to take more actions/decisions in
presence of more information and less time
Evolution of Enterprise Analytics
Isolation:
What has
happened?
In-time:
What is
happening?
Integration:
Why has
it happened?
Intelligence:
What will happen?
What are others saying?
Information:
Harnessing
Knowledge

10. “any fool can know … the point is to understand.”
- Albert Einsteinand … the goal of understanding is to predict
Reactive Intelligence Predictive Intelligence
*courtesy Pawan Sinha, MIT

11. “any fool can know … the point is to understand.”
- Albert Einsteinand … the goal of understanding is to predict
Reactive Intelligence Predictive Intelligence
*courtesy Pawan Sinha, MIT

12. “any fool can know … the point is to understand.”
- Albert Einsteinand … the goal of understanding is to predict
Reactive Intelligence Predictive Intelligence
*courtesy Pawan Sinha, MIT

13. sampling P(X) manually => infinite time / infinite # people!
m attributes, each with d possible values: O(d2m) ‘cubes’
for m=40, d=10 this becomes 1080 > # atoms in the universe
so – BI folks need to learn analytics
Customers( x1… xm)
Big Data is about ‘wide’ data

15. Random-access to data is poor, even in memory!
Map-reduce based procedures exploit this.
network
speed
distributed
processing works
in-memory DB
no panacea

16. POV 1 : Big Data is here to stay and will be an increasingly significant arena of competitive
differentiation
POV 2 : There are two fundamental aspects to Big Data : Harnessing: The Technology required to
Manage Big Data and Harvesting : The Technology required to analyze and derive insight from Big
Data.
POV 3 : Big Data Technology Platform can solve traditional Data Problems as well and is not
limited by the use of Big Data itself.
POV 4 : The current innovation landscape is vast, varied with multiple products and offerings. We
can expect natural Consolidation in next 2-5 years.
POV 5 : Unstructured Data cannot be consumed in its raw form. It is essential to convert it
into consumable structured form for useful interpretation
POV 6 : Fusion of Unstructured and Structured Information is creating the need for a new science
stream: Data Science which requires both Business context and Hard Science
POV 7 : Big data is in the incubation Phase for most of the organizations. Only the likes of
Google, Yahoo, Amazon, Facebook are matured adopters.
POV 8 : Enterprises will have to undergo business process adjustments / redefinition both for
upstream and downstream connect (consumption) on big data, i.e. harnessing and harvesting

18.  Event Detection Engine
 Data Harmonisation
 Causal Analytics
Framework
 Topic Evolution in News
 Email Mining …
 Sensor Pattern Matching
 Sensor Data Motif Discovery
 Temporal Event/Sensor Rules
 Geo-Spatio-Temporal
Patterns/Motifs/Rules
 Sensor-stream Databases
 Collective Entity Resolution
 Relationship Discovery
 Searching Linked-open-data
 Federated Object Discovery

20. TCS
Big Data
Accelerators
Sentiment
Analysis,
Social Media
Adaptors,
Data
Connectors,
Video
Analytics, Utiliti
es
TCS Active
Archival
Archival
using
Hadoop
storage.
Abundant
space. Warm
data
TCS Meta
Data
Manager
Searchable
platform to
manage the
metadata of
Hadoop data
across clusters
TCS Data
Migration
Tool
Fast, secure
data
movement
in/out of
Hadoop from
any source
(m/f, oracle
etc.)
TCS
Sensor
Data
Analytics
Receive, store
and analyze
any type of
sensor / log
data
TCS
Perigon™
Provide a
confluence of
customer data
and analytics
using
enterprise as
well as social
data
(Customer 360
view)

21. Let’s Innovate Together
Corporate Technology Organization
CTO.Evangelize@tcs.com