Slides: The Business Value of Data Modeling

1© 2018 IDERA, Inc. All rights reserved.
THE BUSINESS VALUE OF DATA MODELING
AUGUST 21, 2018
Ron Huizenga
Senior Product Manager, Enterprise Architecture & Modeling
@DataAviator

2© 2016 IDERA, Inc. All rights reserved. Proprietary and confidential. 2© 2018 IDERA, Inc. All rights reserved.
AGENDA
 Determining business value
• SMART metrics
 Agile project context
• Data modeling
 Case Study
• Background & scope
• Plan vs. reality
• Quality metrics
• Data modeling impact
• The bottom line
 Summary
 Q&A

DETERMINING BUSINESS VALUE
 Return on Investment (ROI)
 3 classic approaches
• Revenue enhancement
• Cost savings
• Cost avoidance
 Specific data modeling benefits often used in determining ROI
• Improved data requirements and specifications
• Faster development
• Reduced error correction/rework
• Better data quality
• Re-use of data assets
• Improved system integration
 By contrast, costs incurred by not data modeling

Actionable: easy to understand. It is clear when chart your
performance over time which direction is good and which direction is
bad, so that one knows when to take action.
Measurable: Need to be able to collect data that is accurate and
complete.
Specific: Metrics must be specific and target the area that is being
measured.
Relevant: There is a common trap of trying to measure everything.
Only measure what is relevant. Ignore the noise from irrelevant data.
Timely: Need to be able to get data when it is needed (as near to
real time as possible). If data is received too late, it may no longer be
actionable.

AGILE TEAMS
 Scrum vs. Extreme
 Self-organizing team concept
• Often misinterpreted as role-less
• Any person can perform any role
• Can switch from sprint to sprint (iteration)
• No specialization
• Reality
• A formula for disaster in all but the simplest of projects
 Often accompanied by attitude of disdain for data modelers
• “They just slow us down”
• “We don’t need a data model”
• Long term compromised in favor of short term project goals.

AGILE DATA ARCHITECT
 Enterprise data perspective
 Facilitator
• Enabler vs. Gatekeeper
 Full engagement in sprint planning
• Ensure completeness of deliverables
• Prioritization of dependencies
 Iterative work style
• Many simultaneous deliverables
 Collaboration
• Work with multiple teams simultaneously
• Cross-project focus
vs.

REAL WORLD PROJECT – AS PLANNED
 Supply Chain – Commercial Application Suite
 1 Common Database
 4 Parallel Development Streams
• By functional area
 Planned Duration: 1 year
 Planned Cost: $6,000,000
 Agile Methodology (Extreme & Scrum)
• Developers responsible for all design/development
• 2 week sprints (iterations)
 Weekly budgeted direct staffing costs: $ 92,800
• Did not include business SMEs as they were covered separately in corporate
budget

INITIAL WEEKS
 High defect rate
 Backlog growing rapidly
 By week 16, 50% of effort being spent addressing defects
• Direct cost $46,400/week
• Without being addressed, project schedule would need to be extended 40
weeks (additional cost of $ 3.7 million)
Excitement!
Anticipation!
Reality:

PROBLEM ASSESSMENT
 Define
• Defect categories
 Measure
• Discrete vs. weighted impact
• Linear vs. cumulative measurement
 Analyze
• Time series distribution
• Defects per object
• Defects vs. opportunities
 Improve
• Remediation strategy
 Control
• Comparative metrics

System Defects
& Rework
Requirements
Database & Persistence
User Interface
Business Services
Incomplete user stories
Incorrect business analysis documents
Missing foreign key constraints
Missing check constraints
Missing default values
Incorrect data type
Missing index
Missing audit columns
Incorrect table name
Incorrect column name
Tables not in 3rd
Normal Form Incorrect state transition
Calculation Error
Logic construct error
Incorrect looping or branching
Services not invoked
Messages
Navigation flow
Values not sorted in dropdowns
Subfile/list overflow
Controls not working
Missing prompts
Screens not user friendly
Incorrect service invoked
Entity Framework mapping error
Business Process has changed
Incorrect test cases
Defective unit tests
3rd
party widget
integration problems
Missing processes
Inadequate Subject Matter Expert Knowledge
DEFINE: DEFECT CATEGORIES & IMPACT

DEFECT CATEGORIES
Defect Category Primary Layer
Impact
Comments Defect
Count
Cumulative
Count
Defect % Cumulative
Defect %
Database & Persistence Data Layer Database and persistence errors can be very
problematic, time consuming and expensive to
correct. There is always an impact to the
persistence mapping in the business services layer
which must be corrected. In addition, changes may
also ripple to the User Interface layer.
243 243 35.01% 35.01%
Business services Business Layer Errors in business services are typically problems in
logic, calculations etc. This could cause erroneous
data. However, the corrections are usually limited
to the business layer itself, and do not require
structural changes (and hence mapping changes to
the data layer).
212 455 30.55% 65.56%
User Interface Presentation Layer UI errors are almost always isolated to the
presentation layer and generally fairly straigh
forward to fix.
197 652 28.39% 93.95%
Requirements any Requirments errors could impact any and all layers,
depending upon the severity or scope of the error.
They can not be quantified in general and must be
examined on a case by case basis to determine
impact.
42 694 6.05% 100.00%
Total 694 694 100.00% 100.00%

CUMULATIVE DEFECTS

WEIGHTED DEFECT CATEGORIES
Defect Category Primary Layer
Impact
Comments Defect
Count
Cumulative
Count
Defect % Cumulative
Defect %
Database &
Persistence
Data Layer Database and persistence errors can be very
problematic, time consuming and expensive
to correct. There is always an impact to the
persistence mapping in the business services
layer which must be corrected. In addition,
changes may also ripple to the User Interface
layer.
243 243 35.01% 35.01%
Business services Business Layer Errors in business services are typically
problems in logic, calculations etc. This could
cause erronious data. However, the
corrections are usually limited to the
business layer itself, and do not require
structural changes (and hence mapping
changes to the data layer).
212 455 30.55% 65.56%
User Interface Presentation Layer UI errors are almost always isolated to the
presentation layer and generally fairly straigh
forward to fix.
197 652 28.39% 93.95%
Requirements any Requirments errors could impact any and all
layers, depending upon the severity or scope
of the error. They can not be quantified in
general and must be examined on a case by
case basis to determine impact.
42 694 6.05% 100.00%
Total 694 694 100.00% 100.00%
Severity
Points
Weighted
Score
Cumulative
Score
Score % Cumulative
Score %
7 1,701 1,701 62.95% 62.95%
3 636 2,337 23.54% 86.49%
1 197 2,534 7.29% 93.78%
4 168 2,702 6.22% 100.00%
2702 2,702 100.00% 100.00%
A x B =

CUMULATIVE DEFECT SEVERITY

SPECIFIC DATABASE DEFECT POINT VALUES (SEVERITY)
No. Defect Type Description Points
1 Duplicate table 10
2 Table not normalized 10
3 Primary Key Incorrect 5
4 Missing Foreign Key (relationship) 5
5 Referential Integrity constraint incorrect 3
6 Missing foreign key index 2
7 Audit Column missing 2
8 Check Constraint Missing 1
9 Default value not specified 1
10 Incorrect table naming 3
11 Column data type incorrect 2
12 Column NULL specification incorrect 1
13 Incorrect column naming 2

DATABASE & PERSISTENCE DEFECTS

TIME SERIES DISTRIBUTION OF DEFECTS

DEFECTS/POINTS PER OBJECT

DEFECTS VS. OPPORTUNITIES

DEFECT POINTS VS. DEFECT POINT OPPORTUNITIES

CUMULATIVE DEFECT POINTS VS. OPPORTUNITIES

SMOOTHING – CUMULATIVE ANALYSIS

REMEDIATION
 Use Senior Data Architect – Cross Team Focus
• Introduced in week 21 of project
 Model all changes
 Generate DDL from modeling tool
 1 developer dedicated to persistence mapping
• Works for data architect
 Halt functional design/development
• Redesign database
• Sprints dedicated to problem cleanup
 Target: Reduce data defects by at least 75% going forward

RESULTING DEFECTS VS. OBJECTS COMPARISON

OBJECTS & DEFECTS/WEEK COMPARISON

RESULTING DEFECTS/POINTS PER OBJECT
Previous
Scale

DEFECTS PER OBJECT COMPARISON

SMOOTHING – CUMULATIVE OBJECTS VS. DEFECTS

COMPARISON – CUMULATIVE OBJECTS VS. DEFECTS

COMPARATIVE
Measurement
Measurement Period
(Weeks 1 -20)
Control Period (Weeks
21 - 31)
Performance
Improvement
Interval Length (weeks) 20 11
Objects Created 957 1,083
Defects 1,077 38
Defect Opportunities 4,090 4,333
Defect Points 1,696 87
Defect Point Opportunities 8,886 8,991
Average Objects/week 47.85 98.45 205.76%
Average Defects/week 53.85 3.45 1558.82%
Average Defect Points/week 84.80 7.91 1072.18%
Average defects/object 1.13 0.04 3207.37%
Average Defect Opportunities/Week 204.50 393.91
Defects/Opportunity 0.263 0.009 3002.60%
Defect Points/Opportunity 0.191 0.010 1972.46%

THE BOTTOM LINE
 On time completion
 Avoided $3.7 million overrun
 Senior Enterprise Data Architect + Modelling Tools $200K
• Duration of project
 ROI: ($3.7 million – $200K)/$200K = 1,750%
• Had this been done at the beginning of the project, returns would have been
even greater
Would you invest?

ADDITIONAL CONSIDERATIONS
 Data Architects/Modelers MUST be involved in all development projects
• The “glue” that holds everything together
• Understand the complex data relationships
 It is essential to map, document & understand the data ecosystem
 Building data quality into design is much more cost effective than detecting errors
and fixing later.
 Data modeling drives major business value!
• Today’s discussion was only 1 example
 Required:
• Sound project management framework & discipline
• Solid data architecture framework
• Accurate measurement and SMART Metrics!

THANKS!
Any questions?
You can find me at:
ron.huizenga@idera.com
@DataAviator

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