To succeed, an analytics or data science team must effectively engage with business experts who are often inexperienced with advanced analytics, machine learning and data science. They need a framework for connecting business problems to possible analytics solutions and operationalizing results. Decision modeling brings clarity to analytics projects, linking analytics solutions to business problems to deliver value.

1. BRINGING CLARITY TO ANALYTICS
PROJECTS WITH DECISION
MODELING
A Leading Practice

2. • CEO of Decision Management Solutions
• Works with clients to improve their business
by identifying and modeling decisions, and
applying analytic and business rule technology
to automate & improve these decisions.
• Spent 14 years championing Decision Management.
• Consultant, author, speaker
• IIA Faculty leader
Copyright © 2017 IIA and Decision Management Solutions. All Rights Reserved
JAMES TAYLOR
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3. • The Big Ideas
• The Value of Decision Modeling
• Clarity of Purpose
• Effective Collaboration
• Operationalizing analytics
• Practical Recommendations
3
AGENDA
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4. • Hard to connect business environment to analytics
• Business users lack data science experience, vocabulary
• Data scientists/analysts are not familiar with business problem
• Decision Modeling:
• Builds a shared understanding with business clients
• Revives projects that have lost their purpose
• Brings clarity to problems long thought difficult
• Delivers value quickly
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Reserved
THE BIG IDEA: DECISION MODELING
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5. What kind of analytic/data science group are you in?
A. Individual contributor
B. Dedicated to a single business function/business team
C. Supporting multiple business functions/business teams
D. Center of Excellence supporting multiple analytic teams
POLL #1
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6. Data
Business
Understanding
Data
Understanding
Modeling
Data Preparation
Evaluation
Deployment
THEORY: PROJECTS USE CRISP-DM
• Central Data Science Team
• Provide diverse skillset
• Support multiple projects across
operations
• Use CRISP-DM
• Cross-Industry Standard Process
for Data Mining
• Most widely used approach for
managing analytic and data
science efforts
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7. IN PRACTICE: NOT EXACTLY
• Business Objectives not
Business Understanding
• A lack of clarity
• Rework not reevaluation
• A lack of collaboration
• IT handoff not deployment
• No operationalization
Data
Data
Understanding
Modeling
Data Preparation
EvaluationIT
Business
Objectives
A lack of
clarity
A lack of
collaboration
No Operationalization
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8. THE VALUE OF DECISION MODELING:
CLARITY OF PURPOSE
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9. • Value of data science comes from improved decisions
• Any data science project must influence decisions
to add value
DATA SCIENCE IMPROVES DECISIONS
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10. WHICH DECISION MUST BE IMPROVED?
• Define a Question
• Define Allowed Answers
• This
• Focuses on discrimination
• Resolves disagreements
• Reveals inconsistency
• Clarifies different assumptions
• Approve Claim?
• In what way should this
claim be processed once
complete details have been
received?
• Auto-approve
• Fast-track
• Regular process
• Reject
• Fraud investigation unit
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11. Copyright © 2017 IIA and Decision Management Solutions. All Rights Reserved
MODEL DECISION REQUIREMENTS
Source: DecisionsFirst Modeler
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Specific Analytic
Decision
Analytic Models
Overall decision to
be improved
Other Relevant
Experience
Regulations and
Policies
Data
Sub-decisions

12. • Costs increasing
• Organizations that drove cost increases
were not accountable - could not allocate costs
• Decision Model for Allocation Decision
• Identified data needed for decision
• Identified how that data was derived
• Revealed (much simpler) original data sources
• Allocation could be managed
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CASE: COST ALLOCATION
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13. Copyright © 2017 IIA and Decision Management Solutions. All Rights Reserved
OTHER EXAMPLE RESULTS
• Life insurance provider
• Decision model of life underwriting decision
• Identified that a planned medical
risk prediction would not be usable
• Project was redirected
• No wasted effort
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14. THE VALUE OF DECISION MODELING:
EFFECTIVE
COLLABORATION
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15. How much of an issue is collaboration for you and your
team?
A. We don’t really collaborate with anyone
B. We collaborate with business partners well, IT not so much
C. We collaborate with IT well, business partners not so much
D. We collaborate well with business partners and IT – no
problems
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POLL #2

16. Three Legs
1. Business
2. IT
3. Analytics
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COLLABORATION

17. • Automated lead qualification
• Need to predict size also for prioritization
• Decision Model
• Revealed that analytic would modify estimate
• Showed how prediction would be used
• Focused analytic effort on business value
not just accuracy
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CASE: AUTOMATED LEAD SIZE PREDICTION
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18. Copyright © 2017 IIA and Decision Management Solutions. All Rights Reserved
OTHER EXAMPLE RESULTS
• Healthcare insurance provider
• Developed a decision model for healthcare claims
adjudication
• The model showed where and
how to use a fraud analytic
• Increased usage
• Improved results
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19. THE VALUE OF DECISION MODELING:
OPERATIONALIZING
ANALYTICS
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20. DECISIONS HAVE CONTEXT
• Decisions are…
• Made during Business Processes
• Triggered by Events
• Made by Systems
• They impact KPIs, Objectives
• Various organizational roles
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System
Decision
Process
Event
Performance
Indicator
Objective
Organizational
Unit

21. OPERATIONALIZING DATA SCIENCE
Decision
Traditional New
External  
Internal  
Analytically enrich it
Make Consistent, Precise, Real-Time
Decisions Across All Channels
Learn Continuously
Start with customer data
Add Policies, Regulations
Best Practices and Preferences
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22. • Predict if customer will renew service contract
• Why is that the prediction?
• Here are the predictive variables
• I’m still not sure. Maybe if you just added this….
• Actually:
• Needed to prioritize selling opportunities
• Churn prediction just part of this
• Stay focused
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CASE: SERVICE CONTRACT RENEWAL
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23. Copyright © 2017 IIA and Decision Management Solutions. All Rights Reserved
OTHER EXAMPLE RESULTS
• Manufacturing company
• Identified predictions of quality risk
• Could change task assignments
• Developed decision models around
product quality
• Found opportunity to analytically focus
daily activities of quality team
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24. How much of a challenge is operationalizing your
analytics?
A. We struggle to operationalize any analytics
B. We operationalize analytics too slowly
C. We can only operationalize simple analytics
D. We effectively operationalize everything up to real-time
embedded analytics
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POLL #3

25. PRACTICAL
RECOMMENDATIONS
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26. Review
• Examine portfolio for stagnated analytics projects
• Identify functions that need but don’t have analytics
Learn and adopt
• CRISP-DM as an approach
• Decision modeling as a technique
Approach
• Hold one-day decision modeling sessions with stakeholders to kick off
• Work with people affected by decisions across business, IT, analytics
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IMPLEMENTATION CHECKLIST

27. LESSONS LEARNED
• Natural impatience to rush
to the data
• Not just a way to rescue
projects
• Diverse teams are a
powerful asset
• Many projects don’t know
how data science can help
• Bring in decision modeling
at start of the project
• Teach data scientists to
use as standard approach
• Decision modeling focuses
these teams
• Use a facilitated one-day
decision modeling session
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28. 1. Performance Measures
2. Identify Decisions
3. Clearly Define Decisions
4. Specify Business Context
5. Define Data Requirements
6. Define Knowledge Requirements
7. Define Decision Requirements
8. Iterate and Refine
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DECISION MODELING APPROACH
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29. QUESTIONS AND DISCUSSIONS
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30. Copyright © 2017 IIA and Decision Management Solutions. All Rights Reserved
TAKEAWAYS
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31. research@iianalytics.com
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James Taylor
james@decisionmanagementsolutions.com
www.linkedin.com/in/jamestaylor