from information to intelligence

© Right Brain Systems LLC.

Srini KoushikPresident and CEORight Brain Systems LLC.Twitter Handle - @skoushik

RBS on Analytics

innovation – agility - executionRight 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 minuteLower barriers to connectivity drives integration of islands

of data

Source – Big Data – The next frontier for innovation, competition and productivityMcKinsey 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?

04/12/2023 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?

04/12/2023 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. HarrisHarvard Business School Press, 2007

Today’s market leaders have “cracked the code”

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The RBS Approach to Agile Analytics

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© Right Brain Systems LLC.04/12/2023 9

RBS on Analytics

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

DATAWhat 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

Data Foundation

Information Design and Visualization

Analytics CapabilitiesAnalytics Operational Framework

Active Business Sponsorship

012345


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 DataOperational data from internal

and external data sources

Unstructured DataDigital 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 VolumeLatency: What is the latency of the data – real-time, near real-time or batched


Information Design

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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

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 dataHistorical dataCustomer interaction data

Syndicated dataPublic dataHistorical 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 DeliveryVisualization

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 VisualizationData Ecosystem and relationshipsInformation design Analytics PatternsAccess and IntegrationUse and Implementation


Analytics Capabilities - Reference ArchitectureVisualization 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 DataOperational data from internal and external data sources

Unstructured DataDigital data (audio, video), text data (from

social networks) etc.

Semi-Structured and Real-time DataData 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 andGovernance


Managing a smarter business with Analytics

Analytics Operational Framework

WorkforceAnalytics

Operational Analytics

CustomerAnalytics

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 AnalysisCorrelation of dataPattern recognition

MCMC MethodsRegression AnalysisPattern 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 AnalyticsWhat 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

UXVisualization

CEPScoring

Business AnalyticsNext Best Action

Next Best Offer

Customer Management Action

Service Recovery


• 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/srinikoushikTwitter - @skoushikSlideshare – http://www.slideshare.net/rightbrainsystemsBlog – http://rightbrainsystems.tumblr.com

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from information to intelligence

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