peter aiken - dama-ny.com · peter aiken • full time in information technology since 1981 • it...

MonetizingData

ManagementHow to get management to stop focusing on symptom

fixing, and address the underlying data management problems responsible for all

the IT wrongs in their organizations

- datablueprint.com 6/15/2012 © Copyright this and previous years by Data Blueprint - all rights reserved!

Peter [email protected] +1 804 382 5957

1


Peter Aiken• Full time in information technology since 1981• IT engineering research and project background• University teaching experience since 1979• Seven books and dozens of articles• Research Areas

– reengineering, data reverse engineering, software requirements engineering, information engineering, human-computer interaction, systems integration/systems engineering, strategic planning, and DSS/BI

• Director– George Mason University/Hypermedia Laboratory (1989-1993)

• DoD Computer Scientist– Reverse Engineering Program Manager/Office of the Chief Information Officer (1992-1997)

• Visiting Scientist– Software Engineering Institute/Carnegie Mellon University (2001-2002)

• Published Papers– Communications of the ACM, IBM Systems Journal, InformationWEEK, Information & Management, Information

Resources Management Journal, Hypermedia, Information Systems Management, Journal of Computer Information Systems and IEEE Computer & Software

• DAMA International President (http://dama.org)

– 2001 DAMA International Individual Achievement Award (with Dr. E. F. "Ted" Codd)– 2005 DAMA Community Award

• Founding Advisor/International Association for Information and Data Quality (http://iaidq.org)

• Founding Advisor/Meta-data Professionals Organization (http://metadataprofessional.org)

• Founding Director Data Blueprint 19992

• Why is it important?– Things are not improving– Concretizing essential facts– Communicating using C-level terms– Two quick examples

• British Armed Forces• Pentagon's internet connection

• International Chemical Company– Data management: Test results

• Up to $25 million dollars (not yet realized)

• State Agency Time & Leave Tracking– Time and leave tracking

• $1 million USD annually

• ERP Implementation– Customize Option

• $1 million USD on a large project – Data Conversion Measures

• Person years– Transformation of non-tabular

to tabular data• Person Centuries

• Data Warehouse Quality Analysis– Unnecessarily carrying expired

inventory• $5 billion USD US DoD (prevention)

• British Telecom Project Rollout– £250 (small investment)

• Different Non-Monetized Examples– USAid

• Counting and locating people (especially during disasters response)

– A National Cancer Institute designated Cancer Center• Improving analytics productivity

– A National Bone Marrow• Doubling the number of bone marrow

matches– Friendly Fire– Suicide Mitigation

• Legal– McGuire Woods

• $20K->$2M– ERP Implementation Legal Case

• $ 5,355,450 CAN damages/penalties

Monetizing Data Management

- datablueprint.com 6/15/2012 © Copyright this and previous years by Data Blueprint - all rights reserved!3

What

How


Simon Sinek: How great leaders inspire action

4

Why

http://www.ted.com/talks/simon_sinek_how_great_leaders_inspire_action.html


Data Remains Underutilized-Why?• Incorrect educational focus:

– Curricula generally emphasis how to build new things to hold data instead of demonstrating how data can support organizational

• Division of responsibilities: – IT and business fundamentally do not have a good agreement on

various types of responsibilities for data assets. Organizations do not have much success attempting to evolve things with methods designed to create something from nothing.

• Organizational data ROT: – 80% of organizational data is

redundant, obsolete, or trivial– Poor data quality negatively

impacts reliability– Resturant presentation example

5


Great point of initial inspiration ...

• Formalizing stuff forces clarity

• Special shout out to Chapter 7 – Measuring the value of

information– ISBN: 0470539399– http://www.amazon.com/

How-Measure-Anything-Intangibles-Business

6


Capture Cost of Labor/Category


Monetizing - from Wikipedia

8

• 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 and emeralds, 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.

Academic Research Findings0% 12.500% 25.000% 37.500% 50.000%

49.00%

39.00%

21.00%

20.00%

20.00%

20.00%

19.00%

18.00%

18.00%

17.00%

Retail

Consulting

Air Transportation

Food Products

Construction

Steel

Automobile

Publishing

Industrial Instruments

Telecommunications


A 10% improvement in data usability on

productivity (increased sales per

employee by 14.4% or $55,900)

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


Academic Research Findings

10

Projected increase in sales (in $M) due to 10% improvement in

data usability on productivity (sales per employee)



Projected impact of a 10% improvement in data quality and

sales mobility on Return on Equity




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

data on Return on Assets




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

data on Return on Assets




5% Sales Increase Versus Data Volume

14

1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016

Sales Data Volume


Motivation• Organizations:

– $600 billion annually due to poor data management practices

– Remain unaware of the root causes of their losses

• IT Professionals/Data Managers must:– Gain executive-level approval for basic

data management investments– must monetize these lost opportunities

and their related costs • To avoid an unfortunate loop:

– Management focused on fixing symptoms

– Cannot address the underlying problems


High Cost of Poor Quality Government Information

Organization Loss Amount

Australia Department of Defense $2,900,000,000

Fannie Mae $12,000,000,000Federal Agency $2,300,000,000FEMA $1,000,000,000Federal Government $2,500,000,000Government Contractors $160,000,000Haliburton $1,400,000,000Hubble Telescope $700,000,000L. A. County $1,200,000,000Medicare $358,000,000NASA-Mars Lander $125,000,000Nashville Metro Government $57,000,000Pentagon $13,000,000,000State of Tennessee $833,000U. K. Government $19,564,000,000U. S. Government $30,800,000,000IRS $640,000,000,000

Total: $728,064,833,000Excerpted from Table 1-1 Information Quality Applied by Larry English 2009 pages 4-716


IT Project Failure RatesRecent IT project failure rates statistics can be summarized as follows:

– Carr 1994• 16% of IT Projects completed on time,

within budget, with full functionality– OASIG Study (1995)

• 7 out of 10 IT projects "fail" in some respect – The Chaos Report (1995)

• 75% blew their schedules by 30% or more• 31% of projects will be canceled before they ever get completed• 53% of projects will cost over 189% of their original estimates• 16% for projects are completed on-time and on-budget

– KPMG Canada Survey (1997)• 61% of IT projects were deemed to have failed

– Conference Board Survey (2001) • Only 1 in 3 large IT project customers were very “satisfied"

– Robbins-Gioia Survey (2001)• 51% of respondents viewed their large IT implementation project as unsuccessful

– MacDonalds Innovate (2002)• Automate fast food network from fry temperature to # of burgers sold-$180M USD write-

off– Ford Everest (2004)

• Replacing internal purchasing systems-$200 million over budget– FBI (2005)

• Blew $170M USD on suspected terrorist database-"start over from scratch"

http://www.it-cortex.com/stat_failure_rate.htm (accessed 9/14/02)

New York Times 1/22/05 pA31

17


IT Project Failure Rates (moving average)

18

Source: Standish Chaos Reports as reported at: http://www.galorath.com/wp/software-project-failure-costs-billions-better-estimation-planning-can-help.php

0%

15%

30%

45%

60%

1994 1993 1998 2000 2002 2004 2009

16%

27% 26%28%

34%

29%

32%

53%

33%

46%

49%51%

53%

44%

31%

40%

28%

23%

15%

18%

24%

Failed Challenged Succeeded


Ishikawa Fishbone Analysis Diagrams

19

• Why is infant mortality so high?– Malnourished mothers

• Why are mothers malnourished?– Substandard biology educations in high

school• Why do we have substandard biology

programs?– Poor education of high school biology

teachers• Why do we have poor biology teacher

education?– We don't understand the long-term effects

Asking "why"

repeatedly!


Root Cause Analysis

20

• Symptom of the problem– The weed– Above the

surface – Obvious

• The underlying Cause– The root– Below the surface – Not obvious

• Poor Information Management Practices

Asking "why"

repeatedly!

projectcartoon.com - datablueprint.com 6/15/2012 © Copyright this and previous years by Data Blueprint - all rights reserved!

• Original business concept• As the consultant described it• As the customer explained it• How the project leader

understood it• How the programmer wrote it• What the beta testers received• What operations installed• As accredited for operation• When it was delivered• How the project was

documented• How the help desk supported it• How the customer was billed• After patches were applied• What the customer wanted21

Traditional Systems Life Cycle Challenges


Correctimplementations

Correctfunctionality

Correct designs

Correct specifications

Implementations based on

erroneous design

Implementations based on

erroneous specs

Incorrect implementations

Uncorrectableerrors

Hiddenerrors

Correctable functionality

(Adapted from [Mizuno 1983] as reproduced by Davis 1990.)

Design

Implementation

Requirements

Testing

imperfect program products

Cumulative Effect of Errors in Systems Development

Erroneous designs

Erroneous specifications

the "real" problem

Designs based on erroneous specs

22


Drop Table

23

Data Governance, Data Quality, Data Security, Analytics, Data Compliance,

Data Mashups, Business Rules (more ...)

DataManagement

(DM) ≈ 2000-

Organization-wide DM coordinationOrganization-wide data integration

Data stewardship, Data use

EnterpriseData

Administration(EDA)

≈ 1990-2000Data requirements analysis

Data modeling

Data Administration

(DA) ≈ 1970-1990


Expanding DM Scope

DataBase Administration (DBA) ≈ 1950-1970

Database designDatabase operation

24


Misunderstanding Data Management

25


"Understanding the current and future data needs of an enterprise and making that data effective and efficient in supporting business activities"Aiken, P, Allen, M. D., Parker, B., Mattia, A., "Measuring Data Management's Maturity: A Community's Self-Assessment" IEEE Computer (research feature April 2007)

Data Management

26


Data Management

Understanding the current and future data needs of an organization and making the data effective and efficient in supporting business activities.

Art

Engineering

Architecture

Business

27


CFO Necessary Prerequisites/Qualifications

• CPA• CMA• Masters of Accountancy• Other recognized degrees/certifications

• These are necessary but insufficient prerequisites/qualifications

28

0

0.09

0.18

0.27

0.36

0.45

SuccessfulPartial Success

Don't know/too soon to tellUnsuccessful

Does not exist

• In 25 years:– "Successful" DM organizations fell from 43% to 15%– "Unsuccessful" increased from 5% to 21%.


% of DM organizations labeled "successful"

29

19812007


DM Origins – Which arrives first – DM or DBMS?

• A key indicator of organizational awareness• 75% reacting instead of anticipating • Best practices are obvious

26%68%

6%

9%

75%

6%

DM 1st

DBMS 1st

Simultaneously

1981 2007

30


Data Management Involvement

31

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


Why Data Projects Fail by Joseph R. Hudicka

• Assessed 1200 migration projects!– Surveyed only

experienced migration specialists who have done at least four migration projects

• The median project costs over 10 times the amount planned!• Biggest Challenges: Bad Data; Missing Data; Duplicate Data

• The survey did not consider projects that were cancelled largely due to data migration difficulties

• "… problems are encountered rather than discovered"

$0 $125,000 $250,000 $375,000 $500,000

Median Project Expense

Median Project Cost

Joseph R. Hudicka "Why ETL and Data Migration Projects Fail" Oracle Developers Technical Users Group Journal June 2005 pp. 29-3132


Organizations Surveyed

33

• Results from more than 500 organizations

• 32% government

• Appropriate public company representation

• Enough data to demonstrate European organization DM practices are generally more mature

Local Government4%

State Government Agencies17%

Federal Government11%

Public Companies 58%

International Organizations10%

• Approximately, 10% percent of organizations achieve parity and (potential positive returns) on their DM investments.

• Only 30% of DM investments achieve tangible returns at all.• Seventy percent of organizations have very small or no tangible

return on their DM investments. - datablueprint.com 6/15/2012 © Copyright this and previous years by Data Blueprint - all rights reserved!

Largely Ineffective

EIM Investments

34

Investment <= Return10%

Investment > Return20%

Return ≈ 070%

t

t

Strategy

Goals/Objectives

Systems/Applications

Network/Infrastructure

Data/Information


t

• In support of strategy, the organization develops 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:– This ensures that data is formed

around the application and not the organizational information requirements

– Process are narrowly formed around applications

– Very little data reuse is possible

Application-Centric Development Flow

Original articulation from Doug Bagley @ Walmart

t

t

Strategy

Goals/Objectives

Data/Information

Network/Infrastructure

Systems/Applications


t

• 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/information needs and compliment organizational process flows

– Data/information reuse is maximized

Data-Centric Development Flow

Original articulation from Doug Bagley @ Walmart


Toyota versus Detroit Engine Mounting

• Detroit– 3 different

bolts– 3 different

wrenches– 3 different

bolt inventories

• Toyota– Same bolts

used for all three assemblies

– 1 bolt inventory

– 1 type of wrench

37

• British Lieutenant attempting to correct a 4 year underpayment his private's pay – Significant impact on moral– Immediate cash issues– Cost tens of man hours over

months of time to resolve– £11,000 or $17,500


British Armed Forces

38


Why Britain has 17,000 pregnant men

39

• This research, published as a letter this week in the British Medical Journal, was meant to draw attention to how much data gets entered incorrectly in the country’s medical system. These guys weren’t turning up at the doctor for pregnancy-related services. Instead, they were at their doctor for procedures that had medical codes similar to those of midwifery and obstetric services. With a misplaced keystroke here or there, an annual physical could become a consultation with a midwife.

4/6/12


Port of Seattle

• Needed trench for electricalcable 2.52" - delivered 2.5"

• $1M required to rent other facilities while new cable is obtained

• Either rounding or truncation could explain – We need to get a summary on all of this," he said. "How did the

mistake occur? Who's at fault? What are the damages? And how is money going to be recovered?"

40


WHS Accounting Success Story

41

• Legacy, accounting system shared by Pentagon operations and other DoD organizations

– Poor reliability and heavy reliance on manual processing– Internet connection was turned off in one area of the Pentagon due to

bills not being paid!• Identified and quantified areas of capable of improving data quality,

data entry and reporting– Based on this assessment, the agency created a plan of action







• International Chemical Company– Data management: Test results– Individual tests can cost up to 1/4 million

dollars• ERP Implementation

– Customize Option• $1 million USD on a large project

– Data Conversion Measures• Person years

– Transformation of non-tabular to tabular data• Person Centuries

• Data Warehouse Quality Analysis– Unnecessarily carrying expired inventory

• $5 billion USD US DoD (prevention)

• USAid– Counting and locating people

• $ millions (especially during responding to disasters)


• Different Non-Monetized Examples– A National Cancer Institute designated

Cancer Center• Improving analytics productivity







Monitization: Time & Leave Tracking

43

At Least 300 employees are spending 15 minutes/week

tracking leave/time


Capture Cost of Labor/Category


Computer Labor as OverheadRoutine Data EntryRoutine Data EntryRoutine Data Entry

District-L (as an example) Leave Tracking Time AccountingEmployees 73 50Number of documents 1000 2040Timesheet/employee 13.70 40.8Time spent 0.08 0.25Hourly Cost $6.92 $6.92Additive Rate $11.23 $11.23Semi-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

• 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 - datablueprint.com 6/15/2012 © Copyright this and previous years by Data Blueprint - all rights reserved!46

Annual Organizational Totals

























International Chemical Company Engine Testing

48

• $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 from

$7,000 - $250,000!


Overview of 1 Data Management Process

49

1. Manual transfer of digital data2. Manual file movement/duplication3. Manual data manipulation4. Disparate synonym reconciliation 5. Tribal knowledge requirements 6. Non-sustainable technology


Design Approach

Enterprise Data GovernanceNon-Integrated

Function-specific

Information

Transaction

Reference Data

SO

UR

CE

ETL

Non-Integrated Function-specific

Information merged by

SourceTransaction

Reference Data

INTE

GR

ATIO

N

ETL

Consolidated Cross-functional

Information

Transaction

Reference Data

CU

STO

MIZ

ED

VIE

WS

/M

AR

TS

DATA MOVEMENT

Analysis Ready Data for Decision Support

Analysis Data

OLAP Cubes

50


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

51

























ERP Implementation Success

• Most ERP implementations today result in cost and schedule overruns; courtesy of the Standish Group

On time, within budget, as planned 10%

Cancelled 35%

Overrun 55%

100% 100%41%

178%230%

59%

0%

50%

100%

150%

200%

250%

300%

350%

Cost Schedule PlannedFunctionality

53


ERP Risks

Challenges• 7% on-time completion• Will cost more than estimated• Will very likely deliver unsatisfying

results• Only 50% of users want and use

ERPs

Costs• Installation (averages)

– SAP $17 million– Oracle at $12.6 million– Microsoft $2.6 million– Tier II ERPs average $3.5 million

• Maintenance– 22% for Oracle– Growing at 6.9% annually– Will top $50 billion in 2012– A typical company will spend an

average of $1.2 million each year to maintain, modify and update

Presence• 85% strongly agreed ERP was

essential to their core businesses, they "could not live without them"

• 4% believed that ERPs benefited from competitive differentiation or advantage

54 Why ERP Is Still So Hard by Thomas Wailgum CIO September 09, 2009 http://www.cio.com/article/print/501656


Platform: UniSysOS: OS1998 Age: 21 Data Structure: DMS (Network)Physical Records: 4,950,000Logical Records: 250,000Relationships: 62Entities: 57Attributes: 1478

Predicting Engineering Problem Characteristics

New System

Legacy System #1: Payroll

Legacy System #2: Personnel

Platform: AmdahlOS: MVS1998 Age: 15 Data Structure: VSAM/virtual database tablesPhysical Records: 780,000Logical Records: 60,000Relationships: 64Entities: 4/350Attributes: 683

Characteristics Logical PhysicalPlatform: WinTel Records: 250,000 600,000OS: Win'95 Relationships: 1,034 1,0201998 Age: new Entities: 1,600 2,706Data Structure: Client/Sever RDBMS Attributes: 15,000 7,073

55


Actual Bid From Systems Integrator56

ID Task Name Duration Cost Work1 1000 ORGANIZATION 18.01d $128,335.9 82.44d2 1100 Organize Project 18d $42,585.3 27.36d3 1200 Complete Work Program 18d $71,739.4 46.08d4 Detailed Work Plan and Finalized Deliverable List 0d $0.00 0d5 1300 Develop Quality Plan 18.01d $14,011.2 9d6 2000 ESTABLISH DEVELOPMENT ENVIRONMENT 54d $235,364.3 228.07d7 2100 Setup Application Software 18d $51,310.6 49.86d8 2200 Site Preparation 54d $184,053.6 178.2d9 Comprehensive Backup Plan 0d $0.00 0d10 3000 PLAN CHANGE MANAGEMENT 72.01d $347,901.6 249.13d11 3100 Develop Change Management Plan 18.01d $39,821.0 21.97d12 Change Management Plan 0d $0.00 0d13 3200 Implement Change Management Plan 36d $123,597.0 91.08d14 3300 Develop Impact Analysis Plan 18.01d $17,485.4 12.96d15 Impact Analysis Plan 0d $0.00 0d16 3400 Implement Impact Analysis Plan 18d $166,998.2 123.1217 4000 PERFORM CONFIGURATION TEST 72d $93,585.2 76.14d18 4100 Prepare for Functional Configuration Testing 54d $53,091.6 36.18d19 4200 Perform Functional Configuration Testing 18d $40,493.5 39.96d20 5000 PRELIMINARY SYSTEM & PROCESS DESIGN 108d $1,248,758. 1079.8221 5100 Analyze Business Processes 54d $621,386.2 511.9222 5200 Software Fit Analysis 54d $568,447.1 505.44

S S M T W96 Jun

Task

Progress

Milestone

Summary

Rolled Up Task

Rolled Up Milestone

Rolled Up Progress

Page 1

Project: Date: Thu 9/28/00


"Extreme" Data Engineering• 2 person months = 40 person days• 2,000 attributes mapped onto 15,000• 2,000/40 person days = 50 attributes

per person dayor 50 attributes/8 hour = 6.25 attributes/hour

and• 15,000/40 person days = 375 attributes

per person dayor 375 attributes/8 hours = 46.875 attributes/hour

• Locate, identify, understand, map, transform, document, QA at a rate of -

• 52 attributes every 60 minutes or .86 attributes/minute!

57


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."

58


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

implementationrepresentation

Componentmetadata integration

data metadata

system structure metadata

workflow metadata

post derivationmetadata

analysisand

integration

Reverse Engineering PeopleSoft

TheMAT

59


Home Page

Business Process Name

Business Process Component

Business Process Component Step

PeopleSoft Process Metadata

60

Home Page Name

(relates to one or more)

Business Process Name


Business Process Component Name


Business Process Component Step Name


Example Query Outputs61

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)

Metadata Peoplesoft Does not Possess

62 - datablueprint.com 6/15/2012 © Copyright this and previous years by Data Blueprint - all rights reserved!


Resolution

63

Quantity System Component

Time to make change

Labor Hours

1,400 Panels 15 minutes 3501,500 Tables 15 minutes 375984 Business

process component steps

15 minutes 246

Total 971X $200/hour $194,200X 5 upgrades $1,000,000


Improving Data Quality during System Migration

64

• 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/A2 21.22% 6.97% N/A3 20.66% 7.49% N/A4 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%


Determining Diminishing Returns

65

Time needed to review all NSNs once over the life of the project:Time needed to review all NSNs once over the life of the project:NSNs 2,000,000Average time to review & cleanse (in minutes) 5Total Time (in minutes) 10,000,000

Time available per resource over a one year period of time:Time available per resource over a one year period of time:Work weeks in a year 48Work days in a week 5Work hours in a day 7.5Work minutes in a day 450Total Work minutes/year 108,000

Person years required to cleanse each NSN once prior to migration:Person years required to cleanse each NSN once prior to migration:Minutes needed 10,000,000Minutes available person/year 108,000Total Person-Years 92.6

Resource Cost to cleanse NSN's prior to migration:Resource Cost to cleanse NSN's prior to migration:Avg Salary for SME year (not including overhead) $60,000.00Projected Years Required to Cleanse/Total DLA Person Year Saved

93Total Cost to Cleanse/Total DLA Savings to Cleanse NSN's: $5.5 million - datablueprint.com 6/15/2012 © Copyright this and previous years by Data Blueprint - all rights reserved!

Quantitative Benefits

66


50% Data Warehouse Failure Rate

67

• 1.8 million members• 1.4 million providers• 800,000 providers no key• 2.2% prov_number = 9 digits (required)• 29% prov_ssn ≠ 9 digits• 1 User - datablueprint.com 6/15/2012 © Copyright this and previous years by Data Blueprint - all rights reserved!

Health Care ProviderData Warehouse

68

"I can take a roomful of MBAs and accomplish this analysis faster!"

The average DW costs $30M and take 18 months to build!


Indiana Jones: Raiders Of The Lost Ark

18
























• Problem/Business case:

– According to the current state of their data, units of this defense agency are carrying $1.5 billion worth of expired inventory

– This generates unnecessary costs and negative impacts on the Warfighter

– Procuring parts for maintaining obsolete items ties up monetary and human resources that could be put to better and more relevant use


A Major Defense Agency Data Quality Improvement Project

71


Costs of Maintaining Obsolete Inventory• Undefined costs:

– Mission Readiness• Resources are focused on non-value added tasks of maintaining obsolete

inventory, which creates distractions to the agency’s main mission– Storage

• Physical/real estate needed to house items– Handling

• Includes transportation and human resources dedicated to moving, maintaining, counting and securing outdated inventory

– Opportunity• Inventory could be returned to manufacturer or

sold to free up financial assets for more needed and critical supplies

– Systemic• Cost of inventorying information and maintaing

paper or electronic records which should be used to support mission-critical acquisitions and distribution

– Maintenance• Repairing of expired items

72


Vocabulary is Important-Tank, Tanks, Tankers, Tanked

73


How one inventory item proliferates data throughout the chain

555 Subassemblies & subcomponents

17,659 Repair parts or Consumables

System 1:18,214 Total items75 Attributes/ item

1,366,050 Total attributes

System 247 Total items

15+ Attributes/item720 Total attributes

System 316,594 Total items73 Attributes/item1,211,362 Total

System 48,535 Total items16 Attributes/item

136,560 Total attributes

System 515,959 Total items22 Attributes/item

351,098 Total attributes

Total for the five systems show above:59,350 Items

179 Unique attributes3,065,790 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 and

RTLS - datablueprint.com 6/15/2012 © Copyright this and previous years by Data Blueprint - all rights reserved!

Business Implications

























• Funding for USAID is determined based on the amount of their personnel

• Problem: USAID does not know how many people actually work there and what constitutes an employee (lack of a clear definition)

• Lack of quality data leads to conflicting statements(2009):– Report to the White House that they have

10,000 employees– Report to Congress that they have 11,000

employees– In an attempt to clarify these discrepancies

they took a look at their Microsoft licenses: 15,000 licenses

• Missing out on funding due to lack of quality data

80

– Funding based on the # of employees

– Don’t know how many people work there

– What constitutes an employee? No clear definition


USAID-Impact of bad data

81

• Financial impact:– Funding is based on number of

employees– Missing out on funding due to lack of

quality data• Operational impact:

– Lack of knowledge of who works for them and what qualifications employees have

– In crisis situations, USAID needs to be able to respond quickly and send the right people to the right place, e.g. Earthquake Relief in Haiti


US AID Prototype82

























Seven Sisters from British Telecom

84

Thanks to Dave Evans
























• This Virginia cancer center is a leader in shaping the fight against cancer

• Over 500 researchers and staff tend to over 12,000 patients annually

• This requires robust information management and analytical services

• The problem: It takes 1 month to run a report on an incident, i.e. a patient’s hospital visit that shows all touch points


A National Cancer Institute

86

• Data Blueprint engineered a solution that provides a 360 degree view of an incident, i.e. patient’s hospital visit

• New solution provides reports in 2 days: 360 degree view of patient’s data including diagnosis, treatment, etc.

• Integrated hospital and physician data enhances financial and asset utilization

• Results include improved quality of care, optimized workflow processes as well as operational performance


A National Cancer Institute (cont’d)

87


Other Departments

SQLSQL

Current State Assessment

7

SAS

Cancer Registry

ClaimsDatabase

File Export

Physician Invoices

Patient(Hospital)

Patient(Physician)

Patient(Registry)

Billing Data(Hospital)

Billing Data(Physician)

Diagnoses(Hospital)

Diagnoses(Physician)

Diagnoses(Registry)

Physicians(Hospital)

Physicians(Physician)

Access

SQL

SQL

SAS

SQL

Excel

Excel

Hospital Claims Text

Files FTP FTP

Text Files

FTP orEmail

WordWordWord


Other Departments

Conceptual Target Architecture

02/27/11 10

SSIS

Cancer Registry

Hospital Claims

Staging

SSIS

Physician Invoices

PatientDemographics

Billing Data(Hospital)

Billing Data(Physician)

Diagnoses(Hospital)

Diagnoses(Physician)

Diagnoses(Registry)

Physicians(Hospital)

Physicians(Physician)

SSIS

SSIS

Consolidated/Sandbox

SSIS SSA

S

Patient(Consolidated)

RPT

Physicians(Consolidated)

Diagnoses(Consolidated)

SSRS

SharePoint

Excel

Email

One-off reports

Reusable reports

0

25

50

75

100

Current Improved


Reversing The Measures

• Currently:– Analysts spend 80% of their time manipulating data and 20% of their time

analyzing data– Used to take 1 month to produce key reports

• After rearchitecting:– Analysts spend 20% of their time manipulating data and 80% of their time

analyzing data– Two days to produce key reports

90

Manipulation Analysis

The National Marrow Donor Program and the C.W. Bill Young DoD Marrow Donor Center


• Introduction/Business Case:• 1986 National Marrow Donor

Program (NMDP) is created• A marrow or blood cell transplant

is performed to help patients with leukemia, lymphoma and other life-threatening diseases live longer and healthier lives

• 6+ million Americans have been added to the registry since then

• Number of new donors increases every year: 400,000 records were added in just 6 months in 2009

• NMPD has centers through the nation; one of these centers: C.W. Bill Young DoD Marrow Donor Center in Rockville, MD

• C.W. Bill Young DoD Marrow Center is the single largest contributor to the NMDP

• It provides support for military personnel who wish to volunteer as marrow donors

• The center’s capacity is maxed out with 50,000 to 100,000 new donors being added to the registry every year

• This caseload leaves little time to facilitate or develop improved processes



• Problem Statement:• The DoD is using a combination of applications to support their business

processes• Multi-faceted system leads to:

– duplicated data– multi-step tasks– complex training – substantial paper trail records– complicated processes– integration failures

• Existing systems reach end-of-life, need to move to modern database system is identified

• Existing systems:– minimal use of customized/ad-hoc reporting– limited data constraints on critical donor information– high number of data inconsistencies and errors– lethargic reporting mechanism for results and business metrics

92



• Solution:– integrate multiple databases into one to create holistic

view of data– automation of manual process

• Results:– data is passed safely and effectively– eliminate inconsistencies, redundancies, and corruption– ability to cross-analyze– Significantly reduced turnaround time for matching

patients with potential donor -> increased potential to make life-saving connection in a manner that is faster, safer and more reliable

– Increased safe matches from 3 out of 10 to 6 out of 1093


Friendly Fire deaths traced to

Dead Battery

94

Date: Tue, 26 Mar 2002 10:47:52 -0500From: 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


Suicide Mitigation

95


Suicide Mitigation

96

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

2

Suicide Data Sources

HQDAG-1

AOC(EXSUM)

Level

SIR

MTF/AFIP*Suicide Risk Management & Surveillance Office

**Defense Medical Surveillance System

HQDAG-1

DHR

CMAOC(Online)

CasualtyArea CMD

CasualtyReport

HQDA G-1

Individual(ASER)

ASER Annual Report

ArmySuicideEventReport

SRMSO *Commander>AR 15-6>Line of Duty

HQDAG-1

CID(EXSUM)

CID

CID Report

HQCID

Initial Report

** ****

** Report eventually gets back to individual commanders but is neither analyzed nor shared across the Army

Other Data Sources

DMSS**•Medical encounters

•Medications

•PDHA/PDHRA

Army Central Registry•Domestic Violence

DMDC•Denominator Data

•Deployment info

Judicial/Admin•UCMJ

•Chapter

Adapted from G-1, Human Resources Policy Directorate

SurveillanceReportsTrends/RatesAnalysis Consultation ExperienceActionable Intelligence

CHPPM Suicide Registry (9 of 13)

ComprehensiveDatabase (Army)

At CHPPM

Suspected Army Suicide or Attempt

CCIR/SIRName/SSN

* Only suspected and confirmed case information will be maintained in registry. Periodic studies will require control and cohort data to be collected and analyzed.

AFHSC Auto-populate Data:•Personnel•Medical•Deployment information•MEPS data•TMDS? (Theater Medical Data)•TRAC2ES

Narrative data Sources Input:•CID•AR 15-6•MTF Behavioral Health•LOD

Confirmed Suicide or Attempt

Other Data Sources:•Central Registry•Substance Abuse•UCMJ/Chapter/Waivers•Financial•Medications•Sexual Assault Data

Defense Centers of Excellence (DCOE):DoD Suicide Event Report (DoDSER) format is foundation

for Army data collection

Army G-1

24


Suicide Mitigation

97


What did the soldier look like at entry into the Army?

What happened to the soldier in the Army?

• Mental health• Physical health• Medications• Counseling received• Education• Waivers• Relationships• Criminal history• Abuse (Either as perpetrator or

victim)

• Mental health• Physical health• Relationships• Criminal history (inside and

outside of the Army)• Deployments/Combat experience• Promotions/demotions/Job

problems/Type of position• Employment• Financial/Legal issues• Where stations• Domestic Violence occurrences/

treatment• Alcohol/Substance abuses

occurrences


Key Questions

98


Potential Data Sources

99


Suicide Mitigation

100

Data Mapping

12

Mental illness

Deployments

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


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

101


Suicide Mitigation

102



Communication Patterns

103



Service Suicide Data By Career Field

104

























29APRIL 2010

PERSPECTIVES

Published by the IEEE Computer Society0018-9162/10/$26.00 © 2010 IEEE

In the absence of other published standards of care, it is reasonable for contractual parties to rely on an applicable, widely available code of conduct to guide expectations.

When legal disputes arise, the primary focus of judges, juries, and arbitra-tion panels is on interpreting facts. In cases of alleged underperformance, they must evaluate facts against con-

tract language, which typically states that services will be provided in accordance with industry standards. Legal arbiters seek well-articulated “standards of care” against which to evaluate the behavior of contractual parties and, in the absence of other published standards, increasingly rely on codes of conduct (CoCs) to establish an objective context. In fact, they have successfully applied CoCs—including the ACM/IEEE-CS CoC—in instances where the parties were not even af!liated with the CoC-sponsoring organization.

We illustrate the current application of CoCs with a !ctional enterprise resource planning (ERP) system imple-mentation failure that is a compilation of real-life cases. Subject to binding panel arbitration, the plaintiff and defen-dant in the case presented con"icting interpretations of the same facts: From the plaintiff’s perspective, the defendant

Peter Aiken, Virginia Commonwealth University

Robert M. Stanley and Juanita Billings, Data Blueprint

Luke Anderson, Duane Morris LLC

failed to migrate the ERP system as promised; the defen-dant countered that defective and poor-quality data delayed the migration. Using the ACM/IEEE-CS CoC as a reference, expert testimony convinced the arbitration panel that the defendant’s position was untenable, and the panel accord-ingly awarded the plaintiff a multimillion-dollar judgment.

CASE STUDYAcme Co. received a directive from its parent cor-

poration mandating replacement of its legacy pay and personnel systems with a speci!c ERP software package designed to standardize payroll and personnel processing enterprise-wide. Upon the vendor’s “referred specialist” recommendation, Acme Co. contracted with ERP Systems Integrators to implement the new system and convert its legacy data for $1 million.

The contracted timeline was six months, beginning in July and wrapping up with a “big bang” conversion at the end of December. The year-end conversion failed, alleg-edly due to ERP Systems Integrators’ poor data migration practices, and Acme Co. had to run the old and new sys-

Using Codes of Conduct to Resolve Legal Disputes



Virginia Law Firm

107

Plaintiff(Service Company)

Defendant (Hospital)

Sues for $2 million, claiming maintenance services dating back 4 years

Requests detailed reports to support these charges

Claims it cannot provide reports due to outdated data storage system

Engages Data Blueprint, we prove that it is possible to pull reports and verify charges

Once presented with the proof, they agree that it is doable but decide it is too much of an effort. Decision to settle the case.

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

108


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?

109


Discovery

• In documents and pre-trial testimony, Company Y blamed conversion failure on "bad" data

• Expert witnesses were introduced by both X & Y

110


Expert ReportsOurs provided evidence that :1. Company Y's conversion code introduced

errors into the data2. 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

111

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


FBI & Canadian Social Security Gender Codes1. Male2. Female3. Formerly male now female4. Formerly female now male5. Uncertain6. Won't tell7. Doesn't know8. Male soon to be female9. Female soon to be male

112

If column 1 in source = "m"

• then set value of target data to "male"

• else set value of target data to "female"


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.EMPLIDNID.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_ssnWHERE NID.AJ_APPL_ID = $applicant_idEND-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.

113



Identified & Quantified Risks

115


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 HoursNew Year Conversion 120Tax and payroll balance conversion 120General Ledger conversion 80

Total 320

Resource HoursG/L Consultant 40Project Manager 40Recievables Consultant 40HRMS Technical Consultant 40Technical Lead Consultant 40HRMS Consultant 40Financials Technical Consultant 40

Total 280

Delay Weekly Resources Weeks Tasks CumulativeJanuary (5 weeks) 280 5 320 1720February (4 weeks) 280 4 1120

Total 2840116


PMBOK Project Planning Processes

Scope Planning

Resource Planning

Activity Definition

Activity Duration

Estimating

Activity Sequencing

Scope Definition

Cost Estimating

Schedule Development

Cost Budgeting

Project Plan Development

Risk Response

DevelopmentRisk

Identification

Staff Acquisition

Communications Planning

Organizational Planning

Quality Planning

Risk Quantification

Solicitation Planning

Procurement Management

Core Processes

Facilitating Processes

Page 31, Duncan, W., A guide to the Project Management Body of Knowledge, PMI, 1996117


Project Management Planning

118

Process Planning Area Company YCompany Y Company X LeadMethodology 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 √


Inadequate Standard of Care


Inadequate Standard of Care - Tasks without Predecessors

120


Inadequate Standard of Care

121


As Is InformationRequirementsAssets

As Is Data Design Assets As Is Data Implementation Assets

Exi

stin

gN

ew

Data Engineering

O2 RecreateData Design

Reverse Engineering

Forward engineering

O5 Reconstitute Requirements

O9 Reimplement

Data

To Be Data Implementation Assets

O8 RedesignData

O4Recon-stitute

Data Design

O3 RecreateRequirements

O6 Redesign Data

To BeDesign Assets

O7 Re-developRequire-ments

To Be Requirements Assets

O-1/3 reconstitute original metadataO-4/5 improve the current metadataO-6/9 improve system data capabilities based on the improved metadata

O1 Recreate Data Implementation

Metadata

122



Professional & Workmanlike Manner

124

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.125


Published Industry Standards Guidance• 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

(5,000 + members)– Data Management Association– Largest Data/Metadata conference

126

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:

1. 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;

2. To avoid real or perceived conflicts of interest whenever possible, and to disclose them to affected parties when they do exist;

3. To be honest and realistic in stating claims or estimates based on available data; 4. To reject bribery in all its forms; 5. To improve the understanding of technology, its appropriate application, and potential

consequences; 6. 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; 7. To seek, accept, and offer honest criticism of technical work, to acknowledge and correct

errors, and to credit properly the contributions of others; 8. To treat fairly all persons regardless of such factors as race, religion, gender, disability, age, or

national origin; 9. To avoid injuring others, their property, reputation, or employment by false or malicious action; 10. 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

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 4/10/04.127


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.html128

ACM Code of Ethics and Professional Conduct

• Three days after the hearing, the panel issued a one-page decision awarding damages of $5 million to Company X

Defendant Plaintiff $5,000,000.00 Five million Dollars and 00/100 ************************************************** **************** dollars

one big mistake!


Outcome

129

Jun 15, 2012


Poorly Evaluating Risk in Decision Making1. Fixed price contact to

convert unknown data of unknown quality and complexity

2. Did not practice minimal project management oversight

3. Failed to provide the customer with objective performance standards

4. Failed to heed internal warnings of project failure

130

Data Program Coordination

DataDevelopment


StandardData

Data Management

OrganizationalData Integration

DataStewardship

Data SupportOperations

Data Asset Use

Organizational Strategies

Goals

IntegratedModels

BusinessData

Business Value

Application Models & Designs

Feedback

Implementation

Direction

Guidance

131


Data Management

132

Manage data coherently.

Share data across boundaries.

Assign responsibilities for data.Engineer data delivery systems.

Maintain data


Organizational Data Integration

Data Stewardship

Data Development

Data Support Operations


Our DM practices are ad hoc and dependent upon "heroes" and heroic efforts

Initial(1)

Repeatable(2) We have DM experience and

have the ability to implement disciplined processes

Data Management Capability Maturity Model Levels

Defined(3)

We have experience that we have standardized so that all in the organization

can follow it

Managed(4)

We manage our DM processes so that the whole organization can

follow our standard DM guidance

Optimizing(5)

We have a process for improving our

DM capabilities

One concept for process improvement, others include:

• Norton Stage Theory• TQM• TQdM• TDQM• ISO 9000

and focus on understanding current processes and determining where to make improvements.


Assessment Components

Data Management Practice AreasData Management Practice AreasData program coordination

DM is practiced as a coherent and coordinated set of activities

Organizational data integration

Delivery of data is support of organizational objectives – the currency of DM

Data stewardship Designating specific individuals caretakers for certain data

Data development

Efficient delivery of data via appropriate channels

Data support Ensuring reliable access to data

4

Capability Maturity Model Levels

Examples of practice maturity

1 – Initial Our DM practices are ad hoc and dependent upon "heroes" and heroic efforts

2 - Repeatable We have DM experience and have the ability to implement disciplined processes

3 - Documented We have standardized DM practices so that all in the organization can perform it with uniform quality

4 - Managed We manage our DM processes so that the whole organization can follow our standard DM guidance

5 - Optimizing We have a process for improving our DM capabilities


Organizational Data Integration

Data Stewardship

Data Development

Data Support Operations


Data Management Practices Measurement (DMPA)

135

Focus: Implementation

and Access

Focus: Guidance and

Facilitation

Optimizing (V)

Managed (IV)

Documented (III)

Repeatable (II)

Initial (I)

• CMU's Software Engineering Institute (SEI) Collaboration• Results from hundreds organizations in

various industries including:– Public Companies – State Government Agencies– Federal Government– International Organizations• Defined industry standard• Steps toward defining data

management "state of the practice"

























Someone Notable Thinks DM is Important

137

excerpt from President Obama's 2012 State of the Union Address


http://peteraiken.net

Contact Information:

Peter Aiken, Ph.D.

Department of Information Systems School of BusinessVirginia Commonwealth UniversitySnead Hall Room B4217301 West Main StreetRichmond, Virginia 23284-4000

Data Blueprint 10124C West Broad StreetGlen Allen VA 23060804.521.4056http://datablueprint.com

office: +1.804.883.7594cell: +1.804.382.5957

e-mail: [email protected]: http://peteraiken.net

138

h"p://tw

i"er.com

/paiken

peter aiken - dama-ny.com · peter aiken • full time in information technology since 1981 • it...

Documents