DataEd Slides: Expressing Data Improvements as Business Outcomes

© Copyright 2020 by Peter Aiken Slide # 1paiken@plusanythingawesome.com+1.804.382.5957 Peter Aiken, PhD
Expressing Data
Improvements as
Business Outcomes
Question: How do you get your data initiatives funded repeatedly?
• I've been doing this a long time
• My work is recognized as useful
• Associate Professor of IS (vcu.edu)
• Founder, Data Blueprint (datablueprint.com)
• DAMA International (dama.org)
• MIT CDO Society (iscdo.org)
• Anything Awesome (plusanythingawesome.com)
• 11 books and dozens of articles
• Experienced w/ 500+ data
management practices worldwide
• Multi-year immersions
– US DoD (DISA/Army/Marines/DLA)
– Nokia
– Deutsche Bank
– Wells Fargo
– Walmart …
© Copyright 2020 by Peter Aiken Slide # 2https://plusanythingawesome.com
Peter Aiken, Ph.D.
PETER AIKEN WITH JUANITA BILLINGS
FOREWORD BY JOHN BOTTEGA
MONETIZING
DATA MANAGEMENT
Unlocking the Value in Your Organization’s
Most Important Asset.

3
Program
© Copyright 2020 by Peter Aiken Slide #https://plusanythingawesome.com
Expressing Data Improvements
as Business Outcomes
• Business Case
– Leverage
– Leveraging data in order to ___
– Refocus the request around
business outcomes
• Each case has (at least) a
dual purpose
– Make the case that this will
fix the problem
– Illustrate why a programmatic
approach is preferable
• What are we getting
better at?
– Data challenges are the root
cause of most IT and business
failures
– 2 part example: healthcare.gov
– Assessing the limits of
technology-based approaches
•
• Data evolution must be
distinct from IT projects
– Results are not admirable
– Sequencing is mandatory
– Data leadership agenda
(as an example)
• How do we get better?
– Leadership
– Program
– Math
– Engineering/Architecture
– Storytelling
– Practice
• Takeaways and Q&A
Separating the Wheat from the Chaff
• Better organized data increases in value
• Poor data management
practices are costing
organizations much money/time/effort
• Minimally 80% of organizational data is ROT
– Redundant
– Obsolete
– Trivial
• The question is
– Which data to eliminate?
Incomplete
https://plusanythingawesome.com 4

Leverage is an Engineering Concept
• Using proper engineering techniques, a human can lift a bulk that
is weighs much more than the human
A wholistic approach to obtaining data leverage
Organizational
Data
Knowledge workers
supplemented by
data professionals
Process
Guided by strategy
https://www.computerhope.com/jargon/f/framework.htm
People
Technology
Reducing ROT increases data leverage

Data Leverage is a multi-use concept
• Permits organizations to better manage their data
– Within the organization, and
– With organizational data exchange partners
– In support of the organizational mission
• Leverage
– Obtained by implementation of data-centric
technologies, processes, and human skill sets
– Focus on the non-ROT data
• The bigger the organization, the greater potential leverage exists
• Treating data more asset-like simultaneously
– Lowers organizational IT costs and
– Increases organizational knowledge worker productivity
© Copyright 2020 by Peter Aiken Slide #https://plusanythingawesome.com 8https://www.forbes.com/sites/ciocentral/2019/01/02/what-we-learned-from-top-execs-about-their-big-data-and-ai-initiatives/
2020
0
0.25
0.5
0.75
1
% of challenges: technology % of challenges: people/process
90%
10%
Culture's impact
• 2019 challenges
– 5% technology
– 95% people/process
• 2020 challenges
– 10% technology
– 95% people/process

Clean some data
Decrease the number of
undeliverable targeted
marketing ads
Reorganize the database
Increase the ability of the
salesforce to
perform their own analyses
Develop a taxonomy
Create a common vocabulary
for the organization
Optimize a query
Shaved 1 second off a task that
runs a billion times a day
Reverse engineer the legacy
system
Understand: what was good
about the old system so it can
be formally preserved and,
what was bad so it can be
improved
Compare
10
Program
• Business Case
– Leverage
business outcomes
dual purpose
fix the problem
better at?
failures
•
(as an example)
– Leadership
– Program
– Math
– Storytelling
– Practice

Simple Math
• At the beginning of a project,
• Where the parties know the least about each other
• All are expected to agree on the meaning of price, timing, and
functionalities
• Define X (some resources)
• Define Y (cleaning 1 set of data)
• Define Z (that data will be clean)
If X is invested in Y then outcome Z will result (Z > X)
Simple Math
• Define X ($100)
• Define Y (cleaning 1 set of data)
• Define Z ($1000)
If $100 is invested in cleaning 1 set of data then outcome $1000 will result

Data programmes preceding software development
Common Organizational Data
(and corresponding data needs requirements)
New Organizational
Capabilities
Systems
Development
Activities
Build
Evolve
Future State
(Version +1)
Data evolution is separate from,
external to, and precedes system
development life cycle activities!
Data management
and software
development must
be separated and
sequenced
13
Mismatched railroad tracks non aligned
Data programmes preceding software development
14

Data is not a Project
• Durable asset
– An asset that has a usable
life more than one year
• Reasonable project
deliverables
– 90 day increments
– Data evolution is measured in years
• Data
– Evolves - it is not created
– Significantly more stable
• Readymade data architectural components
– Prerequisite to agile development
• Only alternative is to create additional data siloes!
© Copyright 2020 by Peter Aiken Slide #https://plusanythingawesome.com 15
Differences between Programs and Projects
• Programs are Ongoing, Projects End
– Managing a program involves long term strategic planning and
continuous process improvement is not required of a project
• Programs are Tied to the Financial Calendar
– Program managers are often responsible for delivering
results tied to the organization's financial calendar
• Program Management is Governance Intensive
– Programs are governed by a senior board that provides direction,
oversight, and control while projects tend to be less governance-intensive
• Programs Have Greater Scope of Financial Management
– Projects typically have a straight-forward budget and project financial
management is focused on spending to budget while program planning,
management and control is significantly more complex
• Program Change Management is an Executive Leadership
Capability
– Projects employ a formal change management process while at the program
level, change management requires executive leadership skills and program
change is driven more by an organization's strategy and is subject to market
conditions and changing business goals
Adapted from http://top.idownloadnew.com/program_vs_project/ and http://management.simplicable.com/management/new/program-management-vs-project-management
16
Your data program
must last at least as
long as your HR
program!

17
Program
• Business Case
– Leverage
business outcomes
dual purpose
fix the problem
better at?
failures
•
(as an example)
– Leadership
– Program
– Math
– Storytelling
– Practice
Implementation
DataLeadership
Feedback
Feedback
Data
Governance
Data
Improvement
DataStewards
DataCommunityParticipants
DataGenerators/DataUsers
Data
Things
Happen
Organizational
Things
Happen
DIPs
Data Improves
Over
Time
Data Improves
As A Result of
Focus
≈
≈
≈
≈
≈
≈
≈
≈
≈
≈
≈
≈
≈
≈
≈
≈
≈
≈
≈
≈
≈
≈
X
$
X
$
X
$
X
$
X
$
X
$
X
$
X
$
X $

DataManagement
BodyofKnowledge(DMBoKV2)
Practice
Areas
from The DAMA Guide to the Data Management Body of Knowledge 2E © 2017 by DAMA International
Cycle 1
Data
Strategy
Data
Governance
BI/
Warehouse
Perfecting
operations in 3
data management
practice areas
1X
1X
1X
Metadata
Data
Quality

Cycle 2
Data
Strategy
Data
Governance
BI/
Warehouse
Perfecting
operations in 3
data management
practice areas
Metadata
2X
2X
1X
Cycle 3
Data
Strategy
Data
Governance
BI/
Warehouse
Reference
& Master
Data
Perfecting
operations in 3
data management
practice areas
3X
3X
1X

© Copyright 2020 by Peter Aiken Slide #
Data$Management$
Strategy
Data Management Goals
Corporate Culture
Data Management Funding
Data Requirements Lifecycle
Data
Governance
Governance Management
Business Glossary
Metadata Management
Data
Quality
Data Quality Framework
Data Quality Assurance
Data
Operations
Standards and Procedures
Data Sourcing
Platform$&$
Architecture
Architectural Framework
Platforms & Integration
Supporting$
Processes
Measurement & Analysis
Process Management
Process Quality Assurance
Risk Management
Configuration Management
Component Process$Areas
Data
Governance
Data
Management
Strategy
Data
Operations
Platform
Architecture
Supporting
Processes
Maintain fit-for-purpose data,
efficiently and effectively
23https://plusanythingawesome.com
Manage data coherently
Manage data assets professionally
Data life cycle
management
Organizational support
Data
QualityData$Management$
Strategy
Corporate Culture
Data
Governance
Business Glossary
Metadata Management
Data
Quality
Data
Operations
Data Sourcing
Platform$&$
Architecture
Supporting$
Processes
Process Management
Risk Management
Data$Management$
Strategy
Corporate Culture
Data
Governance
Business Glossary
Metadata Management
Data
Quality
Data
Operations
Data Sourcing
Platform$&$
Architecture
Supporting$
Processes
Process Management
Risk Management
Data$Management$
Strategy
Corporate Culture
Data
Governance
Business Glossary
Metadata Management
Data
Quality
Data
Operations
Data Sourcing
Platform$&$
Architecture
Supporting$
Processes
Process Management
Risk Management
Data$Management$
Strategy
Corporate Culture
Data
Governance
Business Glossary
Metadata Management
Data
Quality
Data
Operations
Data Sourcing
Platform$&$
Architecture
Supporting$
Processes
Process Management
Risk Management
Data$Management$
Strategy
Corporate Culture
Data
Governance
Business Glossary
Metadata Management
Data
Quality
Data
Operations
Data Sourcing
Platform$&$
Architecture
Supporting$
Processes
Process Management
Risk Management
Data architecture
implementation
DMM℠ Structure of
5 Integrated
DM Practice Areas
Data architecture
implementation
Data
Governance
Data
Management
Strategy
Data
Operations
Platform
Architecture
Supporting
Processes
Maintain fit-for-purpose data,
efficiently and effectively
Manage data coherently
Manage data assets professionally
Data life cycle
management
Organizational support
Data
Quality
Data
Governance
Data
Quality
Platform
Architecture
Data
Operations
Data
Management
Strategy
3 3
33
1
Supporting
Processes
Optimized
Measured
Defined
Managed
Initial
Optimized
Measured
Defined
Managed
Initial
Optimized
Measured
Defined
Managed
Initial
Optimized
Measured
Defined
Managed
Initial
Your data foundation
can only be as strong
as its weakest link!
Optimized
Measured
Defined
Managed
Initial

healthcare.gov Data challenges?
• 55 Contractors!
• 6 weeks from launch and
requirements not finalized
• "Anyone who has written a
line of code or built a system
from the ground-up cannot be
surprised or even
mildly concerned that
Healthcare.gov did not work
out of the gate," Standish Group International
Chairman Jim Johnson said in a podcast
• "The real news would have
been if it actually did work.
The very fact that most of it
did work at all is a success in
itself."
• "It was pretty obvious from the
first look that the system
hadn't been designed to work
right," says Marty Abbott. "Any
single thing that slowed down
would slow everything down."
• Software programmed to
access data using
traditional technologies
• Data components
incorporated
"big data technologies"
http://www.slate.com/articles/technology/bitwise/2013/10/
problems_with_healthcare_gov_cronyism_bad_management_
and_too_many_cooks.html
!
!
!
!

Improving Data Quality during System Migration
• Challenge
– Millions of NSN/SKUs
maintained in a catalog
– Key and other data stored in
clear text/comment fields
– Original suggestion was manual
approach to text extraction
– Left the data structuring problem unsolved
• Solution
– Proprietary, improvable text extraction process
– Converted non-tabular data into tabular data
– Saved a minimum of $5 million
– Literally person centuries of work
Unmatched
Items
Ignorable
Items
Items
Matched
Week # (% Total) (% Total) (% Total)
1 31.47% 1.34% N/A
2 21.22% 6.97% N/A
3 20.66% 7.49% N/A
4 32.48% 11.99% 55.53%
… … … …
14 9.02% 22.62% 68.36%
15 9.06% 22.62% 68.33%
16 9.53% 22.62% 67.85%
17 9.5% 22.62% 67.88%
18 7.46% 22.62% 69.92%
Determining Diminishing Returns
Before
After

Time needed to review all NSNs once over the life of the project:
NSNs 2,000,000
Average time to review & cleanse (in minutes) 5
Total Time (in minutes) 10,000,000
Time available per resource over a one year period of time:
Work weeks in a year 48
Work days in a week 5
Work hours in a day 7.5
Work minutes in a day 450
Total Work minutes/year 108,000
Person years required to cleanse each NSN once prior to migration:
Minutes needed 10,000,000
Minutes available person/year 108,000
Total Person-Years 92.6
Resource Cost to cleanse NSN's prior to migration:
Avg Salary for SME year (not including overhead) $60,000.00
Projected Years Required to Cleanse/Total DLA Person Year Saved 93
Total Cost to Cleanse/Total DLA Savings to Cleanse NSN's: $5.5 million
Quantitative Benefits
NSNs 2,000,000
NSNs 150,000
Total Time (in minutes) 750,000
Minutes needed 750,000
Total Person-Years 7
Total Cost to Cleanse/Total DLA Savings to Cleanse NSN's: $420,000

NSNs 2,000,000
32
Program
• Business Case
– Leverage
business outcomes
dual purpose
fix the problem
better at?
failures
•
(as an example)
– Leadership
– Program
– Math
– Storytelling
– Practice

IT Project Failure Rates (1994-2018)
Source: Standish Chaos Reports as reported at: http://standishgroup.com
0%
15%
30%
45%
60%
1994 1996 1998 2000 2002 2004 2006 2008 2010 2011 2012 2013 2014 2015 2016 2017
Failed Challenged Succeeded
Evil Dentist

Chaos Resolution by Project Size (2011-2015)
0%
17.5%
35%
52.5%
70%
Grand Large Medium Moderate Small
Successful Challenged Failed
http://www.infoq.com/articles/standish-chaos-2015
35
1 The project needs to be small Projects should not be allowed to
begin unless the data
requirements for the entire
project are verified
2 The product Owner or sponsor
must be highly skilled
Few in IT have the requisite data
skills and knowledge
3 The process must be agile Agile is a construction technique/
data requires more planning
before construction
4 The agile team must be highly
skilled in both the agile process
and the technology
Few agile teams have requisite
levels of data skills
5 The organization must be highly
skilled at emotional maturity
Few organizations understand
data stuff
Standish–Five Cards of a winning hand for IT project success

Enforced sequence
• Before further construction could proceed
• No IT equivalent
You can accomplish
Advanced Data Practices
without becoming proficient
in the Foundational Data
Practices however
this will:
• Take longer
• Cost more
• Deliver less
• Present
greater
risk
(with thanks to
Tom DeMarco)
Unenforced sequence
Advanced
Data
Practices
• MDM
• Mining
• Big Data
• Analytics
• Warehousing
• SOA
Foundational Data Practices
Data Platform/Architecture
Data Governance Data Quality
Data Operations
Data Management Strategy
Technologies
Capabilities
https://plusanythingawesome.com 38

V1
Organizations
without
a formalized
data focus
V3
Data Focus: Use data
to create strategic
opportunities
V4
Data Focus: both
Improve Operations
Innovation
Unenforced sequence
Only 1 is 10 organizations has a board
approved data strategy!
V2
Data Focus: Increase
organizational efficiencies/
effectiveness
X
X
39
CDO Agenda
Inventory Data -> uncovering
assets & decreasing ROT
Develop the first version of an
organizational data strategy
Monetize your organization's data
The CDOs goal is to better manage data as an organizational
asset in support of the organizational mission!

Data Inventory
• When will it be done?
– Sounds like a task or a project
– Data is not a project
– No organization has ever
completed a data inventory!
• Reframe the question
– How rapidly can we achieve
the required capabilities?
– What sort of preexisting
classification frameworks
can be used to jumpstart?
– How often does each classification
require reassessment?
https://www.everteam.com/en/building-your-data-inventory/
https://www.travelers.com/resources/cyber-security/data-assessment-inventory-and-classification
https://plusanythingawesome.com
Data Asset Inventory (Implementation)
1. Purpose is the goal of understanding, not definitions
– Definitions are passive, purpose statements incorporate strategic elements,
the rationale and justification based on the need for data
2. The sharing of inventoried data assets are categorized as:
A. Data items that are shared with external organizations
B. Data items that are shared within the organization
C. Data items that are not shared but are used to derive shared data items
D. Data items not shared outside but used to support workgroup activities
E. Organizational data ROT
3. Assign each data asset inventoried, an existing subject area from which that
data item best supports the organizational mission (ex. PAY is part of BACK
OFFICE OPERATIONS) – based on (refine-able) purpose statements, primary
subject-area allegiance is posited
4. Identify, de-dupe and harmonize data assets participating in synonyms/
homonym/other challenges - ensure only one item is designated as a
(current) golden source
5. Identify which data items are deemed to be sensitive or personal data items
and what specific controls need to be in place
6. Document all mapping rules for data items in categories 2A and 2B above
Note: this exercise cannot be comprehensively performed in a single cycle so equally as important as the exercise itself, a
processing system needs to be established so that as other data items are inevitably discovered, this inventory can be easily
updated

43
Program
• Business Case
– Leverage
business outcomes
dual purpose
fix the problem
better at?
failures
•
(as an example)
– Leadership
– Program
– Math
– Storytelling
– Practice
The Enterprise Data Executive Takes One for the Team

(Things that further)
Organizational Strategy
Lighthouse Projects Provide Program Focus
(OccasionstoPractice)
NeededDataSkills
(Opportunitiestoimprove)
Datausebythebusiness
45
Math
• VCU
– $5 million 35 year
faculty member
– +$20 million in grants/
funded research
projects/student
supplemental salaries
• Collaborations
– $0
– +$1.5 billion
documented savings

Engineering
Architecture
Engineering/Architecting
Relationship
• Architecting is used to create
and build systems too complex
to be treated by engineering
analysis alone
– Require technical details as the
exception
• Engineers develop the
technical designs
– Engineering/Crafts-persons deliver
components supervised by:
• Manufacturer
• Building Contractor
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30

A Musical Analogy
+ =
https://www.youtube.com/watch?v=4n1GT-VjjVs&frags=pl%2Cwn

51
Program
• Business Case
– Leverage
business outcomes
dual purpose
fix the problem
better at?
failures
•
(as an example)
– Leadership
– Program
– Math
– Storytelling
– Practice
It Seemed Simple
• Please add a new field
– NEW field (E) = A + B/C
– Where A is sourced from 1 of 6 systems
– B is another customer record sourced from 1 of 6 systems, and
– C is data provided by a vendor
• Data challenges
– Some loans were missing field A
– Others were missing field B
– Others were missing the vendor-provided value for C
• Explanation
– We reached out to other systems to populate missing fields, and
– We created new matching routines to grab data from disparate
loan records
– After months of effort and bringing resolution to everything we
could, we were finally ready to go to user acceptance testing

It Seemed Simple
• Before approval of user acceptance testing, the team was asked
one question:
– For how many loans were we able to calculate the new field?
• The response:
– 43%
• Next question:
– Why only 43%?
• Responses:
– We still have bad and missing data
– We resolved everything we could
– We do not have the required fields populated for all loans; therefore, the
calculation does not return a value
– We know it sounds low, but we double-checked and
all requirements have been satisfied
It Seemed Simple
• Team challenged to conduct deeper dive/provide detailed metrics
—not typical of developers
– Met the challenge/produced metrics around missing data
– Shared numbers with client, who agreed: 43% did not meet expectations!
• More analyses-after several iterations:
– Updated business rules to accommodate the “bad data”
– Used alternate fields
when field A, B or C
was blank
– Discovered/corrected
zero value errors
– We identified vendor
errors on column C
– At user acceptance, we increase the rate of “Computed a valid New Field
Calc(E)” from 43 % to 88% and
– Explain every scenario of accounts unable to calculate the new field

Data-Centric Perspective
• Measure success differently
• Same project
• Same process
• Different measures for success
– Asking if our data is correct;
– Valuing data more than
we value "on time
and within budget";
– Valuing correct data
more than correct
processes; and
– Auditing data rather
than project documents
– $50 million annually!
• Articulation by Linda Bevolo
Program
• Business Case
– Leverage
business outcomes
dual purpose
– Make the case that this
fix the problem
better at?
failures
– ERP implementation (customize
versus tailoring)
•
– Data leadership agenda (as an
example)
– Leadership
– Program
– Math
– Storytelling
– Practice
• Business Case
– Leverage
business outcomes
dual purpose
– Make the case that this
fix the problem
better at?
failures
– ERP implementation (customize
versus tailoring)
•
– Data leadership agenda (as an
example)
– Leadership
– Program
– Math
– Storytelling
– Practice

Getting (Re)started with Data Stewardship
8 September 8, 2020
Essential Metadata Strategies
13 October 13, 2020
Getting Data Quality Right - Success Stories
10 November 2020
Necessary Prerequisites to Data Success:
Exorcising the Seven Deadly Data Sins
8 December 2020
© Copyright 2020 by Peter Aiken Slide # 57
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