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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
- datablueprint.com 6/15/2012 © Copyright this and previous years by Data Blueprint - all rights reserved!
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
- datablueprint.com 6/15/2012 © Copyright this and previous years by Data Blueprint - all rights reserved!
Simon Sinek: How great leaders inspire action
4
Why
http://www.ted.com/talks/simon_sinek_how_great_leaders_inspire_action.html
- datablueprint.com 6/15/2012 © Copyright this and previous years by Data Blueprint - all rights reserved!
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
- datablueprint.com 6/15/2012 © Copyright this and previous years by Data Blueprint - all rights reserved!
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
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Capture Cost of Labor/Category
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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
- datablueprint.com 6/15/2012 © Copyright this and previous years by Data Blueprint - all rights reserved!9
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
- datablueprint.com 6/15/2012 © Copyright this and previous years by Data Blueprint - all rights reserved!
Academic Research Findings
10
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
- datablueprint.com 6/15/2012 © Copyright this and previous years by Data Blueprint - all rights reserved!11
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
- datablueprint.com 6/15/2012 © Copyright this and previous years by Data Blueprint - all rights reserved!12
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
- datablueprint.com 6/15/2012 © Copyright this and previous years by Data Blueprint - all rights reserved!13
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
- datablueprint.com 6/15/2012 © Copyright this and previous years by Data Blueprint - all rights reserved!
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
- datablueprint.com 6/15/2012 © Copyright this and previous years by Data Blueprint - all rights reserved!15
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
- datablueprint.com 6/15/2012 © Copyright this and previous years by Data Blueprint - all rights reserved!
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
- datablueprint.com 6/15/2012 © Copyright this and previous years by Data Blueprint - all rights reserved!
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
- datablueprint.com 6/15/2012 © Copyright this and previous years by Data Blueprint - all rights reserved!
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
- datablueprint.com 6/15/2012 © Copyright this and previous years by Data Blueprint - all rights reserved!
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!
- datablueprint.com 6/15/2012 © Copyright this and previous years by Data Blueprint - all rights reserved!
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
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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
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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
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Expanding DM Scope
DataBase Administration (DBA) ≈ 1950-1970
Database designDatabase operation
24
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Misunderstanding Data Management
25
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"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
- datablueprint.com 6/15/2012 © Copyright this and previous years by Data Blueprint - all rights reserved!
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
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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%.
- datablueprint.com 6/15/2012 © Copyright this and previous years by Data Blueprint - all rights reserved!
% of DM organizations labeled "successful"
29
19812007
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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
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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
- datablueprint.com 6/15/2012 © Copyright this and previous years by Data Blueprint - all rights reserved!
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
- datablueprint.com 6/15/2012 © Copyright this and previous years by Data Blueprint - all rights reserved!
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
- datablueprint.com 6/15/2012 © Copyright this and previous years by Data Blueprint - all rights reserved!
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
- datablueprint.com 6/15/2012 © Copyright this and previous years by Data Blueprint - all rights reserved!
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
- datablueprint.com 6/15/2012 © Copyright this and previous years by Data Blueprint - all rights reserved!
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
- datablueprint.com 6/15/2012 © Copyright this and previous years by Data Blueprint - all rights reserved!
British Armed Forces
38
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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
- datablueprint.com 6/15/2012 © Copyright this and previous years by Data Blueprint - all rights reserved!
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
- datablueprint.com 6/15/2012 © Copyright this and previous years by Data Blueprint - all rights reserved!
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
Monetizing Data Management
- datablueprint.com 6/15/2012 © Copyright this and previous years by Data Blueprint - all rights reserved!42
• 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
• State Agency Time & Leave Tracking– Time and leave tracking
• $1 million USD annually
• 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)
• British Telecom Project Rollout– £250 (small investment)
• Different Non-Monetized Examples– 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
- datablueprint.com 6/15/2012 © Copyright this and previous years by Data Blueprint - all rights reserved!
Monitization: Time & Leave Tracking
43
At Least 300 employees are spending 15 minutes/week
tracking leave/time
- datablueprint.com 6/15/2012 © Copyright this and previous years by Data Blueprint - all rights reserved!44
Capture Cost of Labor/Category
- datablueprint.com 6/15/2012 © Copyright this and previous years by Data Blueprint - all rights reserved!45
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
Monetizing Data Management
- datablueprint.com 6/15/2012 © Copyright this and previous years by Data Blueprint - all rights reserved!47
• 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
• State Agency Time & Leave Tracking– Time and leave tracking
• $1 million USD annually
• 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)
• British Telecom Project Rollout– £250 (small investment)
• Different Non-Monetized Examples– 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
- datablueprint.com 6/15/2012 © Copyright this and previous years by Data Blueprint - all rights reserved!
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!
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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
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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
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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
Monetizing Data Management
- datablueprint.com 6/15/2012 © Copyright this and previous years by Data Blueprint - all rights reserved!52
• 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
• State Agency Time & Leave Tracking– Time and leave tracking
• $1 million USD annually
• 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)
• British Telecom Project Rollout– £250 (small investment)
• Different Non-Monetized Examples– 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
- datablueprint.com 6/15/2012 © Copyright this and previous years by Data Blueprint - all rights reserved!
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
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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
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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
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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
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"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!
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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."
58
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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
implementationrepresentation
Componentmetadata integration
data metadata
system structure metadata
workflow metadata
post derivationmetadata
analysisand
integration
Reverse Engineering PeopleSoft
TheMAT
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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
(relates to one or more)
Business Process Component Name
(relates to one or more)
Business Process Component Step Name
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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!
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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
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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%
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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
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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!
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Indiana Jones: Raiders Of The Lost Ark
18
Monetizing Data Management
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• 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
• State Agency Time & Leave Tracking– Time and leave tracking
• $1 million USD annually
• 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)
• British Telecom Project Rollout– £250 (small investment)
• Different Non-Monetized Examples– 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
• 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
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A Major Defense Agency Data Quality Improvement Project
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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
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Vocabulary is Important-Tank, Tanks, Tankers, Tanked
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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
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• 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
Monetizing Data Management
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• 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
• State Agency Time & Leave Tracking– Time and leave tracking
• $1 million USD annually
• 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)
• British Telecom Project Rollout– £250 (small investment)
• Different Non-Monetized Examples– 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
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• 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
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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
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US AID Prototype82
Monetizing Data Management
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• 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
• State Agency Time & Leave Tracking– Time and leave tracking
• $1 million USD annually
• 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)
• British Telecom Project Rollout– £250 (small investment)
• Different Non-Monetized Examples– 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
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Seven Sisters from British Telecom
84
Thanks to Dave Evans
Monetizing Data Management
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• 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
• State Agency Time & Leave Tracking– Time and leave tracking
• $1 million USD annually
• 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)
• British Telecom Project Rollout– £250 (small investment)
• Different Non-Monetized Examples– 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
• 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
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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
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A National Cancer Institute (cont’d)
87
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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
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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
One-off reports
Reusable reports
0
25
50
75
100
Current Improved
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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
- datablueprint.com 6/15/2012 © Copyright this and previous years by Data Blueprint - all rights reserved!91
• 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
- datablueprint.com 6/15/2012 © Copyright this and previous years by Data Blueprint - all rights reserved!
The National Marrow Donor Program and the C.W. Bill Young DoD Marrow Donor Center
• 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
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The National Marrow Donor Program and the C.W. Bill Young DoD Marrow Donor Center
• 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
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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
- datablueprint.com 6/15/2012 © Copyright this and previous years by Data Blueprint - all rights reserved!
Suicide Mitigation
95
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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
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Suicide Mitigation
97
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
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
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Key Questions
98
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Potential Data Sources
99
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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
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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
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Suicide Mitigation
102
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
- datablueprint.com 6/15/2012 © Copyright this and previous years by Data Blueprint - all rights reserved!
Communication Patterns
103
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
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Service Suicide Data By Career Field
104
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
Monetizing Data Management
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• 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
• State Agency Time & Leave Tracking– Time and leave tracking
• $1 million USD annually
• 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)
• British Telecom Project Rollout– £250 (small investment)
• Different Non-Monetized Examples– 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
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
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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
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Messy Sequencing Towards Arbitration
108
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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
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Discovery
• In documents and pre-trial testimony, Company Y blamed conversion failure on "bad" data
• Expert witnesses were introduced by both X & Y
110
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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
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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"
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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.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
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Identified & Quantified Risks
115
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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 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
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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
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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 √
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Inadequate Standard of Care
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Inadequate Standard of Care - Tasks without Predecessors
120
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Inadequate Standard of Care
121
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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
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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.
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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.125
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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]
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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
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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.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!
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Outcome
129
Jun 15, 2012
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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
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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
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Data Management
132
Manage data coherently.
Share data across boundaries.
Assign responsibilities for data.Engineer data delivery systems.
Maintain data
Data Program Coordination
Organizational Data Integration
Data Stewardship
Data Development
Data Support Operations
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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.
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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
Data Program Coordination
Organizational Data Integration
Data Stewardship
Data Development
Data Support Operations
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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"
Monetizing Data Management
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• 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
• State Agency Time & Leave Tracking– Time and leave tracking
• $1 million USD annually
• 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)
• British Telecom Project Rollout– £250 (small investment)
• Different Non-Monetized Examples– 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
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Someone Notable Thinks DM is Important
137
excerpt from President Obama's 2012 State of the Union Address
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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