Monetizing data management 09162010

10/4/2010

Monetizing Data Management
Dr. Peter Aiken
CEO and Founding Director, Data Blueprint
President, DAMA International
Associate Professor of Information Systems, Virginia Commonwealth University

Abstract: Monetizing Data Management

Organizations have lost millions due to poor data management
practices, but remain unaware of the root causes of their losses.
Unless IT professionals can monetize these lost opportunities
and their related costs, gaining executive-level approval for
basic data management investments will continue to be difficult.
This sets up an unfortunate loop: executive management is
focused on fixing symptoms, but cannot address the underlying
problems. This talk illustrates how to identify specific costs of
poor data management practices using examples from HR,
Financial, Supply Chain, and Compliance. As organizations
understand poor data management practices as the root cause
of many of their problems, they will be more than willing to make
the required investments in our profession.
PAGE 2

10/4/2010 © Copyright this and previous years by Data Blueprint - all rights reserved!

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10/4/2010

Speaker Bio

Dr. Peter Aiken is an award-winning, internationally
recognized thought leader in the areas of organizational data
management, architecture, and engineering. As a practicing
data manager, consultant, author and researcher, he has
been actively performing and studying these areas for more
than 25 years. He has held leadership positions with the US
Department of Defense and consulted with more than 50
organizations in 17 different counties. Dr. Aiken is the current
president of DAMA International, Associate Professor in
Virginia Commonwealth University’s Information Systems
Department and the Founding Director of Data Blueprint, an
IT consulting and data management firm based in Richmond,
Virginia.
PAGE 3


Monetizing - from Wikipedia
• 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, emerald 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.

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10/4/2010

Root Cause Analysis
• Symptom of the
problem
– The weed
– Above the surface
– Obvious
• The underlying Cause
– The root
– Below the surface
– Not obvious
• Poor Information Management
Practices
– Did not hire Adastra!

PAGE 5


Expanding DM Scope

DataBase Administration (DBA) ≈ 1950-1970 Data Enterprise Data
Administration Data Management
Database design Database operation (DA) Administration (DM)
≈ 1970- (EDA) ≈ 2000-
1990 ≈ 1990-2000
Data requirements analysis
Data modeling
Organization-wide DM coordination
Organization-wide data integration
Data stewardship, Data use

Data Governance, Data Quality,
Data Security, Analytics, Data Compliance,
Data Mashups, Business Rules (more ...)
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10/4/2010

Data Management Involvement

Data Warehousing

XML

Data Quality

Customer Relationship
Management

Master Data Management

Customer Data Integration

Enterprise Resource Planning

Enterprise Application Integration

Value Title

Initiative Leader Initiative Involvement Not Involved
PAGE 7


Niccolo Machiavelli
(1469-1527)
1469-

He who doesn’t lay his
foundations before
hand, may by great
abilities do so
afterward, although with
great trouble to the
architect and danger to the
building.
Machiavelli, Niccolo. The Prince. 19 Mar. 2004 http://pd.sparknotes.com/philosophy/prince

PAGE 8


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10/4/2010

Look Familiar?

PAGE 9


A Model Specifying Relationships Among Important Terms

Wisdom & knowledge are
often used synonymously

Intelligence
Data

Information Use
Data

Data Request
Data
Data

Fact Meaning
Data Data
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 USES. [Built on definition by Dan Appleton 1983]
PAGE 10


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10/4/2010

Date: Tue, 26 Mar 2002 10:47:52 -0500
From: Jamie McCarthy <jamie@mccarthy.vg>
Subject: 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."

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.
Friendly Fire
The air controller had recorded the correct value in the GPS receiver when
the battery died. Upon replacing the battery, he called in the deaths traced
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. to Dead
The 2,000-pound bomb landed on his position, killing three Special Forces Battery
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 (reminiscent of the Mars Climate
Orbiter slamming into the planet when ground crews confused English with
metric); and using a device with such flaws in a combat situation
PAGE 11


Academic Research Findings

A 10% improvement in data
usability on productivity
(increases sales per employee by
14.4% or $55,900)

Measuring the Business Impacts of Effective Data by Anitesh Barua, Deepa Mani, Rajiv Mukherjee
PAGE 12


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10/4/2010


Projected increase in sales (in
$M) due to 10% improvement in
data usability on productivity
(sales per employee)

PAGE 13


Projected impact of a 10%
improvement in data quality and
sales mobility on Return on Equity


PAGE 14


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10/4/2010


Projected Impact of a 10% increase
in intelligence and accessibility of
data on Return on Assets

PAGE 15


Monetization: Time & Leave Tracking

At Least 300 employees are
spending 15 minutes/week
tracking leave/time

PAGE 16


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10/4/2010

Capture Cost of Labor/Category

PAGE 17

Computer Labor as Overhead

Routine Data Entry
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

PAGE 18


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10/4/2010

Annual Organization 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!
PAGE 19


Challenge

• "Green screen" legacy system to be replaced with
Windows Icons Mice Pointers (WIMP) interface; and
• Major changes to operational processes
– 1 screen to 23 screens
• Management didn't think workforce could adjust to
simultaneous changes
– Question: "How big a change will it be to replace all instances
of person_identifier with social_security_number?"
• Answer:
– (from "big" consultants) "Not a very big change."
PAGE 20


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10/4/2010

Reverse Engineering PeopleSoft
implementation Component
representation metadata integration Metadata Uses

• System Structure
Installed
PeopleSoft
Metadata -
• Queries to System requirements
PeopleSoft
workflow metadata verification and
Internals
system change
TheMAT
analysis

system structure metadata • Data Metadata - data
• PeopleSoft post conversion, data
external derivation
security,and user
RDBM metadata
analysis training
Tables
and
integration • Workflow Metadata -
business practice
• Printed analysis and
PeopleSoft realignment
Datamodel
PAGE 21
data metadata

PeopleSoft Process Metadata

Home Page Name Home Page

(relates to one or more)

Business Process
Business Process Name
Name


Business Process
Business Process Component Name Component


Business Process Component Step Name Business Process
Component Step

PAGE 22


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10/4/2010

Example Query Outputs
PAGE 23

10/4/2010
- datablueprint.com
© Copyright this and previous years by Data Blueprint - all rights reserved! 9/8/2010 © Copyright this and previous years by Data Blueprint - all rights reserved!

Data processes homepages menugroups
(39) (7) (8)
Metadata (41) (8)

Structure (182) (86)

components stepnames menunames
(180) (822) (86)
(949)

(847) (281)

panels menuitems menubars
(1421) (1149) (31)
(1916) (1259)

(25906)
(5873)
(264)
fields records parents
(7073) (2706) (264)

(708) (647)
(647)
(347) reports children
(347) (647)

PAGE 24


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10/4/2010

Resolution

Quantity System Time to make Labor Hours
Component change
1,400 Panels 15 minutes 350
1,500 Tables 15 minutes 375
984 Business 15 minutes 246
process
component
steps
Total 971
X $200/hour $194,200
X 5 upgrades $1,000,000
PAGE 25


An Iterative Approach to MDM Structuring
Unmatched Unmatched Ignorable Ignorable Avg Items Matched
Items Items Items Extracted
Rev (% Total) NSNs (% Total) Items Per Item (% Total) Items
# Matched Extracted
1 329948 31.47% 14034 1.34% N/A N/A N/A 264703

2 222474 21.22% 73069 6.97% N/A N/A N/A 286675

3 216552 20.66% 78520 7.49% N/A N/A N/A 287196

4 340514 32.48% 125708 11.99% 582101 1.1000221 55.53% 640324

… … … … … … … … …

14 94542 9.02% 237113 22.62% 716668 1.1142914 68.36% 798577

15 94929 9.06% 237118 22.62% 716276 1.1139281 68.33% 797880

16 99890 9.53% 237128 22.62% 711305 1.1153007 67.85% 793319

17 99591 9.50% 237128 22.62% 711604 1.1154392 67.88% 793751

18 78213 7.46% 237130 22.62% 732980 1.2072812 69.92% 884913

PAGE 26


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10/4/2010

Quantitative Benefits
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
PAGE 27


Messy Sequencing Towards Arbitration
Plaintiff Defendant
(Company X) (Company Y)
April Requests a Responds indicating
recommendation from "Preferred Specialist"
ERP Vendor status
July Contracts Defendant to Begins
implement ERP and implementation
convert legacy data
January Realizes a key Stammers an
milestone has been explanation of "bad"
missed data
July Slows then stops Removes project team
Defendant invoice
payments
Files arbitration request
as governed by contract
with Defendant
PAGE 28


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10/4/2010

FBI & Canadian Social Security Gender Codes
1. Male
2. Female
3. Formerly male now female If column 1 in
source = "m"
4. Formerly female now male •then set value
5. Uncertain of target data
to "male"
6. Won't tell •else set
value of target
7. Doesn't know data to
8. Male soon to be female "female"

9. Female soon to be male
Hypothesized extensions contributed by a Chicago DAMA Member
10.Psychologically female, biologically male
11.Psychologically male, biologically female
12.Both soon to be female
13.Both soon to be male
PAGE 29


220-
220-Process_Emp_Data
More Examples - State

An exclamation point indicates
! if $state = ' ' or $state = '' that anything to the right will not
! move 'State' to $blank_field be executed (“commented out”)
! move 'Y' to $blank_state
! do 221-Blank-Field-Error
! end-if To protect data quality
if $state = '' the program should use the
221-Blank-Field-Error
move ' ' to $state
Procedure
end-if

If there is no state, then
this code makes the state
a space

PAGE 30


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10/4/2010

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 The defendant knew to
! First it checks if the applicant/employee exists on
! the PeopleSoft database using the SSN. prevent duplicate SSNs
!
!************************************************************************
Begin-Procedure 230-Assign-PS-Emplid

move 'N' to $found_in_PS !DAR 01/14/04
The exclamation point
move 'N' to $found_on_XXX !DAR 01/14/04
prevents this line from
BEGIN-SELECT -Db'DSN=HR83PRD;UID=PS_DEV;PWD=psdevelopment' looking for duplicates, so
NID.EMPLID
NID.NATIONAL_ID no check is made for a
move 'Y' to $found_in_PS !DAR 01/14/04 duplicate SSN/National
move &NID.EMPLID to $ps_emplid
ID
FROM PS_PERS_NID NID
!WHERE NID.NATIONAL_ID = $ps_ssn
WHERE NID.AJ_APPL_ID = $applicant_id
END-SELECT
Legacy systems business
if $found_in_PS = 'N'
do 231-Check-XXX-for-Empl
!DAR 01/14/04
!DAR 01/14/04
rules allowed employees to
if $found_on_XXX = 'N' !DAR 01/14/04 have more than one
add 1 to #last_emplid
let $last_emplid = to_char(#last_emplid) AJ_APPL_ID.
let $last_emplid = lpad($last_emplid,6,'0')
let $ps_emplid = 'AJ' || $last_emplid
end-if
end-if !DAR 01/14/04

PAGE 31 End-Procedure 230-Assign-PS-Emplid


PAGE 32


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10/4/2010

Identified & Quantified Risks

PAGE 33


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 "The go-live date may need to
New Year Conversion 120
Tax and payroll balance conversion 120
be extended due to certain
General Ledger conversion 80 critical path deliverables not
Total 320 being met. This extension will
require additional tasks and
Resource Hours
G/L Consultant 40 resources. The decision of
Project Manager 40 whether or not to extend the
Recievables Consultant 40 go-live date should be made
HRMS Technical Consultant 40
Technical Lead Consultant 40
by Monday, November 3,
HRMS Consultant 40 20XX so that resources can be
Financials Technical Consultant 40 allocated to the additional
Total 280 tasks."
Delay Weekly Resources Weeks Tasks Cumulative
January (5 weeks) 280 5 320 1720
February (4 weeks) 280 4 1120
PAGE 34
Total 2840

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10/4/2010

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.
PAGE 35


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.
PAGE 36


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10/4/2010

Published Industry Standards Guidance

Examples from the:
• IEEE (365,000 members)
– Institute of Electrical and Electronic Engineers
– 150 countries, 40 percent outside the United States
– 128 transactions, journals and magazines
– 300 conferences
• ACM (80,000+ members)
– Association of Computing Machinery
– 100 conferences annually
• ICCP (50,000+ members)
– Institute for Certification of Computing Professionals
• DAMA International (3,500+ members)
– Data Management Association
– Largest Data/Metadata conference
PAGE 37


IEEE Code of Ethics

We, the members of the IEEE, in recognition of the importance of our technologies in affecting the
quality of life throughout the world, and in accepting a personal obligation to our profession, its
members and the communities we serve, do hereby commit ourselves to the highest ethical and
professional conduct and agree:
To accept responsibility in making engineering decisions consistent with the safety, health and welfare
of the public, and to disclose promptly factors that might endanger the public or the environment;
To avoid real or perceived conflicts of interest whenever possible, and to disclose them to affected
parties when they do exist;
To be honest and realistic in stating claims or estimates based on available data;
To reject bribery in all its forms;
To improve the understanding of technology, its appropriate application, and potential consequences;
To maintain and improve our technical competence and to undertake technological tasks for others only
if qualified by training or experience, or after full disclosure of pertinent limitations;
To seek, accept, and offer honest criticism of technical work, to acknowledge and correct errors, and to
credit properly the contributions of others;
To treat fairly all persons regardless of such factors as race, religion, gender, disability, age, or national
origin;
To avoid injuring others, their property, reputation, or employment by false or malicious action;
To assist colleagues and co-workers in their professional development and to support them in following
this code of ethics. [Approved by the IEEE Board of Directors, August 1990]
PAGE 38
http://www.ieee.org/portal/site/mainsite/menuitem.818c0c39e85ef176fb2275875bac26c8/index.jsp?&p Name=corp_level1&path=about/whatis&file=code.xml&xsl=generic.xsl accessed on 4/10/04.
10/4/2010 © Copyright this and previous years by Data Blueprint - all rights reserved! 9/8/2010 © Copyright this and previous years by Data Blueprint - all rights reserved!

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10/4/2010

ACM Code of Ethics and Professional Conduct

1. General Moral Imperatives.
1.2 Avoid harm to others
• Well-intended actions, including those that accomplish assigned
duties, may lead to harm unexpectedly. In such an event the
responsible person or persons are obligated to undo or mitigate the
negative consequences as much as possible. One way to avoid
unintentional harms is to carefully consider potential impacts on all
those affected by decisions made during design and implementation.
• To minimize the possibility of indirectly harming others, computing
professionals must minimize malfunctions by following generally
accepted standards for system design and testing. Furthermore, it is
often necessary to assess the social consequences of systems to
project the likelihood of any serious harm to others. If system features
are misrepresented to users, coworkers, or supervisors, the individual
computing professional is responsible for any resulting injury.
PAGE 39
http://www.acm.org/constitution/code.html
10/4/2010 © Copyright this and previous years by Data Blueprint - all rights reserved! 9/8/2010 © Copyright this and previous years by Data Blueprint - all rights reserved!

Outcome

Sep 8, 2010

PAGE 40


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10/4/2010

http://peteraiken.net

Contact Information:

Peter Aiken, Ph.D.

Department of Information Systems
School of Business
Virginia Commonwealth University
1015 Floyd Avenue - Room 4170
Richmond, Virginia 23284-4000

Data Blueprint
Maggie L. Walker Business & Technology Center
501 East Franklin Street
Richmond, VA 23219
804.521.4056
http://datablueprint.com

office :+1.804.883.759
cell:+1.804.382.5957

e-mail:peter@datablueprint.com
PAGE 41
http://peteraiken.net

Questions?

PAGE 42

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Monetizing data management 09162010

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