rbs - 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

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“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?

2/6/2013 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?

2/6/2013 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”

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The RBS Approach to Agile Analytics

2/6/2013 8


RBS on Analytics

2/6/2013 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

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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

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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,

Enterprise Risk Management etc.)

Operational (Product, Sales, Service,

Fraud 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, Complex Event Processing etc.


Analytics Capabilities - Governance

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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

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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 Center On 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. 2/6/2013 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

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http://www.linkedin.com/in/srinikoushik

http://www.slideshare.net/rightbrainsystems

http://rightbrainsystems.tumblr.com/

rbs - from information to intelligence

Technology

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