Data-Ed: Monetizing Data Management

Copyright 2013 by Data Blueprint
Show Me The Money: Monetizing Data Management
Failure to successfully monetize data management
investments sets up an unfortunate loop of fixing
symptoms without addressing the underlying
problems. As organizations begin to understand poor
data management practices as the root causes of
many of their business problems, they become more
willing to make the required investments in our
profession. This presentation uses specific examples
to illustrate the costs of poor data management and
how it impacts business objectives. Join us and learn
how you can better align your data management
projects with business objectives to justify funding
and gain management approval.
http://www.datablueprint.com/monetizing-data-management-survey/
1
PETER AIKEN WITH JUANITA BILLINGS
FOREWORD BY JOHN BOTTEGA
MONETIZING
DATA MANAGEMENT
Unlocking the Value in Your Organization’s
Most Important Asset.
Basic Data Management Practices
– Data Program Management
– Organizational Data Integration
– Data Stewardship
– Data Development
– Data Support Operations
Advanced
Data
Practices
• MDM
• Mining
• Big Data
• Analytics
• Warehousing
• SOA
Data
Data
Data
Information
Fact Meaning
Request
[Built on definitions from Dan Appleton 1983]
Intelligence
Strategic Use
Wisdom & knowledge are
often used synonymously
Data
Data
Data Data
18
Organizational
Data
Organizational
Data Managers
Technologies
Process
People
Less Data ROT ->
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!
Data/
Information
Network/
Infrastructure
Systems/
Applications
Goals/
Objectives
Strategy
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)
Systems/
Applications
Network/
Infrastructure
Data/
Information
Goals/
Objectives
Strategy
District-L (as an example) Leave Tracking Time Accounting
Employees 73 50
Number of documents 1000 2040
Timesheet/employee 13.70 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
34
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 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

Executive Editor at DATAVERSITY.net
2
Shannon Kempe

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Show Me The Money
Monetizing Data Management 
Presented by Peter Aiken, Ph.D.
MONETIZING
DATA MANAGEMENT

5
2
• 30+ years of experience in data
management
• Multiple international awards &  
recognition
• Founder, Data Blueprint (datablueprint.com)
• Associate Professor of IS, VCU (vcu.edu)
• (Past) President, DAMA Int. (dama.org)
• 9 books and dozens of articles
• Experienced w/ 500+ data
management practices in 20 countries
• Multi-year immersions with
organizations as diverse as the
US DoD, Nokia, Deutsche Bank, Wells
Fargo, Walmart, and the
Commonwealth of Virginia
Peter Aiken, Ph.D.
The Case for the
Chief Data Officer
Recasting the C-Suite to Leverage
Your MostValuable Asset
Peter Aiken and
Michael Gorman
MONETIZING
DATA MANAGEMENT

1. Data Management Overview
2. Book Motivations & Survey Results
3. Leveraging Data
4. Monetary ROI (6 cases)
5. Non-Monetary ROI (2 cases)
6. Legal Considerations
7. Take Aways and Q&A PETER AIKEN WITH JUANITA BILLINGS
MONETIZING
DATA MANAGEMENT
Outline
6
Tweeting now:
#dataed

3. Leveraging Data
MONETIZING
DATA MANAGEMENT
Outline
7

Data Program  
Coordination
Feedback
Data 
Development
Standard 
Data
Data Management is an Integrated System of Five Practice Areas
Organizational Strategies
Goals
Business 
Data
Business Value
Application  
Models &
Designs
Implementation
Direction
Guidance
8
Organizational 
Data Integration
Data 
Stewardship
Data Support 
Operations
Data  
Asset Use
Integrated 
Models
Leverage data in organizational activities
Data management
processes and
infrastructure
Combining multiple
assets to produce
extra value
Organizational-entity
subject area data
integration
Provide reliable
data access
Achieve sharing of data
within a business area

Five Integrated DM Practices
9
Manage data coherently.
Share data across boundaries.
Assign responsibilities for data.
Engineer data delivery systems.
Maintain data availability.
Data Program  
Coordination
Data 
Development
Organizational 
Data Integration
Data 
Stewardship
Data Support 
Operations

Maslow's
Hierarchiy of
Needs
10

You can accomplish
Advanced Data Practices
without becoming proficient
in the Basic Data
Management Practices
however this will:
• Take longer
• Cost more
• Deliver less
• Present  
greater 
risk
Data Management Practices Hierarchy
Advanced  
Data  
Practices
• MDM
• Mining
• Big Data
• Analytics
• Warehousin
g
• SOA
11
Data Program Management
Data Stewardship Data Development
Data Support Operations
Organizational Data Integration

Data Program  
Coordination
Feedback
Data 
Development
Standard 
Data
Five Integrated DM Practice Areas
Organizational Strategies
Goals
Business 
Data
Business Value
Application  
Models &
Designs
Implementation
Direction
Guidance
12
Organizational 
Data Integration
Data 
Stewardship
Data Support 
Operations
Data  
Asset Use
Integrated 
Models
Leverage data in organizational activities
Data management
processes and
infrastructure
Combining multiple
assets to produce
extra value
Organizational-entity
subject area data
integration
Provide reliable
data access
Achieve sharing of data
within a business area

Five Integrated DM Practice Areas
13
Manage data coherently.
Share data across boundaries.
Assign responsibilities for data.
Engineer data delivery systems.
Maintain data availability.
Data Program
Coordination
Organizational
Data Integration
Data Stewardship Data Development
Data Support
Operations

Hierarchy of Data Management Practices (after Maslow)
– Data Program Management
– Organizational Data Integration
– Data Stewardship
– Data Development
– Data Support Operations
http://3.bp.blogspot.com/-ptl-9mAieuQ/T-idBt1YFmI/AAAAAAAABgw/Ib-nVkMmMEQ/s1600/maslows_hierarchy_of_needs.png
Advanced  
Data  
Practices
• MDM
• Mining
• Big Data
• Analytics
• Warehousin
g
• 5 Data
management
practices areas /
data management
basics ...
• ... are necessary
but insufficient
prerequisites to
organizational data
leveraging
applications that is
self actualizing data
or advanced data
practices

3. Leveraging Data
MONETIZING
DATA MANAGEMENT
Outline
15

3. Leveraging Data
MONETIZING
DATA MANAGEMENT
Outline
16

2013 Monetizing Data Management Survey Results
17

• Soon to be released: white paper & survey results
18
2013 Monetizing Data Management Survey Results

Amazon Reviews
19

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."
20
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!"
21
MONETIZING
DATA MANAGEMENT

Motivation ...
• Amazon rank: 1,257,801
• 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
22
MONETIZING
DATA MANAGEMENT

3. Leveraging Data
MONETIZING
DATA MANAGEMENT
Outline
23

3. Leveraging Data
MONETIZING
DATA MANAGEMENT
Outline
24

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
25

Leverage is an Engineering Concept
26
• Using proper engineering
techniques, a human can lift
a bulk that is weighs much
more than the human

Data Leverage is an Engineering Concept
27
 
 
 
Organizationa
l Data
Organizational
Data Managers
Technologies
Process
People
• Note: Reducing ROT increases data leverage
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
28

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
29

Evolving Data is Different than Creating New Systems
30
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!

Application-Centric Development
Original articulation from Doug Bagley @ Walmart
31
Data/
Information
Network/
Infrastructure
Systems/
Applications
Goals/
Objectives
Strategy
• In support of strategy, organizations develop
specific goals/objectives
• The goals/objectives drive the development
of specific systems/applications
• Development of systems/applications leads
to network/infrastructure requirements
• Data/information are typically considered
after the systems/applications and network/
infrastructure have been articulated
• 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)
32
Typical System Evolution

Einstein Quote
33
"The significant
problems we face
cannot be solved at
the same level of
thinking we were at
when we created
them." 
- Albert Einstein

Data-Centric Development
Original articulation from Doug Bagley @ Walmart
34
Systems/
Applications
Network/
Infrastructure
Data/
Information
Goals/
Objectives
Strategy
• In support of strategy, the organization
develops specific goals/objectives
• The goals/objectives drive the development
of specific data/information assets with an
eye to organization-wide usage
• Network/infrastructure components are
developed to support organization-wide use
of data
• Development of systems/applications is
derived from the data/network architecture
• 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

Polling Question #1
• Who or what
department(s) makes the
decision on investing in
data management
initiatives?
A) IT
B) Supported business area
C) IT and the supported
business area together
D) Office of Chief Data
Officer or Enterprise Data
Office/Equivalent
35

3. Leveraging Data
MONETIZING
DATA MANAGEMENT
Outline
36

3. Leveraging Data
MONETIZING
DATA MANAGEMENT
Outline
37

Monitization: Time & Leave Tracking
38
At Least 300 employees are
spending 15 minutes/week
tracking leave/time

39
Capture Cost of Labor/Category

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
40
Compute Labor Costs

• 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
41
Annual Organizational Totals

International Chemical Company Engine Testing
42
• $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!

43
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
44

Vocabulary is Important-Tank, Tanks, Tankers, Tanked
45

How one inventory item proliferates data throughout the chain
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

• 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
Business Implications

Improving Data Quality during System Migration
48
• 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
49

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
50
Quantitative Benefits

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

Polling Question #2
• Is it hard to obtain
funding for your data
management projects?
A) Yes, because it is hard to
show value
B) Yes, because we have not
aligned with the business
objectives
C) Yes, because no
precedent has been set
D) No, because we can
clearly demonstrate value
52

3. Leveraging Data
MONETIZING
DATA MANAGEMENT
Outline
53

3. Leveraging Data
MONETIZING
DATA MANAGEMENT
Outline
54

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."
http://www.washingtonpost.com/wp-dyn/articles/A8853-2002Mar23.html
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
Friendly Fire
deaths traced
to Dead
Battery 
55

Suicide Mitigation
56

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
57

Senior Army Official
• A very heavy dose of  
management support
• Any questions as to future  
data ownership, "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
58

Communication Patterns
59
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

Polling Question #3
• What percentage of
your data projects are
successful?
A) All
B) 25%
C) 75%
D) none
60

3. Leveraging Data
MONETIZING
DATA MANAGEMENT
Outline
61

3. Leveraging Data
MONETIZING
DATA MANAGEMENT
Outline
62

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
63

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?
64

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
65

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

The defendant knew to
prevent duplicate SSNs
!************************************************************************
! 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
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.
67

68

Identified & Quantified Risks
69

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
70

Process Planning Area Company Y Company X Lead
Methodology Demonstrated
Scope Planning √ √
Scope Definition √ √
Activity Definition √
Activity Sequencing √
Activity Duration Estimation √
Schedule Development √
Resource Planning √ √
Cost Estimating √
Cost Budgeting √
Project Plan Development ?
Quality Planning ? ?
Communication Planning √ √
Risk Identification √ √
Risk Quantification √
Risk Response √ ? ?
Organizational Planning √ √
Staff Acquisition √
Project Management Planning
71

Inadequate Standard of Care - Tasks without Predecessors
72

Inadequate Standard of Care 
73

Professional & Workmanlike Manner
74
Defendant warrants that the services
it provides hereunder will be
performed in a professional and
workmanlike manner in accordance
with industry standards.

The Defense's "Industry Standards"
• Question:
– What are the industry standards that you are referring to?
• Answer:
– There is nothing written or codified, but it is the standards
which are recognized by the consulting firms in our (industry).
• Question:
– I understand from what you told me just a moment ago that
the industry standards that you are referring to here are not
written down anywhere; is that correct?
• Answer:
– That is my understanding.
• Question:
– Have you made an effort to locate these industry standards
and have simply not been able to do so?
• Answer:
– I would not know where to begin to look.
75

3. Leveraging Data
MONETIZING
DATA MANAGEMENT
Outline
76

3. Leveraging Data
MONETIZING
DATA MANAGEMENT
Outline
77

Monetizing Data Management
78
• 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
MONETIZING
DATA MANAGEMENT

Questions?
79
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=
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Data-Ed: Monetizing Data Management

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Data-Ed: Monetizing Data Management