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

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.

Date:
Time:
Presenter:

October 8, 2013
2:00 PM ET/11:00 AM PT
Peter Aiken, Ph.D.

MONETIZING
DATA MANAGEMENT

Unlocking the Value in Your Organization’s
Most Important Asset.

PETER AIKEN WITH JUANITA BILLINGS
FOREWORD BY JOHN BOTTEGA

1

Copyright 2013 by Data Blueprint

Executive Editor at DATAVERSITY.net

Shannon Kempe

2

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3

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4

Peter Aiken, PhD
•
•
•
•
•
•
•
•

25+ years of experience in data
management
Multiple international awards &
recognition
Founder, Data Blueprint (datablueprint.com)
Associate Professor of IS, VCU (vcu.edu)
President, DAMA International (dama.org)
8 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, and the Commonwealth
of Virginia
5

2

MONETIZING
DATA MANAGEMENT

Show Me The Money

Monetizing Data Management

Presented by Peter Aiken, Ph.D.


10124 W. Broad Street, Suite C
Glen Allen, Virginia 23060
804.521.4056

Outline
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

Tweeting now:
#dataed
7

Outline
3. Leveraging Data
8

Five Integrated DM Practice Areas
Data management
processes and
infrastructure

Implementation

Organizational Strategies

Data Program
Coordination

Guidance

Goals

Organizational
Data Integration

Combining multiple
assets to produce
extra value

Organizational-entity
subject area data
integration

Integrated
Models
Achieve sharing of data
within a business area

Data
Stewardship

Standard
Data

Application
Models &
Designs

Provide reliable
data access

Direction

Data Support
Operations

Feedback
Leverage data in organizational activities

Data
Development

Business
Data

Data
Asset Use

Business Value

9

Five Integrated DM Practice Areas
Manage data coherently.
Data Program
Coordination

Share data across boundaries.
Organizational
Data Integration

Data Development

Data Stewardship

Assign responsibilities for data.
Engineer data delivery systems.

Data Support
Operations
Maintain data availability.

10

Hierarchy of Data Management Practices (after Maslow)
•

•

5 Data
management
practices areas /
data management
basics ...

Advanced
Data
Practices

... are necessary
but insufficient
• MDM
prerequisites to
• Mining
• Big Data
organizational data
• Analytics
leveraging
• Warehousing
applications that is
• SOA
self actualizing data
or advanced data
Basic Data Management Practices
practices
–
–
–
–
–

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


Outline
3. Leveraging Data
12

Outline
3. Leveraging Data
13

Motivation ...
•
•

•

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
–
–
–

•

MONETIZING
DATA MANAGEMENT

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



14

2013 Monetizing Data Management Survey Results

15

2013 Monetizing Data Management Survey Results

• Soon to be released: white paper & survey results

16

Outline
3. Leveraging Data
17

Outline
3. Leveraging Data
18

Strategic Information Use: Prerequisites
Wisdom & knowledge are
often used synonymously

Intelligence

Data

Information

Strategic Use

Data
Request

Data
Data
Data
Meaning

Fact
Data
1.
2.
3.
4.
5.
6.

Data

Each FACT combines with one or more MEANINGS.
Each specific FACT and MEANING combination is referred to as a DATUM.
An INFORMATION is one or more DATA that are returned in response to a specific REQUEST
INFORMATION REUSE is enabled when one FACT is combined with more than one MEANING.
INTELLIGENCE is INFORMATION associated with its STRATEGIC USES.
DATA/INFORMATION must formally arranged into an ARCHITECTURE.
[Built on definitions from Dan Appleton 1983]
19

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

20

Data Leverage is an Engineering Concept
Process

Organizational
Data Managers

Organizational
Data

People

Less Data

ROT ->

Technologies

• Note: Reducing ROT increases data leverage
21

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
22

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
23

Evolving Data is Different than Creating New Systems
Common Organizational Data

Future State

(and corresponding data needs requirements)

Evolve

(Version +1)

Data evolution is separate from,
external to, and precedes system
development life cycle activities!
Systems
Development
Activities
Create
New Organizational
Capabilities
24

Application-Centric Development
•

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

Strategy

Goals/
Objectives
Systems/
Applications
Network/
Infrastructure
Data/
Information
Original articulation from Doug Bagley @ Walmart
25

Typical System Evolution
Payroll Data
(database)

Finance Application
(3rd GL, batch
system, no source)

Payroll Application
(3rd GL)
Finance
Data
(indexed)

Marketing Data
(external database)

Marketing Application
(4rd GL, query facilities,
no reporting, very large)

Personnel Data
(database)

R&D
Data
(raw)

Personnel App.
(20 years old,
un-normalized data)

R& D Applications
(researcher supported, no documentation)

Mfg. Data
(home grown
database)

Mfg. Applications
(contractor supported)

26

Data-Centric Development
•
•

•

•
•

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

Strategy

Goals/
Objectives
Data/
Information
Network/
Infrastructure
Systems/
Applications
Original articulation from Doug Bagley @ Walmart
27

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
28

Outline
3. Leveraging Data
29

Outline
3. Leveraging Data
30

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

31

Capture Cost of Labor/Category

32

Compute Labor Costs
District-L (as an example)
Employees

Leave Tracking

Time Accounting

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

$898.49

$5,727.89

$21,563.83

$137,469.40

Total semi-monthly
timekeeper cost
Annual cost

33

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
34

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!
35

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
36

33

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
37

Vocabulary is Important-Tank, Tanks, Tankers, Tanked

38

How one inventory item proliferates data throughout the chain
555
Subassemblies
&
subcomponents

System 1:
18,214 Total items
75 Attributes/ item
1,366,050 Total attributes

17,659
Repair
parts
or
Consumables

System
2
47
Total
items
15+
A>ributes/item
720
Total
a>ributes

System
3
16,594
Total
items
73
A>ributes/item
1,211,362
Total
a>ributes

System
4
8,535
Total
items
16

A>ributes/item
136,560
Total
a>ributes

System
5
15,959

Total
items
22

A>ributes/item
351,098
Total
a>ributes

Total
for
the
ﬁve
systems
show
above:
59,350
Items
179
Unique
a>ributes
3,065,790
values


39

Business Implications
•

National Stock Number (NSN)
Discrepancies
–

–

•

Serial Number Duplication
–

–

•

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
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


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
41

Determining Diminishing Returns
Week #
1

Unmatched
Items
(% Total)
31.47%

Ignorable
Items
(% Total)
1.34%

Items
Matched
(% Total)
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

9.50%

22.62%

67.88%

18

7.46%

22.62%

69.92%
42

Quantitative Benefits
Time needed to review all NSNs once over the life of the project:
NSNs
Average time to review & cleanse (in minutes)
Total Time (in minutes)

2,000,000
5
10,000,000

Time available per resource over a one year period of time:
Work weeks in a year
Work days in a week
Work hours in a day
Work minutes in a day
Total Work minutes/year

48
5
7.5
450
108,000

Person years required to cleanse each NSN once prior to migration:
Minutes needed
Minutes available person/year
Total Person-Years

10,000,000
108,000
92.6

Resource Cost to cleanse NSN's prior to migration:
Avg Salary for SME year (not including overhead)
Projected Years Required to Cleanse/Total DLA Person Year Saved
Total Cost to Cleanse/Total DLA Savings to Cleanse NSN's:

$60,000.00
93
$5.5 million
43

Seven Sisters (from British Telecom)

44

Thanks to Dave Evans

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
45

Outline
3. Leveraging Data
46

Outline
3. Leveraging Data
47

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

48

Suicide Mitigation

49

Data
Suicide Mitigation Mapping
Deploy
ments
Soldier

Work
History
Legal
Issues

Mental
illness

DMSS

DMDC

G1

Abuse

Suicide
Analysis

FAP

CID

MDR

Data objects
complete?

All sources
identified?

Best source for
each object?

How reconcile
differences
between
sources?
12

50

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
51

Communication Patterns
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

52

Polling Question #3

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

53

Outline
3. Leveraging Data
54

Outline
3. Leveraging Data
55

Messy Sequencing Towards Arbitration
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

Realizes a key milestone
has been missed

Stammers an
explanation of "bad"
data

Slows then stops
Defendant invoice
payments

Removes project team

January
July

Files arbitration request
as governed by contract
with Defendant
56

•
•
•
•

•
•

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?

57

Expert Reports

Expert
Report

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
58

FBI & Canadian Social Security Gender Codes

1.
2.
3.
4.
5.
6.
7.
8.
9.

Male
If column 1 in
source = "m"
Female
• then set
Formerly male now female
value of
target data
Formerly female now male
to "male"
Uncertain
• else set
value of
Won't tell
target data
to "female"
Doesn't know
Male soon to be female
Female soon to be male

51


AJHR0213_CAN_UPDATE.SQR

!************************************************************************
! 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.

The defendant knew to
prevent duplicate SSNs

!************************************************************************
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

The exclamation point
prevents this line from
looking for duplicates, so
no check is made for a
duplicate SSN/National
ID

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'
do 231-Check-XXX-for-Empl

!DAR 01/14/04
!DAR 01/14/04

if $found_on_XXX = 'N'

!DAR 01/14/04

add 1 to #last_emplid

Legacy systems business
rules allowed employees to
have more than one
AJ_APPL_ID.

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

60

61

Identified & Quantified Risks

62

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
Tasks
Hours
New Year Conversion
Tax and payroll balance conversion
General Ledger conversion
Total

120
120
80
320

Resource
Hours
G/L Consultant
Project Manager
Recievables Consultant
HRMS Technical Consultant
Technical Lead Consultant
HRMS Consultant
Financials Technical Consultant
Total

40
40
40
40
40
40
40
280

"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."

Delay
Weekly Resources Weeks Tasks Cumulative
January (5 weeks)
280
5 320
1720
February (4 weeks)
280
4
1120
Total
2840
63

Project Management Planning
Process Planning Area
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

Company Y
Methodology
Demonstrated
√
√
√
√
√
√
√
√
√
√
√
√
?
?
√
√
√
√
√
√
?
√
√
√

Company X Lead

?

?

64

Inadequate Standard of Care - Tasks without Predecessors

65

Inadequate Standard of Care

66

Professional & Workmanlike Manner

Defendant warrants that the services
it provides hereunder will be
performed in a professional and
workmanlike manner in accordance
with industry standards.
67

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.
68

Outline
3. Leveraging Data
69

Outline
3. Leveraging Data
70

Monetizing Data Management
•

State Agency Time & Leave Tracking
–

Time and leave tracking
•

•

International Chemical Company
–
–

•

Transformation of non-tabular data
$5 million annually
Person Centuries

British Telecom Project Rollout
–

£250 (small investment)

Non-Monetary Examples
–
–

•

DATA MANAGEMENT

Data management: Test results
$25 million UDS annually

•
•

•

MONETIZING

ERP Implementation
–

•

$1 million USD annually

Friendly Fire
Suicide Mitigation

Legal
–

ERP Implementation Legal Case
•



$ 5,355,450 CAN damages/penalties

71


Questions?

+

=

It’s your turn!
Use the chat feature or Twitter (#dataed) to submit
your questions to Peter now.
72

Upcoming Events
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Novemeber 12, 2013 @ 2:00 PM – 3:30 PM ET
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Data-Ed: Show Me the Money: Monetizing Data Management

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