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10/4/2010
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Monetizing Data Management
Dr. Peter AikenCEO and Founding Director, Data BlueprintPresident, DAMA InternationalAssociate Professor of Information Systems, Virginia Commonwealth University
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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.
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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.
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Monetizing - from Wikipedia
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• Monetization is the process of converting or establishing something into legal tender.
• It usually refers to the printing of banknotesby 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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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!
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Data Governance, Data Quality, Data Security, Analytics, Data Compliance,
Data Mashups, Business Rules (more ...)
Data
Management
(DM)
≈ 2000-
Organization-wide DM coordinationOrganization-wide data integration
Data stewardship, Data use
Enterprise
Data
Administration
(EDA)
≈ 1990-2000
Data requirements analysisData modeling
Data
Administration
(DA)
≈ 1970-
1990
Expanding DM Scope
DataBase Administration (DBA) ≈ 1950-1970
Database design Database operation
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Value Title
Data Warehousing
XML
Data Quality
Customer Relationship
Management
Master Data Management
Customer Data Integration
Enterprise Resource Planning
Enterprise Application Integration
Initiative Leader Initiative Involvement Not Involved
Data Management Involvement
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NiccoloNiccolo Machiavelli Machiavelli ((14691469--1527)1527)
Machiavelli, Niccolo. The Prince. 19 Mar. 2004 http://pd.sparknotes.com/philosophy/prince
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.
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Look Familiar?
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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.
Data Data
Data
Information
Fact Meaning
Request
A Model Specifying Relationships Among Important Terms
[Built on definition by Dan Appleton 1983]
Intelligence
Use
Wisdom & knowledge are often used synonymously
Data
Data
Data Data
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Date: Tue, 26 Mar 2002 10:47:52 -0500From: Jamie McCarthy <[email protected]>Subject: Friendly Fire deaths traced to dead battery
In one of the more horrifying incidents I've read about, U.S. soldiers andallies were killed in December 2001 because of a stunningly poor design of aGPS 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 fromsome sort of battery-powered device. He "had used the GPS receiver tocalculate the latitude and longitude of the Taliban position in minutes andseconds for an airstrike by a Navy F/A-18."
According to the *Post* story, the bomber crew "required" a "secondcalculation in 'degree decimals'" -- why the crew did not have equipment toperform the minutes-seconds conversion themselves is not explained.
The air controller had recorded the correct value in the GPS receiver whenthe battery died. Upon replacing the battery, he called in thedegree-decimal position the unit was showing -- without realizing that theunit 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 Forcessoldiers and injuring 20 others.
If the information in this story is accurate, the RISKS involve replacingmemory settings with an apparently-valid default value instead of blinking 0or some other obviously-wrong display; not having a backup battery to holdvalues in memory during battery replacement; not equipping users totranslate one coordinate system to another (reminiscent of the Mars ClimateOrbiter slamming into the planet when ground crews confused English withmetric); and using a device with such flaws in a combat situation
Friendly Fire deaths traced to Dead Battery
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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
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Projected increase in sales (in $M) due to 10% improvement in
data usability on productivity (sales per employee)
Measuring the Business Impacts of Effective Data by Anitesh Barua, Deepa Mani, Rajiv Mukherjee
Academic Research Findings
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Projected impact of a 10% improvement in data quality and
sales mobility on Return on Equity
Measuring the Business Impacts of Effective Data by Anitesh Barua, Deepa Mani, Rajiv Mukherjee
Academic Research Findings
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Projected Impact of a 10% increase in intelligence and accessibility of
data on Return on Assets
Measuring the Business Impacts of Effective Data by Anitesh Barua, Deepa Mani, Rajiv Mukherjee
Academic Research Findings
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Monetization: Time & Leave Tracking
At Least 300 employees are spending 15 minutes/week
tracking leave/time
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Capture Cost of Labor/Category
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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
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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!
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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."
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InstalledPeopleSoftSystem
• Queries to
PeopleSoft
Internals
• PeopleSoft
external
RDBM
Tables
• Printed
PeopleSoft
Datamodel
Metadata Uses
• System Structure
Metadata -
requirements
verification and
system change
analysis
• Data Metadata - data
conversion, data
security,and user
training
• Workflow Metadata -
business practice
analysis and
realignment
implementation
representation
Component
metadata integration
data metadata
system structure metadata
workflow metadata
post
derivation
metadata
analysis
and
integration
Reverse Engineering PeopleSoft
TheMAT
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Home Page Name
(relates to one or more)
Business Process Name
(relates to one or more)
Business Process Component Name
(relates to one or more)
Business Process Component Step Name
Home Page
Business Process Name
Business Process Component
Business Process Component Step
PeopleSoft Process Metadata
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- datablueprint.com 9/8/2010 © Copyright this and previous years by Data Blueprint - all rights reserved!
Example Query OutputsExample Query Outputs10/4/2010 © Copyright this and previous years by Data Blueprint - all rights reserved!
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processes(39)
homepages(7)
menugroups(8)
components(180)
stepnames(822)
menunames(86)
panels(1421)
menuitems(1149)
menubars(31)
fields(7073)
records(2706)
parents(264)
reports(347)
children(647)
(41) (8)
(182)
(847)
(949)
(86)
(281)
(1259)(1916)
(5873)(264)
(647)(708)
(647)
(25906)
(347)
Data Metadata Structure
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Resolution
Quantity System Component
Time to make change
Labor Hours
1,400 Panels 15 minutes 350
1,500 Tables 15 minutes 375
984 Business process component steps
15 minutes 246
Total 971
X $200/hour $194,200
X 5 upgrades $1,000,000
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An Iterative Approach to MDM Structuring
Unmatched Items
Unmatched Items
Ignorable Ignorable Items
AvgExtracted
Items Matched
Rev#
(% Total) NSNs (% Total) Items Matched
Per Item (% Total) Items 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
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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
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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
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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
then set value
value of target
If column 1 in
source = "m"
•then set value of target data
to "male"
•else set
value of target
data to
"female"
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Hypothesized extensions contributed by a Chicago DAMA Member10.Psychologically female, biologically male11.Psychologically male, biologically female 12.Both soon to be female13.Both soon to be male
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220220--Process_Emp_DataProcess_Emp_DataMore Examples More Examples -- StateState
! if $state = ' ' or $state = ''
! move 'State' to $blank_field
! move 'Y' to $blank_state
! do 221-Blank-Field-Error
! end-if
if $state = ''
move ' ' to $state
end-if
An exclamation point indicates that anything to the right will not be executed (“commented out”)
If there is no state, then this code makes the state
a space
To protect data quality the program should use the221-Blank-Field-ErrorProcedure
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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.SQRAJHR0213_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.
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Identified & Quantified Risks
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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 284010/4/2010 © Copyright this and previous years by Data Blueprint - all rights reserved!
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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.
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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.
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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
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9/8/2010 © Copyright this and previous years by Data Blueprint - all rights reserved!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.
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]
IEEE Code of Ethics
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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.
http://www.acm.org/constitution/code.html
ACM Code of Ethics and Professional Conduct
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Outcome
Sep 8, 2010
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http://peteraiken.net
Contact Information:
Peter Aiken, Ph.D.
Department of Information SystemsSchool of BusinessVirginia Commonwealth University1015 Floyd Avenue - Room 4170Richmond, Virginia 23284-4000
Data Blueprint Maggie L. Walker Business & Technology Center501 East Franklin StreetRichmond, VA 23219804.521.4056http://datablueprint.com
office :+1.804.883.759cell:+1.804.382.5957
e-mail:[email protected]://peteraiken.net
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Questions?