Copyright 2013 by Data Blueprint
Show Me The Money: Monetizing Data Management
Failure to successfully monetize data manag...
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Executive Editor at DATAVERSITY.net
2
Shannon Kempe
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Commonly Asked Questions
1) Will I get copies of the
slides after the event?
1) Is this b...
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Peter Aiken, PhD
• 25+ years of experience in data
management
• Multiple international ...
Show Me The Money
Monetizing Data Management
Presented by Peter Aiken, Ph.D.
10124 W. Broad Street, Suite C
Glen Allen, Vi...
Copyright 2013 by Data Blueprint
1. Data Management Overview
2. Book Motivations & Survey Results
3. Leveraging Data
4. Mo...
Copyright 2013 by Data Blueprint
1. Data Management Overview
2. Book Motivations & Survey Results
3. Leveraging Data
4. Mo...
Data Program
Coordination
Feedback
Data
Development
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Standard
Data
Five Integrated DM Prac...
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Five Integrated DM Practice Areas
10
Manage data coherently.
Share data across boundaries...
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Hierarchy of Data Management Practices (after Maslow)
• 5 Data
management
practices areas...
Copyright 2013 by Data Blueprint
1. Data Management Overview
2. Book Motivations & Survey Results
3. Leveraging Data
4. Mo...
Copyright 2013 by Data Blueprint
1. Data Management Overview
2. Book Motivations & Survey Results
3. Leveraging Data
4. Mo...
Copyright 2013 by Data Blueprint
Motivation ...
• Task: helping our community better articulate the
importance of what we ...
Copyright 2013 by Data Blueprint
2013 Monetizing Data Management Survey Results
15
Copyright 2013 by Data Blueprint
• Soon to be released: white paper & survey results
16
2013 Monetizing Data Management Su...
Copyright 2013 by Data Blueprint
1. Data Management Overview
2. Book Motivations & Survey Results
3. Leveraging Data
4. Mo...
Copyright 2013 by Data Blueprint
1. Data Management Overview
2. Book Motivations & Survey Results
3. Leveraging Data
4. Mo...
Copyright 2013 by Data Blueprint
Data
Data
Data
Information
Fact Meaning
Request
Strategic Information Use: Prerequisites
...
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Leverage is an Engineering Concept
20
• Using proper engineering
techniques, a human can ...
Copyright 2013 by Data Blueprint
Data Leverage is an Engineering Concept
21
Organizational
Data
Organizational
Data Manage...
Copyright 2013 by Data Blueprint
Why Is Data Management Important?
• Too much data leads directly to wasted productivity
–...
Copyright 2013 by Data Blueprint
Incorrect Educational Focus
• Building new systems
– 80% of IT costs are spent rebuilding...
Copyright 2013 by Data Blueprint
Evolving Data is Different than Creating New Systems
24
Common Organizational Data
(and c...
Copyright 2013 by Data Blueprint
Application-Centric Development
Original articulation from Doug Bagley @ Walmart
25
Data/...
Copyright 2013 by Data Blueprint
Payroll Application
(3rd GL)Payroll Data
(database)
R& D Applications
(researcher support...
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Data-Centric Development
Original articulation from Doug Bagley @ Walmart
27
Systems/
App...
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Polling Question #1
• Who or what
department(s) makes the
decision on investing in
data m...
Copyright 2013 by Data Blueprint
1. Data Management Overview
2. Book Motivations & Survey Results
3. Leveraging Data
4. Mo...
Copyright 2013 by Data Blueprint
1. Data Management Overview
2. Book Motivations & Survey Results
3. Leveraging Data
4. Mo...
Copyright 2013 by Data Blueprint
Monitization: Time & Leave Tracking
31
At Least 300 employees are
spending 15 minutes/wee...
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32
Capture Cost of Labor/Category
District-L (as an example) Leave Tracking Time Accounting
Employees 73 50
Number of documents 1000 2040
Timesheet/employee...
• Range $192,000 - $159,000/month
• $100,000 Salem
• $159,000 Lynchburg
• $100,000 Richmond
• $100,000 Suffolk
• $150,000 ...
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International Chemical Company Engine Testing
35
• $1billion (+) chemical
company
• Devel...
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Overview of Existing Data Management Process
36
1. Manual transfer of digital data
2. Man...
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Data Integration Solution
• Integrated the existing systems to
easily search on and find ...
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Vocabulary is Important-Tank, Tanks, Tankers, Tanked
38
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How one inventory item proliferates data throughout the chain
39
555	
  Subassemblies	
  ...
• National Stock Number (NSN)
Discrepancies
– If NSNs in LUAF, GABF, and RTLS are
not present in the MHIF, these records
c...
Copyright 2013 by Data Blueprint
Improving Data Quality during System Migration
41
• Challenge
– Millions of NSN/SKUs
main...
Unmatched
Items
Ignorable
Items
Items
Matched
Week # (% Total) (% Total) (% Total)
1 31.47% 1.34% N/A
2 21.22% 6.97% N/A
3...
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 ...
Copyright 2013 by Data Blueprint
Seven Sisters (from British Telecom)
44
Thanks to Dave Evans
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Polling Question #2
• Is it hard to obtain
funding for your data
management projects?
A) ...
Copyright 2013 by Data Blueprint
1. Data Management Overview
2. Book Motivations & Survey Results
3. Leveraging Data
4. Mo...
Copyright 2013 by Data Blueprint
1. Data Management Overview
2. Book Motivations & Survey Results
3. Leveraging Data
4. Mo...
In one of the more horrifying incidents I've read about, U.S. soldiers and allies
were killed in December 2001 because of ...
Suicide Mitigation
Copyright 2013 by Data Blueprint
49
Suicide MitigationData Mapping
12
Mental
illness
Deploy
ments
Work
History
Soldier Legal
Issues
Abuse
Suicide
Analysis
FAP...
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Senior Army Official
• A very heavy dose of
management support
• Any questions as to futu...
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Communication Patterns
52
Source: The Challenge and the Promise: Strengthening the Force,...
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Polling Question #3
• What percentage of
your data projects are
successful?
A) All
B) 25%...
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1. Data Management Overview
2. Book Motivations & Survey Results
3. Leveraging Data
4. Mo...
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1. Data Management Overview
2. Book Motivations & Survey Results
3. Leveraging Data
4. Mo...
Plaintiff
(Company X)
Defendant
(Company Y)
April
Requests a
recommendation from
ERP Vendor
Responds indicating
"Preferred...
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Points of Contention
• Who owned the
risks?
• Who was the project
manager?
• Was the data...
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Expert Reports
Ours provided evidence that :
1. Company Y's conversion code introduced
er...
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FBI & Canadian Social Security Gender Codes
1. Male
2. Female
3. Formerly male now female...
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The defendant knew to
prevent duplicate SSNs
!*******************************************...
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Identified & Quantified Risks
62
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Risk Response
“Risk response development involves defining enhancement steps
for opportun...
Process Planning Area Company YCompany Y Company X Lead
Methodology Demonstrated
Scope Planning √ √
Scope Definition √ √
A...
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Inadequate Standard of Care - Tasks without Predecessors
65
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Inadequate Standard of Care
66
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Professional & Workmanlike Manner
67
Defendant warrants that the services
it provides her...
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The Defense's "Industry Standards"
• Question:
– What are the industry standards that you...
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1. Data Management Overview
2. Book Motivations & Survey Results
3. Leveraging Data
4. Mo...
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1. Data Management Overview
2. Book Motivations & Survey Results
3. Leveraging Data
4. Mo...
Monetizing Data Management
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• State Agency Time & Leave Tracking
– Time and leave track...
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Data-Ed Online: Show Me the Money - Monetizing Data Management

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Failure to successfully monetize data management investments sets up an unfortunate loop of fixing symptoms without addressing the underlying problems. As organizations begin to understand poor data management practices as the root causes of many of their business problems, they become more willing to make the required investments in our profession. This presentation uses specific examples to illustrate the costs of poor data management and how it impacts business objectives. Join us and learn how you can better align your data management projects with business objectives to justify funding and gain management approval.

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Data-Ed Online: Show Me the Money - Monetizing Data Management

  1. 1. Copyright 2013 by Data Blueprint Show Me The Money: Monetizing Data Management Failure to successfully monetize data management investments sets up an unfortunate loop of fixing symptoms without addressing the underlying problems. As organizations begin to understand poor data management practices as the root causes of many of their business problems, they become more willing to make the required investments in our profession. This presentation uses specific examples to illustrate the costs of poor data management and how it impacts business objectives. Join us and learn how you can better align your data management projects with business objectives to justify funding and gain management approval. Date: October 8, 2013 Time: 2:00 PM ET/11:00 AM PT Presenter: Peter Aiken, Ph.D. 1 PETER AIKEN WITH JUANITA BILLINGS FOREWORD BY JOHN BOTTEGA MONETIZING DATA MANAGEMENT Unlocking the Value in Your Organization’s Most Important Asset. PETER AIKEN WITH JUANITA BILLINGS FOREWORD BY JOHN BOTTEGA MONETIZING DATA MANAGEMENT Unlocking the Value in Your Organization’s Most Important Asset.
  2. 2. Copyright 2013 by Data Blueprint Executive Editor at DATAVERSITY.net 2 Shannon Kempe
  3. 3. Copyright 2013 by Data Blueprint Commonly Asked Questions 1) Will I get copies of the slides after the event? 1) Is this being recorded so I can view it afterwards? 3
  4. 4. Copyright 2013 by Data Blueprint Get Social With Us! Live Twitter Feed Join the conversation! Follow us: @datablueprint @paiken Ask questions and submit your comments: #dataed 4 Like Us on Facebook www.facebook.com/ datablueprint Post questions and comments Find industry news, insightful content and event updates. Join the Group Data Management & Business Intelligence Ask questions, gain insights and collaborate with fellow data management professionals
  5. 5. Copyright 2013 by Data Blueprint 5 Peter Aiken, PhD • 25+ years of experience in data management • Multiple international awards & recognition • Founder, Data Blueprint (datablueprint.com) • Associate Professor of IS, VCU (vcu.edu) • President, DAMA International (dama.org) • 8 books and dozens of articles • Experienced w/ 500+ data management practices in 20 countries • Multi-year immersions with organizations as diverse as the US DoD, Nokia, Deutsche Bank, Wells Fargo, and the Commonwealth of Virginia 2
  6. 6. Show Me The Money Monetizing Data Management Presented by Peter Aiken, Ph.D. 10124 W. Broad Street, Suite C Glen Allen, Virginia 23060 804.521.4056 PETER AIKEN WITH JUANITA BILLINGS FOREWORD BY JOHN BOTTEGA MONETIZING DATA MANAGEMENT Unlocking the Value in Your Organization’s Most Important Asset.
  7. 7. Copyright 2013 by Data Blueprint 1. Data Management Overview 2. Book Motivations & Survey Results 3. Leveraging Data 4. Monetary ROI (6 cases) 5. Non-Monetary ROI (2 cases) 6. Legal Considerations 7. Take Aways and Q&A Outline 7 Tweeting now: #dataed
  8. 8. Copyright 2013 by Data Blueprint 1. Data Management Overview 2. Book Motivations & Survey Results 3. Leveraging Data 4. Monetary ROI (6 cases) 5. Non-Monetary ROI (2 cases) 6. Legal Considerations 7. Take Aways and Q&A Outline 8
  9. 9. Data Program Coordination Feedback Data Development Copyright 2013 by Data Blueprint Standard Data Five Integrated DM Practice Areas Organizational Strategies Goals Business Data Business Value Application Models & Designs Implementation Direction Guidance 9 Organizational Data Integration Data Stewardship Data Support Operations Data Asset Use Integrated Models Leverage data in organizational activities Data management processes and infrastructure Combining multiple assets to produce extra value Organizational-entity subject area data integration Provide reliable data access Achieve sharing of data within a business area
  10. 10. Copyright 2013 by Data Blueprint Five Integrated DM Practice Areas 10 Manage data coherently. Share data across boundaries. Assign responsibilities for data. Engineer data delivery systems. Maintain data availability. Data Program Coordination Organizational Data Integration Data Stewardship Data Development Data Support Operations
  11. 11. Copyright 2013 by Data Blueprint Hierarchy of Data Management Practices (after Maslow) • 5 Data management practices areas / data management basics ... • ... are necessary but insufficient prerequisites to organizational data leveraging applications that is self actualizing data or advanced data practices Basic Data Management Practices – Data Program Management – Organizational Data Integration – Data Stewardship – Data Development – Data Support Operations http://3.bp.blogspot.com/-ptl-9mAieuQ/T-idBt1YFmI/AAAAAAAABgw/Ib-nVkMmMEQ/s1600/maslows_hierarchy_of_needs.png Advanced Data Practices • MDM • Mining • Big Data • Analytics • Warehousing • SOA
  12. 12. Copyright 2013 by Data Blueprint 1. Data Management Overview 2. Book Motivations & Survey Results 3. Leveraging Data 4. Monetary ROI (6 cases) 5. Non-Monetary ROI (2 cases) 6. Legal Considerations 7. Take Aways and Q&A Outline 12
  13. 13. Copyright 2013 by Data Blueprint 1. Data Management Overview 2. Book Motivations & Survey Results 3. Leveraging Data 4. Monetary ROI (6 cases) 5. Non-Monetary ROI (2 cases) 6. Legal Considerations 7. Take Aways and Q&A Outline 13
  14. 14. Copyright 2013 by Data Blueprint Motivation ... • Task: helping our community better articulate the importance of what we do • Until we can meaningfully communicate in monetary or other terms equally important to the C-suite, we will continue to struggle to articulate the value of its role • Today’s business executives – Smart, talented and experienced experts – Executive decision-makers being far removed and insufficiently data knowledgeable – Too many decisions about data have been poor. • Four Parts – Unique perspective to the practice of leveraging data – 11 cases where leveraging data has produced positive financial results – Five instance non-monetary outcomes of critical important to the C-suite – Interaction of data management practices and both IT projects and legal responsibilities 14 PETER AIKEN WITH JUANITA BILLINGS FOREWORD BY JOHN BOTTEGA MONETIZING DATA MANAGEMENT Unlocking the Value in Your Organization’s Most Important Asset.
  15. 15. Copyright 2013 by Data Blueprint 2013 Monetizing Data Management Survey Results 15
  16. 16. Copyright 2013 by Data Blueprint • Soon to be released: white paper & survey results 16 2013 Monetizing Data Management Survey Results
  17. 17. Copyright 2013 by Data Blueprint 1. Data Management Overview 2. Book Motivations & Survey Results 3. Leveraging Data 4. Monetary ROI (6 cases) 5. Non-Monetary ROI (2 cases) 6. Legal Considerations 7. Take Aways and Q&A Outline 17
  18. 18. Copyright 2013 by Data Blueprint 1. Data Management Overview 2. Book Motivations & Survey Results 3. Leveraging Data 4. Monetary ROI (6 cases) 5. Non-Monetary ROI (2 cases) 6. Legal Considerations 7. Take Aways and Q&A Outline 18
  19. 19. Copyright 2013 by Data Blueprint Data Data Data Information Fact Meaning Request Strategic Information Use: Prerequisites [Built on definitions from Dan Appleton 1983] Intelligence Strategic Use 1. Each FACT combines with one or more MEANINGS. 2. Each specific FACT and MEANING combination is referred to as a DATUM. 3. An INFORMATION is one or more DATA that are returned in response to a specific REQUEST 4. INFORMATION REUSE is enabled when one FACT is combined with more than one MEANING. 5. INTELLIGENCE is INFORMATION associated with its STRATEGIC USES. 6. DATA/INFORMATION must formally arranged into an ARCHITECTURE. Wisdom & knowledge are often used synonymously Data Data Data Data 19
  20. 20. Copyright 2013 by Data Blueprint Leverage is an Engineering Concept 20 • Using proper engineering techniques, a human can lift a bulk that is weighs much more than the human
  21. 21. Copyright 2013 by Data Blueprint Data Leverage is an Engineering Concept 21 Organizational Data Organizational Data Managers Technologies Process People • Note: Reducing ROT increases data leverage Less Data ROT ->
  22. 22. Copyright 2013 by Data Blueprint Why Is Data Management Important? • Too much data leads directly to wasted productivity – Eighty percent (80%) of organizational data is redundant, obsolete or trivial (ROT) • Underutilized data leads directly to poorly leveraged organizational resources – Manpower – costs associated with labor resources and market share – Money – costs associated with management of financial resources – Methods – costs associated with operational processes and product delivery – Machines – costs associated with hardware, software applications and data to enhance production capability 22
  23. 23. Copyright 2013 by Data Blueprint Incorrect Educational Focus • Building new systems – 80% of IT costs are spent rebuilding and evolving existing systems and only 20% of costs are spent building and acquiring new systems – Putting fresh graduates on new projects makes this proposition more ridiculous – Only the most experienced professionals should be allowed to participate in new systems development. • Who is responsible for managing data assets? – Business thinks IT is taking care of it - it is called IT after all? – IT thinks if you can sign on to the system their job is complete • System development practices – Data evolution is separate from, external to and must precede system development life cycle activities! – Data is not a project - it has no distinct beginning and end 23
  24. 24. Copyright 2013 by Data Blueprint Evolving Data is Different than Creating New Systems 24 Common Organizational Data (and corresponding data needs requirements) New Organizational Capabilities Systems Development Activities Create Evolve Future State (Version +1) Data evolution is separate from, external to, and precedes system development life cycle activities!
  25. 25. Copyright 2013 by Data Blueprint Application-Centric Development Original articulation from Doug Bagley @ Walmart 25 Data/ Information Network/ Infrastructure Systems/ Applications Goals/ Objectives Strategy • In support of strategy, organizations develop 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: – Ensures data is formed to the applications and not around the organizational-wide information requirements – Process are narrowly formed around applications – Very little data reuse is possible
  26. 26. Copyright 2013 by Data Blueprint Payroll Application (3rd GL)Payroll Data (database) R& D Applications (researcher supported, no documentation) R & D Data (raw) Mfg. Data (home grown database) Mfg. Applications (contractor supported) Finance Data (indexed) Finance Application (3rd GL, batch system, no source) Marketing Application (4rd GL, query facilities, no reporting, very large) Marketing Data (external database) Personnel App. (20 years old, un-normalized data) Personnel Data (database) 26 Typical System Evolution
  27. 27. Copyright 2013 by Data Blueprint Data-Centric Development Original articulation from Doug Bagley @ Walmart 27 Systems/ Applications Network/ Infrastructure Data/ Information Goals/ Objectives Strategy • 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 needs and compliment organizational process flows – Maximum data/information reuse
  28. 28. Copyright 2013 by Data Blueprint Polling Question #1 • Who or what department(s) makes the decision on investing in data management initiatives? A) IT B) Supported business area C) IT and the supported business area together D) Office of Chief Data Officer or Enterprise Data Office/Equivalent 28
  29. 29. Copyright 2013 by Data Blueprint 1. Data Management Overview 2. Book Motivations & Survey Results 3. Leveraging Data 4. Monetary ROI (6 cases) 5. Non-Monetary ROI (2 cases) 6. Legal Considerations 7. Take Aways and Q&A Outline 29
  30. 30. Copyright 2013 by Data Blueprint 1. Data Management Overview 2. Book Motivations & Survey Results 3. Leveraging Data 4. Monetary ROI (6 cases) 5. Non-Monetary ROI (2 cases) 6. Legal Considerations 7. Take Aways and Q&A Outline 30
  31. 31. Copyright 2013 by Data Blueprint Monitization: Time & Leave Tracking 31 At Least 300 employees are spending 15 minutes/week tracking leave/time
  32. 32. Copyright 2013 by Data Blueprint 32 Capture Cost of Labor/Category
  33. 33. District-L (as an example) Leave Tracking Time Accounting Employees 73 50 Number of documents 1000 2040 Timesheet/employee 13.70 40.8 Time spent 0.08 0.25 Hourly Cost $6.92 $6.92 Additive Rate $11.23 $11.23 Semi-monthly cost per timekeeper $12.31 $114.56 Total semi-monthly timekeeper cost $898.49 $5,727.89 Annual cost $21,563.83 $137,469.40 Copyright 2013 by Data Blueprint 33 Compute Labor Costs
  34. 34. • 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 Copyright 2013 by Data Blueprint 34 Annual Organizational Totals
  35. 35. Copyright 2013 by Data Blueprint International Chemical Company Engine Testing 35 • $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 up to $250,000!
  36. 36. Copyright 2013 by Data Blueprint Overview of Existing Data Management Process 36 1. Manual transfer of digital data 2. Manual file movement/duplication 3. Manual data manipulation 4. Disparate synonym reconciliation 5. Tribal knowledge requirements 6. Non-sustainable technology 33
  37. 37. Copyright 2013 by Data Blueprint 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 37
  38. 38. Copyright 2013 by Data Blueprint Vocabulary is Important-Tank, Tanks, Tankers, Tanked 38
  39. 39. Copyright 2013 by Data Blueprint How one inventory item proliferates data throughout the chain 39 555  Subassemblies  &  subcomponents 17,659  Repair  parts  or  Consumables System 1: 18,214 Total items 75 Attributes/ item 1,366,050 Total attributes System  2 47  Total  items 15+  A>ributes/item 720  Total  a>ributes System  3 16,594  Total  items 73  A>ributes/item 1,211,362  Total  a>ributes System  4 8,535  Total  items 16    A>ributes/item 136,560  Total  a>ributes System  5 15,959    Total  items 22    A>ributes/item 351,098  Total  a>ributes Total  for  the  five  systems  show  above: 59,350  Items 179  Unique  a>ributes 3,065,790  values
  40. 40. • 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 & RTLS Copyright 2013 by Data Blueprint Business Implications
  41. 41. Copyright 2013 by Data Blueprint Improving Data Quality during System Migration 41 • 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
  42. 42. Unmatched Items Ignorable Items Items Matched Week # (% Total) (% Total) (% Total) 1 31.47% 1.34% N/A 2 21.22% 6.97% N/A 3 20.66% 7.49% N/A 4 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% Copyright 2013 by Data Blueprint Determining Diminishing Returns 42
  43. 43. 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 2,000,000 Average time to review & cleanse (in minutes) 5 Total Time (in minutes) 10,000,000 Time available per resource over a one year period of time:Time available per resource over a one year period of time: Work weeks in a year 48 Work days in a week 5 Work hours in a day 7.5 Work minutes in a day 450 Total Work minutes/year 108,000 Person years required to cleanse each NSN once prior to migration:Person years required to cleanse each NSN once prior to migration: Minutes needed 10,000,000 Minutes available person/year 108,000 Total Person-Years 92.6 Resource Cost to cleanse NSN's prior to migration:Resource Cost to cleanse NSN's prior to migration: Avg Salary for SME year (not including overhead) $60,000.00 Projected Years Required to Cleanse/Total DLA Person Year Saved 93 Total Cost to Cleanse/Total DLA Savings to Cleanse NSN's: $5.5 million Copyright 2013 by Data Blueprint 43 Quantitative Benefits
  44. 44. Copyright 2013 by Data Blueprint Seven Sisters (from British Telecom) 44 Thanks to Dave Evans
  45. 45. Copyright 2013 by Data Blueprint Polling Question #2 • Is it hard to obtain funding for your data management projects? A) Yes, because it is hard to show value B) Yes, because we have not aligned with the business objectives C) Yes, because no precedent has been set D) No, because we can clearly demonstrate value 45
  46. 46. Copyright 2013 by Data Blueprint 1. Data Management Overview 2. Book Motivations & Survey Results 3. Leveraging Data 4. Monetary ROI (6 cases) 5. Non-Monetary ROI (2 cases) 6. Legal Considerations 7. Take Aways and Q&A Outline 46
  47. 47. Copyright 2013 by Data Blueprint 1. Data Management Overview 2. Book Motivations & Survey Results 3. Leveraging Data 4. Monetary ROI (6 cases) 5. Non-Monetary ROI (2 cases) 6. Legal Considerations 7. Take Aways and Q&A Outline 47
  48. 48. In one of the more horrifying incidents I've read about, U.S. soldiers and allies were killed in December 2001 because of a stunningly poor design of a GPS receiver, plus "human error." http://www.washingtonpost.com/wp-dyn/articles/A8853-2002Mar23.html A U.S. Special Forces air controller was calling in GPS positioning from some sort of battery-powered device. He "had used the GPS receiver to calculate the latitude and longitude of the Taliban position in minutes and seconds for an airstrike by a Navy F/A-18." According to the *Post* story, the bomber crew "required" a "second calculation in 'degree decimals'" -- why the crew did not have equipment to perform the minutes-seconds conversion themselves is not explained. The air controller had recorded the correct value in the GPS receiver when the battery died. Upon replacing the battery, he called in the degree-decimal position the unit was showing -- without realizing that the unit is set up to reset to its *own* position when the battery is replaced. The 2,000-pound bomb landed on his position, killing three Special Forces soldiers and injuring 20 others. If the information in this story is accurate, the RISKS involve replacing memory settings with an apparently-valid default value instead of blinking 0 or some other obviously-wrong display; not having a backup battery to hold values in memory during battery replacement; not equipping users to translate one coordinate system to another; and using a device with such flaws in a combat situation Copyright 2013 by Data Blueprint Friendly Fire deaths traced to Dead Battery 48
  49. 49. Suicide Mitigation Copyright 2013 by Data Blueprint 49
  50. 50. Suicide MitigationData Mapping 12 Mental illness Deploy ments 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 Copyright 2013 by Data Blueprint 50
  51. 51. Copyright 2013 by Data Blueprint 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 51
  52. 52. Copyright 2013 by Data Blueprint Communication Patterns 52 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
  53. 53. Copyright 2013 by Data Blueprint Polling Question #3 • What percentage of your data projects are successful? A) All B) 25% C) 75% D) none 53
  54. 54. Copyright 2013 by Data Blueprint 1. Data Management Overview 2. Book Motivations & Survey Results 3. Leveraging Data 4. Monetary ROI (6 cases) 5. Non-Monetary ROI (2 cases) 6. Legal Considerations 7. Take Aways and Q&A Outline 54
  55. 55. Copyright 2013 by Data Blueprint 1. Data Management Overview 2. Book Motivations & Survey Results 3. Leveraging Data 4. Monetary ROI (6 cases) 5. Non-Monetary ROI (2 cases) 6. Legal Considerations 7. Take Aways and Q&A Outline 55
  56. 56. 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 Copyright 2013 by Data Blueprint Messy Sequencing Towards Arbitration 56
  57. 57. Copyright 2013 by Data Blueprint 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? 57
  58. 58. Copyright 2013 by Data Blueprint Expert Reports Ours provided evidence that : 1. Company Y's conversion code introduced errors into the data 2. 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 58
  59. 59. Copyright 2013 by Data Blueprint FBI & Canadian Social Security Gender Codes 1. Male 2. Female 3. Formerly male now female 4. Formerly female now male 5. Uncertain 6. Won't tell 7. Doesn't know 8. Male soon to be female 9. Female soon to be male If column 1 in source = "m" • then set value of target data to "male" • else set value of target data to "female" 51
  60. 60. Copyright 2013 by Data Blueprint The defendant knew to prevent duplicate SSNs !************************************************************************ ! Procedure Name: 230-Assign-PS-Emplid ! ! Description : This procedure generates a PeopleSoft Employee ID ! (Emplid) by incrementing the last Emplid processed by 1 ! First it checks if the applicant/employee exists on ! the PeopleSoft database using the SSN. ! !************************************************************************ Begin-Procedure 230-Assign-PS-Emplid move 'N' to $found_in_PS !DAR 01/14/04 move 'N' to $found_on_XXX !DAR 01/14/04 BEGIN-SELECT -Db'DSN=HR83PRD;UID=PS_DEV;PWD=psdevelopment' NID.EMPLID NID.NATIONAL_ID move 'Y' to $found_in_PS !DAR 01/14/04 move &NID.EMPLID to $ps_emplid FROM PS_PERS_NID NID !WHERE NID.NATIONAL_ID = $ps_ssn WHERE NID.AJ_APPL_ID = $applicant_id END-SELECT if $found_in_PS = 'N' !DAR 01/14/04 do 231-Check-XXX-for-Empl !DAR 01/14/04 if $found_on_XXX = 'N' !DAR 01/14/04 add 1 to #last_emplid let $last_emplid = to_char(#last_emplid) let $last_emplid = lpad($last_emplid,6,'0') let $ps_emplid = 'AJ' || $last_emplid end-if end-if !DAR 01/14/04 End-Procedure 230-Assign-PS-Emplid AJHR0213_CAN_UPDATE.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. 60
  61. 61. Copyright 2013 by Data Blueprint 61
  62. 62. Copyright 2013 by Data Blueprint Identified & Quantified Risks 62
  63. 63. Copyright 2013 by Data Blueprint Risk Response “Risk response development involves defining enhancement steps for opportunities and threats.” Page 119, Duncan, W., A Guide to the Project Management Body of Knowledge, PMI, 1996 "The go-live date may need to be extended due to certain critical path deliverables not being met. This extension will require additional tasks and resources. The decision of whether or not to extend the go-live date should be made by Monday, November 3, 20XX so that resources can be allocated to the additional tasks." Tasks Hours New Year Conversion 120 Tax and payroll balance conversion 120 General Ledger conversion 80 Total 320 Resource Hours G/L Consultant 40 Project Manager 40 Recievables Consultant 40 HRMS Technical Consultant 40 Technical Lead Consultant 40 HRMS Consultant 40 Financials Technical Consultant 40 Total 280 Delay Weekly Resources Weeks Tasks Cumulative January (5 weeks) 280 5 320 1720 February (4 weeks) 280 4 1120 Total 2840 63
  64. 64. Process Planning Area Company YCompany Y Company X Lead Methodology 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 √ Copyright 2013 by Data Blueprint Project Management Planning 64
  65. 65. Copyright 2013 by Data Blueprint Inadequate Standard of Care - Tasks without Predecessors 65
  66. 66. Copyright 2013 by Data Blueprint Inadequate Standard of Care 66
  67. 67. Copyright 2013 by Data Blueprint Professional & Workmanlike Manner 67 Defendant warrants that the services it provides hereunder will be performed in a professional and workmanlike manner in accordance with industry standards.
  68. 68. Copyright 2013 by Data Blueprint 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. 68
  69. 69. Copyright 2013 by Data Blueprint 1. Data Management Overview 2. Book Motivations & Survey Results 3. Leveraging Data 4. Monetary ROI (6 cases) 5. Non-Monetary ROI (2 cases) 6. Legal Considerations 7. Take Aways and Q&A Outline 69
  70. 70. Copyright 2013 by Data Blueprint 1. Data Management Overview 2. Book Motivations & Survey Results 3. Leveraging Data 4. Monetary ROI (6 cases) 5. Non-Monetary ROI (2 cases) 6. Legal Considerations 7. Take Aways and Q&A Outline 70
  71. 71. Monetizing Data Management Copyright 2013 by Data Blueprint 71 • State Agency Time & Leave Tracking – Time and leave tracking • $1 million USD annually • International Chemical Company – Data management: Test results – $25 million UDS annually • ERP Implementation – Transformation of non-tabular data • $5 million annually • Person Centuries • British Telecom Project Rollout – £250 (small investment) • Non-Monetary Examples – Friendly Fire – Suicide Mitigation • Legal – ERP Implementation Legal Case • $ 5,355,450 CAN damages/penalties PETER AIKEN WITH JUANITA BILLINGS FOREWORD BY JOHN BOTTEGA MONETIZING DATA MANAGEMENT Unlocking the Value in Your Organization’s Most Important Asset.
  72. 72. Copyright 2013 by Data Blueprint Questions? 72 It’s your turn! Use the chat feature or Twitter (#dataed) to submit your questions to Peter now. + =
  73. 73. Copyright 2013 by Data Blueprint Upcoming Events 73 November Webinar: Unlock Business Value Through Reference & MDM Novemeber 12, 2013 @ 2:00 PM – 3:30 PM ET (11:00 AM-12:30 PM PT) December: Unlock Business Value Through Document & Content Management December 10, 2013 @ 2:00 PM – 3:30 PM ET (11:00 AM-12:30 PM PT) Sign up here: • www.datablueprint.com/webinar-schedule • www.Dataversity.net Brought to you by:
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