From information to intelligence

© Right Brain Systems LLC.
Srini Koushik
President and CEO
Right Brain Systems LLC.
Twitter Handle - @skoushik
RBS on Analytics
innovation – agility - execution
Right Brain Systems LLC.
Building Smarter Organizations
with Analytics

Big
Data
Storage Capacity is growing
at an annual growth rate of
23%
Computing Capacity is
growing at an annual growth
rate of 54%
60% of the world’s population
used cell phones in 2010
12% of cell phones are smart
phones and this number is
growing at 20% a year
Over 30 million network
sensor nodes in 2010 growing
at 30% a year
30 billion pieces of content
shared on Facebook every
month
13 hours of content is
uploaded on YouTube
every minute
Lower barriers to connectivity
drives integration of islands
of data
Source – Big Data – The next frontier for innovation,
competition and productivity
McKinsey Global Institute, May 2011
Digitization and Connectivity drives Big Data

Consumerization Universal Access Internet of Things
Cloud Computing Social Business Big Data
The pace of change is accelerating and converging
Current trends help drive more data
3

“What information consumes is rather obvious: it
consumes the attention of its recipients. Hence a
wealth of information creates a poverty of
attention, and a need to allocate that attention
efficiently among the overabundance of
information sources that might consume it.”
- Herbert Simon
“Avoidable failures are common and persistent, not to
mention demoralizing and frustrating across many fields –
from medicine to finance, business to government. And
the reason is increasingly evident: the volume and
complexity of what we know has exceeded our individual
ability to deliver its benefits correctly, safely, or reliably. “
Dr. Atul Gawande, The Checklist Manifesto
Human ability to deal with complexity
has not changed
Machine learning while useful has its
disadvantages. Example, automated
hedge fund trades
Success requires the effective
blending of human intuition and
decision making with business
intelligence and machine learning
Can humans keep up?

What is Analytics?
4/17/2013 5
These patterns lead to business insights which can be
translated into specific actions to drive meaningful
business outcomes.
Analytics is the discovery and communication
of meaningful patterns in data.

How did we get to Analytics?
4/17/2013 6
• Linear programming
• Regression analysis
• Markov chain Monte
Carlo methods
• Simulations
Availability of different
types of data
Digitization & Storage
Easy and inexpensive
access to any data
Cloud and Connectivity
Visualize and act anytime,
anywhere using mobile
devices
Pervasive Access
• Enterprise Data
• Federated Data
• Public/Syndicated Data
• Structured data
• Semi-structured data
• Unstructured data
Computing Capability
Ability to quickly
organize and process a
lot of data
• Provide insights and
actions in real-time
• Deliver them to the where
they can be used
• Access them from any
device
+ + +
Business Intelligence can answer questions such as: what happened; how many, how often, where did it happen; where
exactly is the problem; what actions are needed.
Business analytics answers the questions: why is this happening; what if these trends continue; what will happen next
(predict) and what is the best that can happen (optimize).

Uses advanced analytics to
identify and propose the
“Next Best Offer” based on
Customer browsing and
buying patterns
Uses Cinematch,an advanced
analytics engine to make movie
recommendations based on
rental patterns
Pioneered the use of customer
segmentation and profitability
analysis to target and acquire
most profitable customers Uses web analytics and
customer loyalty program data
to target and drive business
through its most profitable
customers
Source – Competing on Analytics – The new science of
Winning. – Thomas H. Davenport and Jeanne G. Harris
Harvard Business School Press, 2007
Today’s market leaders have “cracked the code”
7

The RBS Approach to Agile Analytics
4/17/2013 8

RBS on Analytics
4/17/2013 9
We want to build Smarter Organizations that deliver meaningful business
results through secure, seamless context-aware experiences in a data driven
world
Our approach to Analytics is focused on
building the organizational capability that
can sense changes, understand them,
respond to them through business actions
and refine these actions continuously to
deliver better business results

ACTIONS:
What do we do?
DATA & INFORMATION
MANAGEMENT
• Understanding the data
ecosystem – Structured,
Semi-structured and
Unstructured data
• Creating and maintaining
data as an asset
BUSINESS
INTELLIGENCE
• Aggregation of
Information – primarily
from Structured data
within the enterprise
• Historical view aimed
at enabling business
planning and improving
business performance
BUSINESS
ANALYTICS
• Correlation across
internal and external
data sources
• Identify patterns and
causal relationships in
historical and real-time
data
ANALYTICS DRIVEN
ORGANIZATION
• Predict / optimize
business decisions
• Translate insights into
actions through
operations
• Experiment,
implement, measure
and improve
DATA
What data?
INFORMATION:
What happened?
INSIGHTS:
Why did it happen?
BETTER
BUSINESS
OUTCOMES
• Ability to sense and
understand changes
in marketplace
• Rapid decisions
driven by
Information
• Enable
differentiated and
seamless context
aware experiences
Building a Smarter Organization
10

The RBS approach for building an analytics driven
organization focuses on five key domains:
• A data foundation that provides an enterprise view of data, its types,
sources, latency and how it is understood and used (metadata) in the
organization
• An Information Design that describes how users will visualize, access,
utilize and act on the insights generated
• Analytics Capabilities which includes the foundational practices, skills
and approaches for driving agility into the organization
• An Analytics Operational Framework that helps establish where and how
analytics can be used within the enterprise and
• Active Business Ownership from Operational leaders within the
company who understand and use the insights generated to make
informed decisions
Building an analytics driven organization takes more than good technology
Building an Analytics Driven Organization
0
1
2
3
4
5
Data
Foundation
Information
Design and
Visualization
Analytics
Capabilities
Analytics
Operational
Framework
Active Business
Sponsorship

Building the Data Foundation
The Data Foundation defines:
1. How data is organized and
used in the Enterprise
(Semantics)
2. How data is persistently
stored and accessed
(Structure) and
3. How data is managed and
understood (access, inquiry,
replicated, updated etc.)
Real-time and Batch Queries (OLTP and
OLAP)
Real-time queries
Data Warehouse, Data marts and
Operational Data Stores
Real-time In-memory Databases
Enterprise Data Governance
Customer Financial Functional
(HR, IT, Marketing, Enter
prise Risk Management
etc.)
Operational
(Product, Sales, Service, Frau
d Detection, Operations
etc.)
Metadata, Data Modeling, Master Data Management
Enterprise
Application
Integration (EAI)
Extract
Transform
& Load
(ETL)
Unstructured Data Loads
(pattern matching, stop
word filtering, backward
pointers etc.)
Real-time Integration
Structured Data
Operational data from internal
and external data sources
Unstructured Data
Digital data (audio, video),
text data (from social
networks) etc.
Semi-Structured and Real-
time Data
Data from real-time
sensors, high volume
transactions etc.
Source: Enterprise Data (Internal), Federated Data (External), Syndicated Data (External)
Volumes: Streams, High Volume, Low Volume
Latency: What is the latency of the data – real-time, near real-time or batched

Information Design
13
User Specific
Information
Context
Information
Visualization
and
Interaction
• What role do they play –
consume content or create
content?
• What are their
preferences?
• What are the capabilities
of the device they are
using?
• What is the context of the
interaction?
• Where is the interaction
happening?
• What is the nature of the
interaction? –
support, transactional etc.
• What are the constraints? –
Device capabilities, Location
awareness, Network
capabilities etc.
• How do we represent the
information being consumed?
• How does the system accept
inputs from the user?
• What does the interaction look
like if there is no human
involved?
• What are the requirements for
data access? – Fire and
Forget, Request/Response, Comp
lex Event Processing etc.

Analytics Capabilities - Governance
4/17/2013 14
Understand Data Domains Information Classification Model Governance Model
Information Privacy, Security, Regulatory and Compliance Rules and Guidelines
Enterprise Risk Management
Confidential
Privileged
Public
Customer
Finance
Product/Pricing
Operational
Operational
Personal Information – Name, Address,
SSN, Credit Car # etc.
Financial Performance Data etc.
Non-identifiable individual data
Historical data
Customer interaction data
Syndicated data
Public data
Historical data
• One Enterprise Governance
Board for all confidential
data, compliance and
regulations
• Information and IT security
policies are driven by the
Enterprise Governance Board
• Business ownership and
stewardship for data
domains and Metadata

Analytics Capabilities – Core Competencies
15
Data
Integration
Analytic
Modeling
Agile
Delivery
Visualization
Embedding
Analytics
into
Operations
Co-relate outcomes to
facts and events and build
models based on patterns
discovered in these
relationships
Implementing metrics,
decision making
structures and a closed
feedback loop to take
advantage of insights
Rapid Prototyping,
incremental delivery,
built-in retrospectives and
continuous improvement
Visualization and User Experience
design to present insights in
a meaningful way that
results in the desired action
The ability to integrate with,
ingest, understand all types of
data from internal and external
sources

Analytics Capabilities - Agile Analytics
Analytics Story Maps
User Stories
Analytics Epics
Analytics Personas
Analytics Solutions
Agile Delivery
Rendering and Visualization
Data Ecosystem and relationships
Information design
Analytics Patterns
Access and Integration
Use and Implementation

Analytics Capabilities - Reference Architecture
Visualization and User Interaction
Interaction Model – Fire-and-forger, Request-Response, Complex Event Processing (CEP)
Analytics Solutions – Social Analytics, Value Chain Performance Analytics, Customer Interaction Analytics etc.
Advanced Statistical
Modeling
Quantitative Analysis Linear Programming Markov Chain,
Monte Carlo
Methods
Regression Models Simulation
Real-time and Batch Queries (OLTP and OLAP) Real-time queries
Data Warehouse, Data marts and Operational Data Stores Real-time In-memory Databases
Enterprise Data Governance
Customer Financial Functional
(HR, IT, Marketing, Enterprise Risk
Management etc.)
Operational
(Product, Sales, Service, Fraud Detection,
Operations etc.)
Master Data Management, Distributed Map Reduce
Enterprise Application
Integration (EAI)
Extract Transform &
Load (ETL)
Unstructured Data Loads
(pattern matching, stop word filtering,
backward pointers etc.)
Real-time Integration
Structured Data
Operational data from internal and external data
sources
Unstructured Data
Digital data (audio, video), text data (from
social networks) etc.
Semi-Structured and Real-time Data
Data from real-time sensors, high volume
transactions etc.

Analytics Capabilities - Workforce Model
• 20% Specialized skills
• Data Scientists – Mix of statistical and quantitative skills (Left Brain) and pattern
detection/matching skills (Right Brain)
• Product Owners – Ability to breakdown complex problems into product features and
backlog that can be delivered incrementally
• Visualization and UX Designers – Creative and Design Thinking skills (Right Brain)
• 80% Delivery skills – Architects, Developers, Software Quality analysts etc.
• Mix of soft skills and technical skills, delivered a 10-20-70 learning model
• Formal and informal mentor-apprentice model
• Campus relationships with arts and science schools
• Industry-Academia interactions to drive new perspectives and to facilitate updates on new
techniques
• Job rotations to drive cross-skilling and enforce knowledge management requirements
• Retrospectives at all levels of delivery and operations to drive experiential learning
• Formal and informal knowledge sharing sessions to drive collective learning
• Client-specific relevance maintained through single-point of contact for business-specific details
• Use of Social networking tools to establish informal knowledge and experience networks
Sustainable
Workforce
Immersive
Learning
Experience
Management

Shared Analytics CenterOn Site
Analytics Capabilities - Hybrid Delivery
Agile Analytics will
require a hybrid delivery
model that takes into
account the data and
privacy concerns and
balancing with the need
for agility and the scarcity
of key skills
Private Data Privileged Public Data
Product Owners Data Scientists
Visualization and UX Designers
Operations Monitoring and Reporting
Analytics Solutions
Business Stewardship and
Governance

Managing a smarter business with Analytics
Analytics Operational Framework
Workforce
Analytics
Operational
Analytics
Customer
Analytics
Financial
Analytics
• We help businesses integrate analytics with
the key metrics that are tracked on an
organization’s Balanced Scorecard1
• This framework:
• Defines and measures key business performance
metrics across all four quadrants of the balanced
scorecard
• Synthesizes findings and delivers insights to business
leaders
• Performs analysis on metrics including variation and
root cause analysis
• Implements a closed loop feedback mechanism that
helps understand results and refine the insights and
actions through agile delivery
1 Kaplan, R. S. and D. P. Norton. 1992. The balanced scorecard - Measures that drive performance. Harvard Business Review (January-February): 71-79.

Customer Analytics Operational Analytics Financial Analytics Workforce Analytics
Marketing Sales Supply Chain
CUSTOMER
ANALYTICS
Help determine Next Best
Action or Next Best Offer
CUSTOMER
INTERACTION
ANALYTICS
Improve Customer
Experience and reduce cost
of service
SOCIAL ANALYTICS
Drive brand loyalty and
service recovery actions
based on sentiment analysis
on social media
CUSTOMER
INTELLIGENCE
Improve customer share of
wallet through target
marketing
MARKET
INTELLIGENCE
Provide insights into market
trends and customer
behavior
MARKETING
EFFECTIVENESS
Drive returns from better
marketing spend allocation
PRICING ANALYTICS
Provide price / discount
insights for specific sales
SALES FORCE
ALLOCATION
Drive sales coverage across
addressable segments
SALES
EFFECTIVENESS
Enable effectiveness of sales
pursuit and conversion
SALES
COMPENSATION
Design and track sales
compensation for
optimization
PRODUCT SALES
PERFORMANCE
Monitor sales performance
by product / solution
DEMAND-SUPPLY
PLANNING
Reduce demand-supply
mismatch through better
forecasting
INVENTORY
OPTIMIZATION
Reduce own and channel
inventory costs
PROCUREMENT
ANALYTICS
Drive savings from spend
forecasting and
consolidation
FINANCIAL
REPORTING &
ANALYTICS
(Budget and forecasting, P&L
/ Balance Sheet review)
COMPLIANCE & RISK
Continuous monitoring,
better audit sampling to test
controls and enable revenue
hedging
CUSTOMER
PROFITABILITY
Continuous monitoring and
optimizing of customer and
product profitability
WORKFORCE
OPTIMIZATION
Allocation / matching of
workforce for efficient usage
ATTRITION
MODELING
Identify attrition propensity
based on characteristics &
drivers
Examples of Analytics in a Smarter Business

© Right Brain Systems LLC. 22
Determining the “Next Best Action” for a customer
An Example
Behavioral data (S, F,E)
• Transactions
• Payment history
• Service history
• Credit history
Descriptive Data (S, E)
• Attributes
• Characteristics
• Self-declared info
• (Geo)demographics
Attitudinal data (U, E, P)
• Opinions
• Preferences
• Needs & Desires
• Sentiment
Interaction data (SS, E)
• E-Mail / chat transcripts
• Call center notes
• Web Click-streams
• In person dialogues
S –Structured U – Unstructured SS – Semi Structured F – Federated P – Public E - Enterprise
Historical Analysis
Correlation of data
Pattern recognition
MCMC Methods
Regression Analysis
Pattern Matching
Business Intelligence
What do our customers say
they want?
What are the major life events
for the customer?
How do customers interact
with the organization?
Business Analytics
What is the customer’s Propensity to
buy?
What are the indicators of customer
retention/attrition?
What is the Customer Profitability?
What is the Product Profitability?
Device data (U, P)
• Location
• Camera
• Microphone
• Multi-touch
• Sensors
UX
Visualization
CEP
Scoring
Business Analytics
Next Best Action
Next Best Offer
Customer
Management Action
Service Recovery

© Right Brain Systems LLC.4/17/2013 23
• Analytics enable business
agility
• Building an analytics driven
organization requires a
comprehensive set of
capabilities
• It's a huge waste without
business and operational buy-in
• RBS has the IP and experience
to help you get there
Conclusion

Srini Koushik
Linkedin – http://www.linkedin.com/in/srinikoushik
Twitter - @skoushik
Slideshare – http://www.slideshare.net/rightbrainsystems
Blog – http://rightbrainsystems.tumblr.com
24

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