1) Cognitive computing systems use algorithms and digital knowledge to enable intelligent insights and actions. They learn and interact naturally with users to extend human and machine capabilities. 2) Drivers of cognitive analytics include growing data from technology innovation, complex databases, and platforms like cloud and mobile. Implications are systems will scale across devices and disrupt business processes, transforming society. 3) Cognitive systems differ from traditional computing by being adaptive, contextual, iterative, and interactive as they learn from data.

1. Enabling Agile and Adaptive Decision Making Through Knowledge
Empowered Business Analytics Solutions
INSIGHTS|ANALYTICS|INNOVATIONS
Data Science & Big Data Practice
Cognitive Solutions

2. Cognitive Solutions combine the power of mathematical algorithms and
computing in collaboration of digital knowledge reasoning to enable
intelligent insights and actions.
Analyst
prospective on
Cognitive
Analytics
Drivers –
• The proliferation in technology innovation across domains/channels
• Growing usage of complex database that can be the major data source and
hold answers to complex business insights
• Emergence of computing platforms such as Cloud, Mobile, Big Data, Social
which holds the key to “closer to customer” insights
Implications –
• Cognitive systems will emerge into a highly scalable entity and will
be delivered via any mobile device
• It will be highly disruptive:
 Business processes, domains and society will be transformed
 A revolution of business 360 is required – People, Process,
Technology, Culture and partner ecosystems
 A strong strategic intent among the business leaders are required to
stay tuned to the industry dynamism
Sources: Insights from Gartner research, IDC and McKinsey Research

3. Cognitive computing systems learn and interact
naturally with business users to extend what
either human or machines could do on their own.
They help make better decisions by penetrating
the complexity of big data
Data Ingestion
Data / Pattern Mining
Hypothesis Generation /
Testing
Experience based Learning
Interacting with users
Deduction / Reflection
Reasoning / Inference
Integrate virtual reality
Structured data, Text,
Video, Images
AI, NLP, Video & Image
Processing, Deep
Learning
Answer to a query or
asking more questions
to provide the right
answer
IterativeProcess
Cognitive Computing Framework
Cognitive Across Verticals
Global financial services firm, turns to cognitive
computing and advanced analytics to boost the
breadth and depth of its products and services
A leading retailer has implemented In-store
cognitive apps to improve personalisation.
Thinking apps go beyond the structured
consumer profile data of age, location and past
purchase history found in databases
A major cancer medical centre is co-creating a
cognitive system that uses cancer patient
treatment data to assist oncologists to diagnose
and treat patients based on the most current
available data
Cognitive System Characteristics
Cognitive systems differ from current computing
applications in that they move beyond tabulating
and calculating based on preconfigured rules and
programs.
Adaptive
Contextual
Iterative
Interactive
Data Proliferation

4. BusinessDataDiscovery
Study and analyze
customer data
touchpoints across
information
systems internal
and external to the
enterprise.
Statistical and
exploratory
analysis of data.
DataMining
Advanced machine
learning,
augmented with
cognitive
knowledge graphs
and business
taxonomies to
decipher semantic
relationships and
patterns in data.
IntelligenceModelling
Define the unified
data model, linking
entities and
attributes across
the business
ecosystem –
internal enterprise
data and external
data.
Implementation
Integrate customer
data points into a
single platform
and build a
metadata
abstraction layer
for business
service
consumption and
intelligent
discovery.
Database
Docum
ents
Disparate structured &
un-structured data ingestion for
discovery and exploratory analysis.
Data Mining using Ontology based
semantic normalization, machine
learning and sematic technology.
Design the unified data model and
semantic data map.
Implement the cognitive data model
Powered with semantic search and
analytics.

5. • Business process
automation
• Recommendation
Engines
• Segmentation
• Customer Intelligence
• Actionable Insights
• Continuous learning
from new data.
•Machine learning with
augmented intelligence
•Text Mining, NLP,
Classification,
Summarization, Entity
Analysis
• Neural Network, Deep
Learning, machine
learning algorithms.
•Knowledge Engineering,
Semantic Processing
•Multi-structured Data,
Events, Logs
•Social Media, Blogs, Web,
Communication logs
• Enterprise Application
Data
Ingest Process
DeployLearn
Enterprise Knowledge Management Cycle
Our cognitive solutions aim towards enrichment of enterprise
information assets through intelligence augmentation from these
multi-structured data sources–
• External Public Data from the web – Social web, blogs,
websites
• External Private Data from 3rd parties – Cross-
functional and cross-domain analytics
• Domain Knowledge
• Business Process Knowledge
• Internal Data – Documents, Emails, Communication
Logs, Web Interaction Logs etc.
Processing these multi-structured data, and applying advanced
artificial techniques with cognitive science, we model and build an
Integrated Enterprise Knowledge layer for intelligence driven
business decision and action.

6. Insights Intelligence Action
• Sentiment Polarity – Positive, Neutral,
Negative
• Topics – Sports, Politics, Fashion, Comfort
etc.
• Emotion Analysis – Excited, Happy,
Passionate
• Digital and Social Footprint- clicks,
mentions, likes, machine data etc.
• Geo-spatial Insights – location, trends etc.
• Experience – Customer value chain
analysis
• Behavior – Event related behavioral
analysis
• Activity – events and activities across
subject areas
• Semantics and Content Discovery
• Business Research
• Market Intelligence
• Consumer Engagement and Intelligence
• Personalization of Offerings
• Target Campaigns and Ads
• Competitive Edge – Brand
Development
• Location Centricity
• Customer Centricity
• Content Classification
• Content Summarization
• 360 degree View
• Business Process Optimization
• Smart Solutions for machine
automation and intelligence

7. 1
Personalization
• Deliver personalized
service offerings
based on consumer
behavior and activity.
2
Unified View
• Single view of entities
across all business
units
3
Real Time Intelligence
• Event driven data
linkage allows real
time analysis and
insights.
4
Intelligent Data
• Semantic empowered
linked data unveils
intelligence and
knowledge across
enterprise.
5
Revenue
• Establish optimized
pricing. sales,
marketing,
campaigning
strategies
6
Decision Making
• Cognitive solutions
encompass collective
intelligence for real-
time focused decision
making.
Business
Impact

8. Transforming your Data Chromosome
0 1
0 1
1 1

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12. Call Recording
Samples
Text
Transcripts
Audio
to Text
Text
Mining/NLP
NLPClassifiers
Topics
Sentiment
Emotion
Personality
Relation
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•
•
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•
•
•
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Business
Expert
Interviews Knowledge Graph
Linguistic
Dictionary

14. Topic wise vector space graph of Customer and Agent Call logs
C1
C2
C3
A1
A2
A3
Topic A
Topic B
Topic C
Sentiment- 6
Emotion – 7
Customers Agents

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Topic and Intent Knowledge Graph
Semantic Query Engine
Sales force
Training
Insights
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Conversionrate(%)

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Experience
Perception
Express
Opinion
1. Natural language
processing and text
representation
Real World Observed World
3. Topic mining and
analysis
5. Text based
prediction
2. Word association
mining and analysis
4. Opinion mining &
sentiment analysis

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Negative Positive

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Positive Campaign Not Sustained

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Competitive Edge
Behavior
Pattern
Location
Intelligence

25. INSIGHTS|ANALYTICS|INNOVATIONS