1) The document discusses adopting an effective decision making framework using common methods and processes. It emphasizes capturing analytic insight as an asset and improving collaboration. 2) Decision Model and Notation (DMN) is presented as a common language that can be used to model decisions. DMN provides constructs to define decisions, their requirements, and relationships in diagrams. 3) Examples of applying DMN to decisions around retail conversion rate are shown. Components such as decisions, data sources, and knowledge bases are modeled visually.

1. Problem Solving
Adopting an Effective Decision Making Framework
James Parnitzke
March 2016

2. Introduction
Jim Parnitzke
Big Data Analytics, Enterprise Architecture
Advisor, Expert, Trusted Partner, and Publisher
Hands-on technology executive, trusted partner, advisor, software publisher, and widely recognized
information management and architecture thought leader. Over his career, Jim has served in executive,
technical, publisher (commercial software), and practice management roles across a wide range of industries.
Using analytic insight to solve the toughest problems
Contact:
(c) 904.607.6299
Linked In: http://www.linkedin.com/in/jimparnitzke
Twitter: http://twitter.com/jparnitzke
j.parnitzke@comcast.net
jim.parnitzke@gmail.com

3. this discussion is about problem solving

4. what we know

5. Analytics is important…
Making better decisions is more important.
Faster, better outcomes
Precise answers for hard-to-solve problems
Uncovering new growth opportunities
Increasing competitive capability
Improving business results

6. How important?
• Fact:
– over 94% of all Companies have Big Data and Analytics in their top 10
priorities to enable better decision making
– Organizations competing on analytics outperform their peers

7. what is a decision?

8. Decision (Meriam-Webster Dictionary)
1 a : the act or process of deciding
b : a determination arrived at after consideration : conclusion <make a
decision>
2 : a report of a conclusion <a 5-page decision>
3 : promptness and firmness in deciding : determination <acting with decision>
4 a : win; specifically : a victory in boxing decided on points <a unanimous decision>
b : a win or loss officially credited to a pitcher in baseball <has five wins in eight decisions>

9. Organizations make decisions every day…
• Strategic: Few in number, large impact
– Should we acquire this company or exit this market segment?
• Tactical: Management and control, moderate impact
– Re-organize the supply chain
– Change risk management approach
• Operational: Day-to-day decisions
– Improve conversion rates
– Select next best offer for a customer
– Select the terms for a loan
– Which supplier to use
– How to handle this claim; which need to be fast-tracked

10. examples

11. Marketing analytics drive business decisions daily
 Customer Profiling and Segmentation
 Up Sell Opportunity Analysis
 Real-time Product and Service Recommendations
 Try and Buy Usage Analytics
 Next Best Offer
 Retention Analytics
 Digital Marketing and Path to Purchase Analysis
 Sell through and Sales Channel Analysis
 Lead to Cash
 Path to Purchase
 Multi-Channel and Attribution Analysis
 Customer Lifetime Value
 Forecasting (Time Series Analysis)

12. Practice
Protocols
Processing
EDW
Analyzable data
Clinicians use diverse
protocols and orders in
daily care
Sub-Optimal State
© 2014 Denis Protti, Dale Sanders & Corinne Eggert
CDS:
EDW:
EHR:
MTTI:
Clinical Decision Support
Enterprise Data Warehouse
Electronic Health Record
Mean Time To Improvement
Clinical Information Systems
Decisions and Actions
Supporting information
Clinical, EHR, EDW
and Analytics Teams
Align metrics and data
Update EHR and EDW
with new data items if
needed where feasible
Start here
Monitor baselines and
clinical processes
Select a problem
Set outcomes and metrics
Quality
Governance
Clinical Variations
and Needs
Internal Evidence
Clinicians’ suggestions
External Evidence
Literature, reports, etc.
Quality
Governance
Use comparative data to
identify best outcomes
Determine standard order
sets, protocols and
decision support rules
External Evidence
Literature, reports, etc.
Analyze data quality
and process outcome
variations
Generate the internal
evidence
Clinical Analytics
Other Data Sources
Clinical, Financial, etc.
MTTI
EHR & CDS
Electronic clinical data
Clinicians use standard
protocols and orders
in daily care
Optimal State
Clinical, EHR, EDW and
Analytics Teams
Update EHR protocols and
EDW metrics
Enterprise Clinical Teams
Act on performance
information
Executive and Clinical
Leadership
Set expectations for use of
evidence and standards
Best Evidence
Information that
clinicians trust
Standards

Performance
12
Health care outcomes

13. sounds great – what’s the problem?

14.  Where is analytic insight captured in your organization?
Is the Intellectual Property managed like any other asset?
 Are standard tools and methods used to model, capture,
manage, and share this significant property?
 Have you even tested the decisions modeled or executed
through peer review or common diagnostics for defects?
 Has your modeling approach evolved at the same speed as
the new technologies and tools used to accelerate the
decision making process?
 Is proven practice shared with others?

15. If you answered yes to all five questions
congratulations…
you are exceptional

16. If not, follow on…

17. We have used a variety of techniques to accurately describe
the requirements for legacy information systems.
They work pretty well.
Many now realize that current approaches
do not solve the decision-making need that is important to
capturing and managing analytic insight to drive better
outcomes.
For the rest of us…

18.  Actionable
 Readily understandable by business users
 Used by business analysts to create decision requirements
and models
 Implemented by technical developers responsible for
automating the decisions in processes
 Managed and monitored by stakeholders who own the
results and outcomes of the decisions
The ideal decision framework should be:

19. Better decisions share common values
Actionable. - analysis for analysis sake is ridiculous.
They begin with the right questions. Value is demonstrated in
defining the decision in advance. Learn what data and metrics
are important and make a difference.
Non-trivial. Enough said…
Measurable. Know which measures matter and which don’t.

20. what is the answer?

21. An effective decision making framework
 Uses common methods and processes with clear goals and
objectives to manage and measure outcomes
 Adopts a common language across business, IT and analytic
communities improving meaningful communication
 Captures and manages analytic insight like any other asset
 Improves collaboration, increases reuse, shares proven
practice to solve complex problems once
 Eases implementation and deployment of decision services

22. let’s start

23. first, methods matter
really.

24. Use common methods and processes
Cross Industry Standard Process for Data Mining (CRISP-DM) is a process model that
describes commonly used analytic approaches. It is the leading methodology with 3-4
times as many people using this model as Sample, Explore, Modify, Model and Assess
(SEMMA) developed by the SAS Institute Inc.

25. Design and build independent
decision services using business
rules and advanced analytics
Decision Services
Legacy Systems Websites Business Process Event Correlation Enterprise Applications Mobile
Decision
Service
Business Rules Predictive Analytics
Data Warehouse, Operational Data Stores, Big Data
Create a closed loop between
operations and analytics to
measure results and drive
improvement
Decision Analysis
Identify and model the decisions
that are most important to
operational processes
Decision Discovery
Decision Model Notation
ActionDecision
Actionable goals and objectives

26. Adopt a common language
Decision Model Notation
 The OMG Decision Model and Notation standard provides a common
notation; a standardized bridge for the gap between the business decision
design and decision implementation.
 The purpose of DMN is to provide the constructs that are needed to model
decisions, so that organizational decision-making can be readily depicted in
diagrams, accurately defined by business analysts. Addresses two different
perspectives by existing modeling standards:
 Business process models (e.g. BPMN) can describe the coordination of
decision-making within business processes by defining specific tasks or
activities within which the decision-making takes place.
 Decision logic (e.g. PRR, PMML) can define the specific logic used to
make individual decisions, for example as business rules, decision
tables, or executable analytic model

27. Capture Analytic Insight
Where to start
DMN provides a third perspective – the Decision Requirements Diagram
 Business process models define tasks within business processes where
decision-making is required to occur. Decision Requirements Diagrams
will define the decisions to be made in those tasks, their
interrelationships, and their requirements for decision logic
 Decision logic (business rules) will define the required decisions in
sufficient detail to allow validation and automation.
Taken together, Decision Requirements Diagrams and decision logic can
provide a complete decision model which complements a business process
model by specifying in detail the decision-making carried out in process tasks.

28. Share proven practice to solve complex problems

29. Improve collaboration

30. Ease implementation

31. examples

32. Select Market Offer

33. Components
1) Decision Determine Product Eligibility
2) Decision Calculate Price
3) Decision Determine Customer Loyalty
4) Decision Determine Customer Value
5) Decision Select Marketing Offer
6) Decision Check Offer Timing
7) Decision Determine Products Offered
8) Decision Determine Offer Value
9) Decision Check Offer Timing
10) Decision Determine Products Offered
11) Decision Determine Offer Value
12) Decision Identify Unmet Needs
13) Decision Create New Market Offers
1) Data Source Customer Transactions
2) Data Source Customer Psychographics
3) Data Source Customer Demographics
4) Data Source Product Catalog
5) Data Source Available Marketing Offers
6) Data Source Strategic Marketing Plan
1) Know How RFM Score
2) Know How Affinity Grouping
3) Know How Customer Net Promoter Score
4) Know How Customer Propensity to Accept
5) Know How Segmentation Analysis
6) Know How Product Recommendation Engine
7) Know How Time Series Forecast
8) Know How Classification Rules
Decisions
Data Sources
Know How (Knowledge Base)

34. Retail Conversion Rate

35. Retail Conversion Rate
Retail Conversion Rate
Retail Conversion
Rate Data Flows DRAFT
SIZ
E
FSCM NO DWG NO
RE
V
SCALE 1 : 1 SHEET 1 OF 11
Assemble
Demographic
Profile
Filtered and Validated
Close (Conversion) Rate
Data Set
Assemble
Conversion Rate
Reporting in
context
Assembly Database
ETL
Transformation
Data Warehouse
Actual Sales and Payroll
FPA (acronym)
Store level weather.
Actuals and two-week forecast
Transformed
Weather Forecast
Enterprise
Reporting Platform
Retail Selling Channels
Point of Sales
Order Fulfillment
Installers
Designers
Retail Sales
Transactions
Location, Camera, Count Type,
Date, Time Interval, Count
Report Scheduler
Video Imaging
Video Management System
Video Business Intelligence
Analytic Publisher
Report Generator
Retail Traffic Database
Retail Traffic Counts
Traffic Reporting
Time and Attendance
Kronos
Time and Attendance
Scheduling
Accuweather Pro
WSI Weather Service
Employee actual hours reported at store i on day t
Assemble
Employee
Values
Assemble
Retail Traffic
Values
Customers who entered per period
store , camera, day
Assemble
Sales and
Transaction Counts
for Store for period
Retail Sales
Transactions
Per capita income for store location,
number of like-kind stores within x miles
where store is located,
Intra-day traffic variability for store locationCustomer Profile
Data Warehouse
Conversion Rate
Data Sets
Formatted
Conversion Rate
Reporting
Assemble
Weather Related
Values
Detailed weather forecast information.
Assemble Raw
Video Formats
Raw Video Feeds captured at store i on day t
HDFS
Transactions
Employees
Video
Retail Traffic Weather
Close (Conversion) Rate Data
Captured in Hadoop
Apply Business
Rules and
Assemble
Source Data
Total number of customers who entered store i on day t
Average number of customers who entered per hour store i on day t
Sales volume for store i on day t
Average sales volume per period for store i on day t
Number of customer transactions per period at store i on day t
Average number of transactions per period for store i on day t
Proportion of customers who made a transaction at store i on day t
Value in U.S. dollars of customers' shopping basket at store i on day t
Total number of employee hours reported at store i on day t
Average no. of employee hours per hour reported at store i on day t
Total number of like-kind stores within x miles where store i is located
Daily temperature for store location i
Per capita income for store location i
Average inter-day traffic variability for store location i
Intra-day traffic variability for store location i on day t
Growth in average traffic for store location i in period p
Average conversion rate for store location i in period p-l
Filtered Conversion
Rate Data Set
Per capita income for store location,
number of like-kind stores within x miles
where store is located,
Intra-day traffic variability for store location
Employee forecasted hours reported at store i on day t
Reporter Client
Reporting Results
Conversion Rate Processing
Results Captured in Assembly
Platform
Analytics Repository
Analytic Data Sets
Check for Additional Values:
Seasonality Vectors
Macro-economic Sets
Other data values
Check for Additional Values:
Seasonality Vectors
Macro-economic Sets
Other data values

36. Retail Conversion Rate (DMN)

37. Components
1) Decision Customer Traffic
2) Decision Items per purchase
3) Decision Gross margin
4) Decision Transaction Count
5) Decision Total number of customers who entered store i on day t
6) Decision Average number of customers who entered per hour store i on day t
7) Decision Number of customer transactions at store i on day t
8) Decision Average number of transactions per hour for store i on day t
9) Decision Sales volume for store i on day t
10) Decision Average sales volume per hour for store i on day t
11) Decision Value in U.S. dollars of customers' shopping basket at store i on day t
12) Decision Average inter-day traffic variability for store location i
13) Decision Intra-day traffic variability for store location i on day t
14) Decision Growth in average traffic for store location i in period p
15) Decision Average conversion rate for store location i in period p-l
16) Decision Proportion of customers who made a transaction at store i on day t
17) Decision Daily weather for store location i
18) Decision Total number of employee hours reported at store i on day t
19) Decision Average no. of employee hours per hour reported at store i on day t
20) Decision Conversion Rate
21) Decision Average purchase value
1) Data Source Retail Traffic Count
2) Data Source Point of Sale
3) Data Source Weather Information
4) Data Source Location Time and Attendence
1) Know How Retail Conversion Knowledge
2) Know How Time Series Analysis
3) Know How Time Series Forecast
Decisions
Data Sources
Know How (Knowledge Base)

38. which diagram is easier to understand?

39. Live Demonstration

40. The framework you have seen:
 Uses common methods and processes with clear goals and
objectives to manage and measure outcomes
 Adopts a common language across business, IT and analytic
communities improving meaningful communication
 Captures and manages analytic insight as any other asset
 Improves collaboration, increases reuse, and shares proven
practice to solve complex problems once
 Eases implementation and deployment of decision services

41. Now you can capture and manage analytic
insight to make better decisions…
Faster, better outcomes
More precise answers
Solve hard problems once
Uncover new growth opportunities
Increase competitive capability
Improve business results

42. Thank You…
Jim Parnitzke
Big Data Analytics, Enterprise Architecture
Advisor, Expert, Trusted Partner, and Publisher
Hands-on technology executive, trusted partner, advisor, software publisher, and widely recognized
information management and architecture thought leader. Over his career, Jim has served in
executive, technical, publisher (commercial software), and practice management roles across a wide
range of industries.
Contact:
(c) 904.607.6299
Linked In: http://www.linkedin.com/in/jimparnitzke
Twitter: http://twitter.com/jparnitzke
j.parnitzke@comcast.net
jim.parnitzke@gmail.com
james.parnitzke@nascentblue.com
If you interested in using analytic insight to solve your toughest problems contact me
below and learn how powerful easy-to-use tools can help across a wide variety of pre-
defined subject areas. Information management, Big Data, Analytics, Master Data
Management, Program and Project Management libraries and tools are available today
to begin delivering fast, quick, and distinctive results.
To get started contact me when ready, look forward to helping us succeed together.

43. additional slides

45. Decision Model Notation Summary

46. Business rules can be expressed using modeling approaches to include:
• Unified Modeling Language
• Business Process Execution Language
• Business Process Modeling Notation
• Decision Model and Notation
• Semantics of Business Vocabulary and Business Rules (SBVR)

47. A simple example…
Gather platform
characteristics profiles
Determine the analytics
operating model in use (can
be more than one)
Gather user profiles and
population counts for each
form of use
Develop platform and tool
signatures
Gather current state
portfolio, interview
stakeholders, and document
findings Refine Critical Analytic
Capabilities as defined
to meet site specific
needs
Weight Critical
Analytic Capability
according to each
operating model in use
Gather data points and
align with the findings
Assemble findings and
complete decision
models for platform
and tooling
optimization
1
2
3
4
5
6
7
8
9
CRISP-DM Method
Nine Step Method
Sample Platform and Tooling Optimization Decision Model