Data-Ed Webinar: Monetizing Data Management - Show Me the Money

Show Me The Money
Monetizing Data Management 
Presented by 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.
• DAMA International President 2009-2013
• DAMA International Achievement Award 2001 (with
Dr. E. F. "Ted" Codd
• DAMA International Community Award 2005
Peter Aiken, Ph.D.
• 33+ years in data management
• Repeated international recognition
• Founder, Data Blueprint (datablueprint.com)
• Associate Professor of IS (vcu.edu)
• DAMA International (dama.org)
• 10 books and dozens of articles
• Experienced w/ 500+ data
management practices
• Multi-year immersions: 
– US DoD (DISA/Army/Marines/DLA) 
– Nokia 
– Deutsche Bank 
– Wells Fargo 
– Walmart 
– … PETER AIKEN WITH JUANITA BILLINGS
MONETIZING
DATA MANAGEMENT
The Case for the
Chief Data Officer
Recasting the C-Suite to Leverage
Your MostValuable Asset
Peter Aiken and
Michael Gorman
2
Copyright 2017 by Data Blueprint Slide #

Show Me The $ - Monetizing Data Management
1. Data Management Overview
2. Book Motivations
3. Leveraging Data (& Accounting for it)
4. Monetary ROI (6 cases)
5. Non-Monetary ROI (2 cases)
6. Legal Considerations
7. Take Aways and Q&A
3Copyright 2017 by Data Blueprint Slide #
The DAMA Guide to the Data Management Body of Knowledge
4
Data Management Functions
Published by DAMA
International
• The professional
association for Data
Managers (40 chapters
worldwide)
DMBoK organized
around
• Primary data
management functions
focused around data
delivery to the
organization
• Organized around
several environmental
elements

Maslow's Hierarchy of Needs
5
Data Management Practices Hierarchy
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
6
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)

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
7
Manage data coherently
Manage data assets professionally
Data life cycle
management
Organizational support
Data  
Quality
Data  
Governance
Data  
Management 
Strategy
Data  
Operations
Platform 
Architecture
Supporting 
Processes
8
Data  
Quality
3 3
33
1
Strategy is often the
weakest link!
DMM℠ Structure of  
5 Integrated  
DM Practice Areas

Data Assets Win!
Data  
Assets
Financial  
Assets
Real 
Estate Assets
Inventory
Assets
Non-
depletable
Available for
subsequent
use
Can be  
used up
Can be  
used up
Non-
degrading √ √ Can degrade 
over time
Can degrade 
over time
Durable Non-taxed √ √
Strategic
Asset √ √ √ √
• Today, data is the most powerful, yet underutilized and poorly
managed organizational asset
• Data is your
– Sole
– Non-depletable
– Non-degrading
– Durable
– Strategic
• Asset
– Data is the new oil!
– Data is the new (s)oil!
– Data is the new bacon!
• As such, data deserves:
– It's own strategy
– Attention on par with similar organizational assets
– Professional ministration to make up for past neglect
9
Asset: A resource controlled by the organization as a result of past events or
transactions and from which future economic benefits are expected to flow [Wikipedia]
2. Book Motivations

2013 Monetizing Data Management Survey Results
11
2013 Monetizing Data Management Survey Results
12

MONETIZING
DATA MANAGEMENT
Motivation ...
• Amazon rank: 1,477,994 in Books
– #765 in Books > Computers & Technology >  
Business Technology > Management Information Systems
– #641 in Books > Computers & Technology >  
Computer Science > Systems Analysis & Design
• Task
– Helping our community better articulate the importance of what we do
– Until we can meaningfully communicate in monetary or other terms equally important to
the C-suite, we will continue to struggle to articulate the value of its role
• Today’s business executives
– Smart, talented and experienced experts
– Executive decision-makers being far removed and insufficiently data knowledgeable
– Too many decisions about data have been poor
• Four Parts
– Unique perspective to the practice of leveraging data
– 11 cases where leveraging data has produced positive financial results
– Five instance non-monetary outcomes of critical important to the C-suite
– Interaction of data management practices and both IT projects and legal responsibilities
13
Amazon Reviews
14

One Star Reviews
• "My reason for purchasing this book was to learn  
about how organizations are finding ways to monitize their data
assets. By that I mean finding ways to generate income using their
data assets or the insights derived from those assets."
• "This book title 'Monetizing data management', the reason I
purchased this book is to know how to earn the money from
organizational data. however this book didn't talk anything about
making money through data management."
15
MONETIZING
DATA MANAGEMENT
Five Star Reviews
• "A book you can read from cover to cover on an  
airplane trip or during lunch over a period of days. I'm very big on
stories, and the book contains many stories from the authors'
experiences on how to valuate data management. It helped me
brainstorm on a presentation I was working on to explain the value
of our enterprise information management initiative."
• "A concise summary of how to put a value on data management in
your organization. I would not categorize this book as a "how to"
guide - more of a brainstorming book to help someone come up
with a value for their hard data management work. Great stories
and tangible results!"
16
MONETIZING
DATA MANAGEMENT

2. Book Motivations
Data
Data
Data
Information
Fact Meaning
Request
Strategic Information Use: Prerequisites
[Built on definitions from Dan Appleton 1983]
Intelligence
Strategic Use
1. Each FACT combines with one or more MEANINGS.
2. Each specific FACT and MEANING combination is referred to as a DATUM.
3. An INFORMATION is one or more DATA that are returned in response to a specific REQUEST
4. INFORMATION REUSE is enabled when one FACT is combined with more than one MEANING.
5. INTELLIGENCE is INFORMATION associated with its STRATEGIC USES.
6. DATA/INFORMATION must formally arranged into an ARCHITECTURE.
Wisdom & knowledge are  
often used synonymously
Data
Data
Data Data
18

Leverage is an Engineering Concept
• Using proper engineering techniques, a human can lift a bulk that
is weighs much more than the human
19
Data Leverage is an Engineering Concept
• Note: Reducing ROT increases data leverage
20
 
 
 
Organizational
Data
Organizational
Data Managers
Technologies
Process
People
Less Data ROT ->

Why Is Data Management Important?
• Too much data leads directly to wasted productivity
– Eighty percent (80%) of organizational data is
redundant, obsolete or trivial (ROT)
• Underutilized data leads directly to poorly leveraged
organizational resources
– Manpower – costs associated with labor resources and
market share
– Money – costs associated  
with management of  
financial resources
– Methods – costs associated  
with operational processes and product delivery
– Machines – costs associated with hardware, software
applications and data to enhance production capability
21
Incorrect Educational Focus
• Building new systems
– 80% of IT costs are spent rebuilding and evolving  
existing systems and only 20% of costs are  
spent building and acquiring new systems
– Putting fresh graduates on new projects makes this proposition more ridiculous
– Only the most experienced professionals should be allowed to participate in new
systems development.
• Who is responsible for managing data assets?
– Business thinks IT is taking care of it - it is called IT after all?
– IT thinks if you can sign on to the system their job is complete
• System development practices
– Data evolution is separate from, external to and must precede system
development life cycle activities!
– Data is not a project - it has no distinct beginning and end
22

Evolving Data is Different than Creating New Systems
23
Common Organizational Data  
(and corresponding data needs requirements)
New Organizational
Capabilities
Systems
Development
Activities
Create
Evolve
Future State
(Version +1)
Data evolution is separate from,
external to, and precedes system
development life cycle activities!
IT Project or Application-Centric Development
Original articulation from Doug Bagley @ Walmart
24
Data/
Information
IT 
Projects
 
Strategy
• In support of strategy, organizations
implement IT projects
• Data/information are typically
considered within the scope of IT
projects
• Problems with this approach:
– Ensures data is formed to the
applications and not around the
organizational-wide information
requirements
– Process are narrowly formed around
applications
– Very little data reuse is possible

Payroll Application 
(3rd GL)
Payroll Data
(database)
R& D Applications 
(researcher supported, no documentation)
R & D
Data
(raw)
Mfg. Data
(home grown
database)
Mfg. Applications 
(contractor supported)
 
Finance
Data
(indexed)
Finance Application 
(3rd GL, batch  
system, no source)
Marketing Application 
(4rd GL, query facilities,  
no reporting, very large)
 
Marketing Data
(external database)
Personnel App. 
(20 years old, 
un-normalized data)
 
Personnel Data 
(database)
Typical System Evolution
25
Einstein Quote
"The significant
problems we
face cannot be
solved at the
same level of
thinking we were
at when we
created them." 
- Albert Einstein
26

Data-Centric Development
Original articulation from Doug Bagley @ Walmart
27
IT 
Projects
Data/ 
Information
 
Strategy
• In support of strategy, the organization
develops specific, shared data-based
goals/objectives
• These organizational data goals/
objectives drive the development of
specific IT projects with an eye to
organization-wide usage
• Advantages of this approach:
– Data/information assets are developed from an
organization-wide perspective
– Systems support organizational data needs and
compliment organizational process flows
– Maximum data/information reuse
Systems/
Applications
Data/
Information
Strategy
This requires a gradual shift from application to data-centric
28
Data/
Information
Systems/
Applications
Strategy

Monetizing
• Monetization is the process of converting or
establishing something into legal tender.
• It usually refers to the printing of banknotes by
central banks, but things such as gold, diamonds
and emeralds, and art can also be monetized.
• Even intrinsically worthless items can be made
into money, as long as they are difficult to make
or acquire.
29
Task vs. Process Orientation
• What is meant by a task
orientation?
– Industrial work should be broken down
into its simplest and most basic tasks
• What is meant by a process
orientation?
– Reunifying tasks into coherent
business processes
• What else must be part of the
analysis?
– Identify and abandon outdated rules
and assumptions that underlie current
business operations
30
Task 1
Task 2
Task 3
Task 4
Task 5
Task 6
Task 7
Task 8
Task 9
Task 10
Task 11
Task 12
Task 1
Task 7
Task 9

Automating Business Process Discovery (qpr.com)
• Benefits
– Obtain holistic perspective on
roles and value creation
– Customers understand and
value outputs
– All develop better shared
understanding
• Results
– Speed up process
– Cost savings
– Increased compliance
– Increased output
– IT systems documentation
31
Sheena's in color Activity-Based Costing Kills Someone
32

Great inspiration ...
• How to Measure Anything: Finding the Value of  
Intangibles in Business by Douglas Hubbard (ISBN: 0470539399)
• Formalizing stuff forces clarity
• Whatever your measurement problem is,
– it's been done before
• You have more data than you think
• You need less data than you think
• Getting more data is more economical than you think
• You probably need completely different data  
than you think
33
Enrico Fermi (Nobel Prize Physics 1938)
• How many piano tuners in the city of Chicago?
– Count them all (yellow pages, licensing agency)
– Current population of Chicago (3 million at the time)
– Average number of people per household (2 or 3)
– Share of households with regularly tuned pianos (1 in 3)
– Required frequency of tuning (1/year)
– How many pianos can a tuner tune daily? (4 or 5)
– How many days/year are worked (250) 
• Tuners in Chicago = Population/people per household 
X % households with tuned pianos 
X tunings per year/ 
(tunings per tuner per day  
X workdays/year)
34

2. Book Motivations
Monitization: Time & Leave Tracking
36
At Least 300 employees are
spending 15 minutes/week
tracking leave/time

Capture the Cost of Labor/Category
37
District-L (as an example) Leave Tracking Time Accounting
Employees 73 50
Number of documents 1000 2040
Timesheet/employee 13.7 40.8
Time spent 0.08 0.25
Hourly Cost $6.92 $6.92
Additive Rate $11.23 $11.23
Semi-monthly cost per
timekeeper
$12.31 $114.56
Total semi-monthly
timekeeper cost
$898.49 $5,727.89
Annual cost $21,563.83 $137,469.40
Compute Labor Costs - Lynchburg
38
+

Annual Organizational Totals
• Range $192,000 - $159,000/month
• $100,000 Salem
• $159,000 Lynchburg
• $100,000 Richmond
• $100,000 Suffolk
• $150,000 Fredericksburg
• $100,000 Staunton
• $100,000 NOVA
• $800,000/month or $9,600,000/annually
• Awareness of the cost of things considered overhead
39
International Chemical Company Engine Testing
• $1billion (+) chemical company
• Develops/manufactures additives
enhancing the performance of oils
and fuels ...
• ... to enhance engine/machine
performance
– Helps fuels burn cleaner
– Engines run smoother
– Machines last longer
• Tens of thousands of  
tests annually
– Test costs range up to  
$250,000!
40

Overview of Existing Data Management Process
1.Manual transfer of digital data
2.Manual file movement/duplication
3.Manual data manipulation
4.Disparate synonym reconciliation
5.Tribal knowledge requirements
6.Non-sustainable technology
41
1.Manual transfer of digital data
2.Manual file movement/duplication
3.Manual data manipulation
4.Disparate synonym reconciliation
5.Tribal knowledge requirements
6.Non-sustainable technology
Data Integration Solution
• Integrated the existing systems to
easily search on and find similar or
identical tests
• Results:
– Reduced expenses
– Improved competitive edge  
and customer service
– Time savings and improve operational
capabilities
• According to our client’s internal
business case development, they
expect to realize a $25 million gain
each year thanks to this data
integration
42

How one inventory item proliferates data throughout the chain
43
555 Subassemblies & subcomponents
17,659 Repair parts or Consumables
System 1: 
18,214 Total items 
75 Attributes/ item 
1,366,050 Total attributes
System 2 
47 Total items 
15+ Attributes/item 
720 Total attributes
System 3
16,594 Total items
73 Attributes/item
1,211,362 Total attributes
System 4 
16 Attributes/item 
136,560 Total attributes
System 5 
22 Attributes/item 
351,098 Total attributes
Total for the five systems show above: 
59,350 Items 
179 Unique attributes 
3,065,790 values
44

Business Implications
• National Stock Number (NSN)  
Discrepancies
– If NSNs in LUAF, GABF, and RTLS are  
not present in the MHIF, these records  
cannot be updated in SASSY
– Additional overhead is created to correct  
data before performing the real  
maintenance of records
• Serial Number Duplication
– If multiple items are assigned the same  
serial number in RTLS, the traceability of  
those items is severely impacted
– Approximately $531 million of SAC 3  
items have duplicated serial numbers
• On-Hand Quantity Discrepancies
– If the LUAF O/H QTY and number of items serialized in RTLS conflict, there can be
no clear answer as to how many items a unit actually has on-hand
– Approximately $5 billion of equipment does not tie out between the LUAF & RTLS
45
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
46

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
47
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
48

NSNs 2,000,000
49
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
50

Seven Sisters (from British Telecom)
http://www.datablueprint.com/thought-leaders/peter-aiken/book-monetizing-data-management/ [Thanks to Dave Evans]
51
2. Book Motivations

Friendly Fire deaths traced to Dead Battery
In one of the more horrifying incidents I've read about, U.S. soldiers and allies were
killed in December 2001 because of a stunningly poor design of a GPS receiver, plus
"human error."
A U.S. Special Forces air controller was calling in GPS positioning from some sort of
battery-powered device. He "had used the GPS receiver to calculate the latitude and
longitude of the Taliban position in minutes and seconds for an airstrike by a Navy F/
A-18."
According to the *Post* story, the bomber crew "required" a "second 
calculation in 'degree decimals'" -- why the crew did not have equipment to  
perform the minutes-seconds conversion themselves is not explained.
The air controller had recorded the correct value in the GPS receiver when the battery
died. Upon replacing the battery, he called in the degree-decimal position the unit was
showing -- without realizing that the unit is set up to reset to its *own* position when
the battery is replaced. The 2,000-pound bomb landed on his position, killing three
Special Forces soldiers and injuring 20 others.
If the information in this story is accurate, the RISKS involve replacing memory
settings with an apparently-valid default value instead of blinking 0 or some other
obviously-wrong display; not having a backup battery to hold values in memory during
battery replacement; not equipping users to translate one coordinate system to
another; and using a device with such flaws in a combat situation
http://www.washingtonpost.com/wp-dyn/articles/A8853-2002Mar23.html
53
Suicide Mitigation
54

Suicide MitigationData Mapping
12
Mental
illness
Deploy
ments
Work
History
Soldier Legal
Issues
Abuse
Suicide
Analysis
FAPDMSS G1 DMDC CID
Data objects
complete?
All sources
identified?
Best source for
each object?
How reconcile
differences
between
sources?
MDR
55
Potential Data Sources

Senior Army Official
• Room full of Colonels
• A very heavy dose of management support
• Advised the group of his opinion on the matter
• Any questions as to future direction
– "They should make an appointment to speak
directly with me!"
• Empower the team
– The conversation turned from "can this be done?" to "how
are we going to accomplish this?"
– Mistakes along the way would be tolerated
– Implement a workable solution in prototype form
Communication Patterns
58
Source: The Challenge and the Promise: Strengthening the Force, Preventing Suicide and Saving Lives - The Final Report of the Department of
Defense Task Force on the Prevention of Suicide by Members of the Armed Forces - August 2010

2. Book Motivations
Plaintiff 
(Company X)
Defendant 
(Company Y)
April
Requests a
recommendation from
ERP Vendor
Responds indicating
"Preferred Specialist"
status
July
Contracts Defendant to
implement ERP and
convert legacy data
Begins implementation
January
Realizes a key milestone
has been missed
Stammers an
explanation of "bad"
data
July
Slows then stops
Defendant invoice
payments
Removes project team
Files arbitration request
as governed by contract
with Defendant
Messy Sequencing Towards Arbitration
60

Points of Contention
• Who owned the risks?
• Who was the project
manager?
• Was the data of poor
quality?
• Did the contractor
(Company Y) exercise
due diligence?
• Was their
methodology
adequate?
• Were required
standards of care
followed and  
were the work
products of required
quality?
61
Expert Reports
Ours provided evidence that :
1. Company Y's conversion code introduced  
errors into the data
2. Some data that Company Y converted was of
measurably lower quality than the quality of the data
before the conversion
3. Company Y caused harm by not performing an analysis
of the Company X's legacy systems and that that the
required analysis was not a part of any project plan used
by Company Y
4. Company Y caused harm by withholding specific
information relating to the perception of the on-site
consultants' views on potential project success
Expert
Report
62

FBI & Canadian Social Security Gender Codes
1. Male
2. Female
3. Formerly male now female
4. Formerly female now male
5. Uncertain
6. Won't tell
7. Doesn't know
8. Male soon to be female
9. Female soon to be male
If column 1 in
source = "m"
• then set
value of
target data
to "male"
• else set
value of
target data
to "female"
51
63
The defendant knew to
prevent duplicate SSNs
AJHR0213_CAN_UPDATE.SQR
The exclamation point
prevents this line from
looking for duplicates, so
no check is made for a
duplicate SSN/National ID
Legacy systems business
rules allowed employees to
have more than one
AJ_APPL_ID.
!************************************************************************
! Procedure Name: 230-Assign-PS-Emplid
!
! Description : This procedure generates a PeopleSoft Employee ID
! (Emplid) by incrementing the last Emplid processed by 1
! First it checks if the applicant/employee exists on
! the PeopleSoft database using the SSN.
!
!************************************************************************
Begin-Procedure 230-Assign-PS-Emplid
move 'N' to $found_in_PS !DAR 01/14/04
move 'N' to $found_on_XXX !DAR 01/14/04
BEGIN-SELECT -Db'DSN=HR83PRD;UID=PS_DEV;PWD=psdevelopment'
NID.EMPLID
NID.NATIONAL_ID
move 'Y' to $found_in_PS !DAR 01/14/04
move &NID.EMPLID to $ps_emplid
FROM PS_PERS_NID NID
!WHERE NID.NATIONAL_ID = $ps_ssn
WHERE NID.AJ_APPL_ID = $applicant_id
END-SELECT
if $found_in_PS = 'N' !DAR 01/14/04
do 231-Check-XXX-for-Empl !DAR 01/14/04
if $found_on_XXX = 'N' !DAR 01/14/04
add 1 to #last_emplid
let $last_emplid = to_char(#last_emplid)
let $last_emplid = lpad($last_emplid,6,'0')
let $ps_emplid = 'AJ' || $last_emplid
end-if
end-if !DAR 01/14/04
End-Procedure 230-Assign-PS-Emplid
64

65
Identified & Quantified Risks
66

Risk Response
“Risk response development involves defining enhancement steps for
opportunities and threats.”
Page 119, Duncan, W., A Guide to the Project Management Body of Knowledge, PMI, 1996
"The go-live date may need to be
extended due to certain critical
path deliverables not being met.
This extension will require
additional tasks and resources.
The decision of whether or not to
extend the go-live date should be
made by Monday, November 3,
20XX so that resources can be
allocated to the additional tasks."
Tasks Hours
New Year Conversion 120
Tax and payroll balance conversion 120
General Ledger conversion 80
Total 320
Resource Hours
G/L Consultant 40
Project Manager 40
Recievables Consultant 40
HRMS Technical Consultant 40
Technical Lead Consultant 40
HRMS Consultant 40
Financials Technical Consultant 40
Total 280
Delay Weekly Resources Weeks Tasks Cumulative
January (5 weeks) 280 5 320 1720
February (4 weeks) 280 4 1120
Total 2840
67
Outcome
Defendant
Plaintiff $5,000,000.00
Five million Dollars and 00/100 ************************************************** **************** dollars
one big mistake!
• Three days after the hearing, the panel issued a one-page
decision awarding damages of $5 million to Company X
7/11/17
68

Monetizing Data Management
• State Agency Time & Leave Tracking
– Time and leave tracking
• $1 million USD annually
• International Chemical Company
– Data management: Test results
– $25 million UDS annually
• ERP Implementation
– Transformation of non-tabular data
• $5 million annually
• Person Centuries
• British Telecom Project Rollout
– £250 (small investment)
• Non-Monetary Examples
– Friendly Fire
– Suicide Mitigation
• Legal
– ERP Implementation Legal Case
• $ 5,355,450 CAN damages/penalties
69
MONETIZING
DATA MANAGEMENT
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+ =
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Data-Ed Webinar: Monetizing Data Management - Show Me the Money

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