monetizing data management 09162010

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.

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

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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


• 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)




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Projected impact of a 10% improvement in data quality and

sales mobility on Return on Equity




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Projected Impact of a 10% increase in intelligence and accessibility of

data on Return on Assets




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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


Business Process Component Name


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"


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?

monetizing data management 09162010

Technology

data compliance

data use data governance

data blueprintpresident

data mashups

data security

data data1

data management firm

poor data managementpractices