The Decision Model - DAMA NY€¦ · Method Decision Model Notation Conditions Conclusion Pattern Policy Tier Policy Discount Policy Pricing Within Bounds 1 ≤ 1 Is No 2 ≤ 1.5

The Decision Model: Its Debut at MIT’s IQ Symposium as the Basis

for an IQ Framework

Barbara von HalleManaging Partner

Knowledge Partners International, LLC2010

About KPI

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Publications

Thought Leader

The Decision ModelBusiness Logic Framework linking

Business with Technology

Business Process ManagementBusiness Decision Management

Business Rule ManagementEnterprise Architecture

Business AnalysisRequirements

Testing

Services

FirstSTEPService to create

unambiguous, and complete Requirements

KPISTEPService to perceive, organize

and manage Business Processes and Rules with

Decision Models

STEPmentMentoring of clients to

achieve self-reliance with Center of Excellence

Training & Certification

Experience

Financial Services

Insurance

Healthcare

Government

Utilities

Transportation

Telecommunication

Energy


“..one of the classic books

of a new era in computing

that will have much traction

in the next few years”Dr. Opher Etzion,

Master Inventor, IBM

Agenda

• Motivation for The Decision Model

• Introduction To The Decision Model

• Top Down, Iterative, Agile

• Impact on Business Process

• Recent Advances and Real World Experiences

• The Decision Model and Information Quality

• Summary


Separation of Concerns:The One Dimension Left Behind

5

Security Component

Workflow Component

Transaction Component

Presentation Component

Base Application

Business Logic

ComponentReporting/BI Component

Database Component

A “Big Ball of Mud”(Foote & Yoder)

Component-Based Architecture (Orr)

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How Business Rules (Logic) are Handled Today

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Auto Future Design Process Flows outdated (Rev Reins-Reissue).vsd


How The Decision Model Changes Everything

Process Model Decision

Model

Rule Families

Agenda







• Summary


Definition of Business Logic

9

Business Logic is the means by which the business

derives conclusions from facts.


The simplest case is the evaluation of single fact, leading to a single conclusion:

One example of such a statement:

A person is highly likely to default on a loan

A person has a poor employment history

Let us examine “Fact”

• Fact Type is the same as an attribute type

• Fact Value is the same as an attribute value

– Person First Name

• Fact Type

– John Doe

• Fact Value

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Domains

• Fact Type Domain is the same as Attribute Type Domain.

• Fact Type Domains are important to the integrity of the business logic

• How so?

11

Fact Type Fact Type Domain

Person Employment History Good, Average, Poor

Person Likelihood of Default on Loan High, Medium, Low

Claim Payment Amount Min Claim Amount to Max Claim Amount

Policy Renewal Method Manual, Automatic

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What is an Atomic Piece of Business Logic?

• An atomic piece of business logic

– Consists of zero to many conditions

– Leading to a conclusion about one fact type

– Each condition is an atomic logical expression

– About an atomic fact type

– Conditions are ANDed together, never ORed


The Rule Family –A Way to Represent Multiple Logic Statements

13

Conditions Conclusion

Person Employment

History

Person Mortgage

Situation

Person Miscellaneous

Loans Assessment

Person Likelihood of

Defaulting on a Loan

Is Poor Is Poor Is High Is High

Is Good Is Low

Is Poor Is Poor Is Low Is Medium

PersonEmployment History

Is Poor PersonMortgageSituation

Is Poor PersonMisc Loan Assessment

Is HighAND

AND

PersonLikelihood of Defaulting on a Loan

Is High

Instead of Multiple Logic Statements that Look Like This:

They May be Represented in Two Dimensional Tables called Rule Families:

Rule Families are Tables that Conform to Rigorous Principles


Building Further: Where Do We Get Our Input?

• Starting with the first condition, we ask where this Fact value comes from. Input from a web page or a file? Is it Persistent data? Is it the result of execution logic?

• In this case we discover that it comes from executing logic that evaluates other business criteria: the business experts want to judge a Person’s Employment History based on criteria such as Person’s Years at Current Employer and Person’s Number of Jobs in the Past Five Years.

• We have to build an additional Rule Family where the conclusion will be “Person Employment History”, a different conclusion to that of our current Rule Family (Rule Family: Business logic grouped by Conclusion.)

14


Rule

Pattern

Person

Employment

History

Person

Mortgage

Situation

Person

Miscellaneous

Loans

Assessment

Person

Outside

Credit

Rating

Person Likelihood

of Defaulting on a

Loan

1 is Poor Is Poor Is High is High


Building Up to Two Rule Families

• Note the Interim Conclusion “Person Employment History”• We discover the need for yet another Rule Family. This one comes

to a conclusion about a Person’s Employment History which is based on two conditions: Person Years at Current Employer and Person Number of Jobs in Past Five Years.

15


Rule

Pattern

Person

Employment

History

Person

Mortgage

Situation

Person

Miscellaneous

Loans Assessment

Person

Outside

Credit Rating

Person Likelihood of

Defaulting on a Loan

1 is Poor Is Poor is High ? ? is High


Rule

Pattern

Person Years at Current

Employer

Person Number of Jobs in

Past Five Years

Person Employment History


Dynamic Data

What is it?

• Created on the fly

• Whenever the business comes to a conclusion even if the conclusion is not stored

• Conclusions guide transactions and business processes

• Conclusions determine outcome, success or failure of a transaction, process, or decision

Why is it important?

• It runs the business!

• Discovering it and managing it instigates business innovation.

• This can make a huge difference in organizational maturity, performance, perhaps survival.

• The data professional brings good data practices to the management of dynamic data.


Decision Model Principles

• Structural Principles – Structural simplicity

• Declarative Principles – Declarative structure

• Integrity Principles – Optimal logical integrity


These Principles ensure that each row, each pattern and each family has business and logical integrity: this

means that the business purpose has been understood and aligned, and that there is no logical error in the

logic, and that there is no conflict or duplication in the logic. The Principles introduce Normalization.

Decision Model Normalization

• Normalization: the concept of decomposing structures into more desirable structures that have greater levels of integrity– First normal form – ensures business logic represented and

interpreted in only one way as a Rule Family with one conclusion fact type and conditions ANDed

– Second normal form – eliminates irrelevant conditions

– Third normal form – eliminates hidden Rule Families in conditions


How Should We Separate the Decision Dimension?

• Universal Model for Decision Structures (Decision Model)

– Normalized Logic Entities

– Inferential relationships

– Logic statements (content)

• Decomposition into Atomicity of Logic

• Normal Forms

• Best Practices

– Naming Conventions, definitions

– Domains

– Not overloading

– Role of nulls

– Integrity Validation

• Modeling tools and generation of code

Copyright © 2010 KPI 19

Decision

Business

Motivation

Model

Decision Model:business rules and

business logic

Process Model

Use Cases

SOA Components

Business

Requirements &

Test Cases

Vocabulary Models:

Glossary/Semantic Model

Logical Data Model

Object Model

Enterprise

Business Unit

Function Function

Business Unit

Function

Organization

Model

Agenda







• Summary


Every Decision Model Starts with a Business Decision

22

“Business decision: a conclusion that a business arrives at through business logic and which the business is interested in managing.”

22

The underlined words (Calculate, Estimate, Determine, Assess, Validate) are “Decision Words”

Fact Type Business Decision

Claim Payment Amount Estimate the claim payment amount

Claim Payment Eligibility Determine Claim Payment Eligibility

Customer Likelihood of Loan Default Determine Customer Likelihood of Loan Default

Insurance Policy Renewal Method Determine insurance policy renewal method

Inventory Item Minimum Stock Level Assess the Inventory Item minimum stock level

Loan Prequalification Determine loan prequalification requirements for a customer

Person BMI (Body Mass Index) Calculate Person BMI

Vendor Performance Index Calculate the Vendor Performance Index


Determine Policy

Renewal Method Decision Model

Notation


Determine Policy


Notation


Policy Renewal MethodPolicy Pricing Within Bounds

Policy Underwriting RiskManual Underwriting Indicator



Policy Pricing Within BoundsPolicy Discount

Policy Tier

Determine Policy


Notation





Policy Tier

Determine Policy


Notation


Pattern Policy Tier Policy Discount

Policy Pricing Within Bounds

1 ≤ 1 Is No

2 ≤ 1.5 > 10% Is No2 ≤ 2 > 20% Is No2 ≤ 2.6 > 22% Is No2 > 1 ≤ 0% Is Yes

2 > 1.5 ≤ 20% Is Yes2 > 2 ≤ 22 Is Yes

1 > 2.6 Is Yes


PatternPolicy Pricing Within Bounds Policy Underwriting Risk

Manual Underwriting Indicator Policy Renewal Method

1 Is Unacceptable Is Manual Renewal Process

2 Is No Is Manual Renewal Process

3 Is On Is Manual Renewal Process

4 Is Yes Is Acceptable is Off Is Automatic Renewal Process


Policy Underwriting RiskInsured Major Ownership Change

Insured Major Location Change Policy Annual Premium

Policy Discontinued Agent

Insured Major Ownership Change

Insured Minority StockholderInsured Majority Stockholder

Insured Board ChangeInsured CEO Change

Insured Major Location Change

Insured Location Zip-5 Insured Location Occupied Square Footage

Insured Location Construction

Policy Discount

Policy Grade Package Grade

Package Discount Location State Category

Decision Model Notation





Policy Tier

Determine Policy

Renewal Method

Agenda







• Summary


Option 1

Option 2

Option 3

Rule

Pattern

1 is Low is Good = "A"

1 is Low is Bad = ?

1 is High is Good = ?

1 is High is Bad = ?


Person's Debt

Person's Employment

History

Person's Credit

Rating

Process Model Rule Family Table Decision Model Diagram

Distinguishing Decisions from Process


Simplify the Models, Improve the Solution

Before After


Auto Future Design Process Flows outdated (Rev Reins-Reissue).vsd

Agenda







• Summary


Recent Advances in the Model and Practice

• Decision Model Views

– Mass Customization: Geographical, political, glossary, customer-based

• Codeless implementation into BRMS

• The Decision Model Data Quality Framework

• The Decision Model for Data Translation Services

• Testing and Generation of Test Cases


Real World

• “The Decision Model’s principles and normalization rules give us confidence we can get repeatability and consistency amongst business analysts when performing rules analysis.

• In addition, the structural integrity of the Decision Model makes the technology implementation straightforward

• IT and Operations have agreed to use our decision model as business requirements for business logic changes – this will greatly speed up the change process

• In addition, the use of a COTS BRMS solution will allow us to take advantage of additional capabilities over time, such as enhanced testing and decision-warehousing capabilities.“

• From policy to automation reduced by 30% in time, while delivering 66% more changes

Mark Pettit, Freddie Mac, Operations Management Group, MIT IQIS, July 15, 2010


Agenda







• Summary


The Decision Model IQ Framework

35

A Decision Model Diagram for Completeness


Rule Family for Completeness

Rule Pattern Conditions Conclusion

Applicant Annual Salary

Documentation

Applicant Supplemental

Income Amount

Applicant Supplemental

Income Amount Completeness

Is Acceptable Is Complete

Is Not

acceptable

Is Populated Is Complete

Is Not

acceptable

Is Not

populated

Is Not complete


Other IQ Dimensions

• Consistency, reasonableness, and accuracy logic originate from– Analysis of internal and external data– Insights from vendors– Fraud detection programs– Analytics– Organizational experience over time

• Because these three dimensions involve conditions leading to a conclusion, the Decision Model View is an appropriate vehicle for managing these specifications.


Agenda







• Summary


Five Reasons to Include the Decision Model

1. The business rules of business decisions are the forgotten dimension.

2. The Decision Model improves business decision-making.

3. The Decision Model transcends and leverages available data.

4. The Decision Model is iterative, agile in nature.5. The Decision Model always reveals important

pieces of information we have ignored (dynamic data).


Five Reasons to Manage Dynamic Data

1. Dynamic data is the glue that ties together the way the business thinks and determines the quality of its decisions.

2. Dynamic data emerges as an asset as important as persistent data.3. If dynamic data is not managed property, Decision Models fall apart.4. If Decision Models fall apart, business conclusions may be risky.

Risky conclusions result from:• Incomplete domain coverage• Overloaded conclusion values• Conclusion values that are null

5. If business conclusions are risky, the business may suffer.

The business needs data professionals to help!


Next Steps


Visit our web siteswww.thedecisionmodel.comwww.kpiusa.com

• Updated Events• Download White Papers• News

Become a member at Linkedin The Decision Model Group

• Network with practitioners• Exchange ideas• Learn new tricks

Read our articles and bookswww.modernanalyst.comwww.tdan.comwww.requirementsnetwork.com

Join our presentationsWe conduct frequently presentations for following interest groups:

www.theiiba.orgwww.bpminstitute.comwww.dama.org

Contact uswww.Twitter.com/[email protected] +1 973 543-1339

Start a projectDiscuss with us how to apply The Decision Model for your Requirement or Business Rules project.

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Documents

The Decision Model - DAMA NY€¦ · Method Decision Model Notation Conditions Conclusion Pattern Policy Tier Policy Discount Policy Pricing Within Bounds 1 ≤ 1 Is No 2 ≤ 1.5