IBM Analytic Decision...

© 2012 IBM Corporation

IBM Analytic Decision Management Jonathan Healy, Product Manager IBM Analytical Decision Management, Business Analytics, IBM Srinivasan Govindaraj, Advanced Analytics SME, Business Analytics, IBM

IBM Analytical Decision Management SaaS, Cloud Tech Talk Series

31-October-2012

© 2012 IBM Corporation 2

Srinivasan Govindaraj is an Advanced Analytics SME with

IBM Business Analytics Growth Initiatives team. In his

current assignment he is developing Signature Solutions

for various industries using IBM Analytic Decision

Management. He has over 11 years of experience

specializing in Advanced Analytics

About the Speakers

Jonathan Healy is the Senior Product Manager for IBM

Analytical Decision Management. He was formerly

product manager for SPSS Blueprints and Industry

Applications. He has held various roles in SPSS since

1993.


Leaders recognize that effective decision making is key to success

75%

Sources: IBM Global CIO Study 2011 & IBM Global CEO Study 2012

84%

of CIOs with mandates to transform

the business are looking to “drive

better real time decisions.”

of CEOs of outperforming

companies strongly differentiate

their organizations by

“translating insight into actions.”


Example – Santam Insurance

• South Africa’s largest short-term insurance company

• More than 650,000 policy holders

• Assets under management of 17 billion South African Rand (US $2.4 billion)

• Market share greater than 22 %

• Instrumented

• When a claim is submitted Santam captures data related to a number of key risk

indicators

• Interconnected

• The analytical engine uses a combination of business rules and sophisticated

predictive models to assess claims for potential fraud and transfer them to the

appropriate processing channel.

• Intelligent

• By segmenting claims according to risk factors Santam can focus on investigating

high-risk claims and catching fraudsters while rewarding good customers with fast

settlement and better service.


Example – Santam Insurance (continued)

• Before

• Minimum time to settle a claim was three days

• After

• Low-risk claims can be settled within an hour

• Customers with legitimate claims get much faster service.

• Significantly reduced the number of claims that need to be assessed by mobile

operatives, which will lead to considerable operational cost savings.

• Enhanced fraud detection

• In the first month able to identify patterns that enabled us to foil a major motor insurance fraud

syndicate

• Within the first four months saved R17 million on fraudulent claims and R32 million in total

repudiations

• Solution delivered a full return on investment almost instantly


What to watch today?

Flip through channels

TV listings

“Negotiate” with sibling


What to watch today?

Objective

• Choose the best shows

Things you know (rules)

• What’s on now

• What’s on the schedule

Things that are uncertain (models)

• Weather forecast

• Dad’s mood

Constraints

• 2 hours total viewing

• Each choose 1 hour


IBM Analytical Decision Management

Decision Management is a business discipline that applies predictive analytics to optimize

the outcomes of everyday business processes

It combines:

Business rules to automate

what you know

Models to predict what you don’t

Optimization to reach

the best decision

Optimized decisions

+ + Business

rules Optimization Predictive

analytics

All data


Decisions


Rules


Rule detail


Models


Optimization


IBM sees successful Decision Management requiring the combination

of business rules, predictive analytics, and optimization

Optimize actions within

resource constraints, aligning

execution with strategy

Empower real-time and

adaptive decisions

accommodating changing

conditions

Provide front-line employees

and systems with

recommended actions

Business

Rules Predictive

Analytics

Optimization

All Data

Optimized Decisions


Sales

Marketing

Customer

Service

Finance

IT

Operations

Product

Development

Human

Resources

Manage risk, regulation & compliance

Grow, retain and satisfy customers

Transform financial processes

Increase operational efficiency

Decisions are made across the organization


What’s New in IBM Analytic Decision Management

20


IBM Analytic Decision Management SaaS features and Benefits

IBM Analytic Decision Management Software as a Service (SaaS) Key features

• Decision Management cloud platform combining predictive analytics,

• Web-based interface based on the generally available IBM Analytics Decision Management

software program

• Provides another way of delivering analytics as an alternative to an on-premise solution.

IBM Analytic Decision Management Software as a Service (SaaS) Key features

• This hosted offering further enables broad usability across your organization - a key value

proposition for IBM Analytic Decision Management SaaS.

• Available as a monthly subscription priced simply on your storage requirements - no limit to

number of users or complexity of model. This has the benefit of using the existing IBM

cloud infrastructure which has global reach

• Easily customizable to solve business problems

• Using the Jump Start consulting option, IBM experts can help you address a wide array of

business areas and help to provide specific analytic-based decision outcomes.

• Because this solution is in the cloud, it is easy for large enterprises or small businesses to

deploy, collaborate, and standardize around a particular business problem.


Richer modeling via Modeler Advantage

This enables IBM

Analytical Decision

Management to

leverage an array

of pre-built

algorithms to

visually and

intuitively create

predictive models

The result: an

analytics “stack”

with the depth and

breadth to be a

leader in the

marketplace

These new

capabilities

include:

association rules,

auto clustering and

auto forecasting


Easy to use Interface to Facilitate Integration

User interface

aims at

empowering

business users

and analyst to

define, simulate

and do what if

scenarios

intuitively

The Interface

also provides

simple

mechanisms to

integrate with

other IBM

software


Integration with Entity Analytics

IBM Analytic

Decision

Management

provides an ability to

incorporate rules

and models in based

on output from Entity

Analytics

This enables IBM

Analytical Decision

Management to

automatically

prepare, cleanse

and transform data

for the best possible

analytics

The result:

preventing fraud and

implementing other

operational cost

take-out agendas


Complex Mathematical Optimization

This capability

enables complex

mathematical

optimization as part

of the configurable

applications

framework utilizing

CPLEX

In turn, this enables

IBM Analytical

Decision

Management to

optimizes decisions

within resource

constraints to link

execution to

strategy so

outcomes are

consistently

maximized for the

organization


IBM Analytic Decision Management Customer Case Studies and Demo

26


Demo: Next Best Action for Telecommunications

Information

Analytics

Speaking

with the

customer

Building

predictive

models

Defining the

Next Best

Action

Creating

marketing

offers

Establishes the

Information

Supply Chain

Operations


Many tactical decisions are part of repeatable processes that need to

be automated

Facilitating healthcare

Manufacturing processes

Call center offers

Insurance claims processing

Financial services


Combining techniques ensures optimal outcomes for today’s complex use

cases

Reduce cost via

preventive

maintenance

Optimization

Identifying claims

fraud

Reducing telco

churn

Predictive Analytics Business Rules

Business Rules Predictive Analytics Predictive Analytics

Business Rules


[email protected]

[email protected]

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