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Data-Ed: Monetizing Data Management

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Many data professionals struggle with the ability to demonstrate tangible returns on data management investments. In a webinar that is designed to appeal to both business and IT attendees, your presenter will describe multiple types of value produced through data-centric development and management practices. One of our examples, the healthcare space, offers the unique opportunity to demonstrate additional types of return on investment or value outcomes, namely returns in the form of lives saved through increased rates of Bone Marrow Donor matches. In addition to metrics around increasing revenues or decreasing costs, i.e. investments that directly impact an organization’s financial position, these additional statistics of lives saved can be used to justify data management and quality initiatives.

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Data-Ed: 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. http://www.datablueprint.com/monetizing-data-management-survey/  1 PETER AIKEN WITH JUANITA BILLINGS FOREWORD BY JOHN BOTTEGA MONETIZING DATA MANAGEMENT Unlocking the Value in Your Organization’s Most Important Asset. Basic Data Management Practices – Data Program Management – Organizational Data Integration – Data Stewardship – Data Development – Data Support Operations Advanced Data Practices • MDM • Mining • Big Data • Analytics • Warehousing • SOA Data Data Data Information Fact Meaning Request [Built on definitions from Dan Appleton 1983] Intelligence Strategic Use Wisdom & knowledge are often used synonymously Data Data Data Data 18 Organizational Data Organizational Data Managers Technologies Process People Less Data ROT -> 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! Data/ Information Network/ Infrastructure Systems/ Applications Goals/ Objectives Strategy 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) Systems/ Applications Network/ Infrastructure Data/ Information Goals/ Objectives Strategy 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 34 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 Data 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
  2. 2. Copyright 2013 by Data Blueprint Executive Editor at DATAVERSITY.net 2 Shannon Kempe
  3. 3. 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 3 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
  4. 4. Show Me The Money Monetizing Data Management
 Presented by Peter Aiken, Ph.D. PETER AIKEN WITH JUANITA BILLINGS FOREWORD BY JOHN BOTTEGA MONETIZING DATA MANAGEMENT Unlocking the Value in Your Organization’s Most Important Asset.
  5. 5. Copyright 2013 by Data Blueprint 5 2 • 30+ years of experience in data management • Multiple international awards & 
 recognition • Founder, Data Blueprint (datablueprint.com) • Associate Professor of IS, VCU (vcu.edu) • (Past) President, DAMA Int. (dama.org) • 9 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, Walmart, and the Commonwealth of Virginia Peter Aiken, Ph.D. The Case for the Chief Data Officer Recasting the C-Suite to Leverage Your MostValuable Asset Peter Aiken and Michael Gorman PETER AIKEN WITH JUANITA BILLINGS FOREWORD BY JOHN BOTTEGA MONETIZING DATA MANAGEMENT Unlocking the Value in Your Organization’s Most Important Asset.
  6. 6. 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 PETER AIKEN WITH JUANITA BILLINGS FOREWORD BY JOHN BOTTEGA MONETIZING DATA MANAGEMENT Unlocking the Value in Your Organization’s Most Important Asset. Outline 6 Tweeting now: #dataed
  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 PETER AIKEN WITH JUANITA BILLINGS FOREWORD BY JOHN BOTTEGA MONETIZING DATA MANAGEMENT Unlocking the Value in Your Organization’s Most Important Asset. Outline 7
  8. 8. Data Program 
 Coordination Feedback Data
 Development Copyright 2013 by Data Blueprint Standard
 Data Data Management is an Integrated System of Five Practice Areas Organizational Strategies Goals Business
 Data Business Value Application 
 Models & Designs Implementation Direction Guidance 8 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
  9. 9. Copyright 2013 by Data Blueprint Five Integrated DM Practices 9 Manage data coherently. Share data across boundaries. Assign responsibilities for data. Engineer data delivery systems. Maintain data availability. Data Program 
 Coordination Data
 Development Organizational
 Data Integration Data
 Stewardship Data Support
 Operations
  10. 10. Maslow's Hierarchiy of Needs Copyright 2013 by Data Blueprint 10
  11. 11. You can accomplish Advanced Data Practices without becoming proficient in the Basic Data Management Practices however this will: • Take longer • Cost more • Deliver less • Present 
 greater
 risk Copyright 2013 by Data Blueprint Data Management Practices Hierarchy Basic Data Management Practices Advanced 
 Data 
 Practices • MDM • Mining • Big Data • Analytics • Warehousin g • SOA 11 Data Program Management Data Stewardship Data Development Data Support Operations Organizational Data Integration
  12. 12. 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 12 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
  13. 13. Copyright 2013 by Data Blueprint Five Integrated DM Practice Areas 13 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
  14. 14. Copyright 2013 by Data Blueprint Hierarchy of Data Management Practices (after Maslow) 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 • Warehousin g • 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
  15. 15. 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 PETER AIKEN WITH JUANITA BILLINGS FOREWORD BY JOHN BOTTEGA MONETIZING DATA MANAGEMENT Unlocking the Value in Your Organization’s Most Important Asset. Outline 15
  16. 16. 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 PETER AIKEN WITH JUANITA BILLINGS FOREWORD BY JOHN BOTTEGA MONETIZING DATA MANAGEMENT Unlocking the Value in Your Organization’s Most Important Asset. Outline 16
  17. 17. Copyright 2013 by Data Blueprint 2013 Monetizing Data Management Survey Results 17 http://www.datablueprint.com/monetizing-data-management-survey/ 
  18. 18. Copyright 2013 by Data Blueprint • Soon to be released: white paper & survey results 18 2013 Monetizing Data Management Survey Results http://www.datablueprint.com/monetizing-data-management-survey/ 
  19. 19. Copyright 2013 by Data Blueprint Amazon Reviews 19
  20. 20. Copyright 2013 by Data Blueprint One Star Reviews • "My reason for purchasing this book was to learn 
 about how organizations are finding ways to monitize their data assets. By that I mean finding ways to generate income using their data assets or the insights derived from those assets." • "This book title 'Monetizing data management', the reason I purchased this book is to know how to earn the money from organizational data. however this book didn't talk anything about making money through data management." 20 PETER AIKEN WITH JUANITA BILLINGS FOREWORD BY JOHN BOTTEGA MONETIZING DATA MANAGEMENT Unlocking the Value in Your Organization’s Most Important Asset.
  21. 21. Copyright 2013 by Data Blueprint Five Star Reviews • "A book you can read from cover to cover on an 
 airplane trip or during lunch over a period of days. I'm very big on stories, and the book contains many stories from the authors' experiences on how to valuate data management. It helped me brainstorm on a presentation I was working on to explain the value of our enterprise information management initiative." • "A concise summary of how to put a value on data management in your organization. I would not categorize this book as a "how to" guide - more of a brainstorming book to help someone come up with a value for their hard data management work. Great stories and tangible results!" 21 PETER AIKEN WITH JUANITA BILLINGS FOREWORD BY JOHN BOTTEGA MONETIZING DATA MANAGEMENT Unlocking the Value in Your Organization’s Most Important Asset.
  22. 22. Copyright 2013 by Data Blueprint Motivation ... • Amazon rank: 1,257,801 • 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 22 PETER AIKEN WITH JUANITA BILLINGS FOREWORD BY JOHN BOTTEGA MONETIZING DATA MANAGEMENT Unlocking the Value in Your Organization’s Most Important Asset.
  23. 23. 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 PETER AIKEN WITH JUANITA BILLINGS FOREWORD BY JOHN BOTTEGA MONETIZING DATA MANAGEMENT Unlocking the Value in Your Organization’s Most Important Asset. Outline 23
  24. 24. 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 PETER AIKEN WITH JUANITA BILLINGS FOREWORD BY JOHN BOTTEGA MONETIZING DATA MANAGEMENT Unlocking the Value in Your Organization’s Most Important Asset. Outline 24
  25. 25. 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 25
  26. 26. Copyright 2013 by Data Blueprint Leverage is an Engineering Concept 26 • Using proper engineering techniques, a human can lift a bulk that is weighs much more than the human
  27. 27. Copyright 2013 by Data Blueprint Data Leverage is an Engineering Concept 27 
 
 
 Organizationa l Data Organizational Data Managers Technologies Process People • Note: Reducing ROT increases data leverage Less Data ROT ->
  28. 28. 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 28
  29. 29. 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 29
  30. 30. Copyright 2013 by Data Blueprint Evolving Data is Different than Creating New Systems 30 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!
  31. 31. Copyright 2013 by Data Blueprint Application-Centric Development Original articulation from Doug Bagley @ Walmart 31 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
  32. 32. 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) 32 Typical System Evolution
  33. 33. Einstein Quote Copyright 2013 by Data Blueprint 33 "The significant problems we face cannot be solved at the same level of thinking we were at when we created them."
 - Albert Einstein
  34. 34. Copyright 2013 by Data Blueprint Data-Centric Development Original articulation from Doug Bagley @ Walmart 34 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
  35. 35. 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 35
  36. 36. 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 PETER AIKEN WITH JUANITA BILLINGS FOREWORD BY JOHN BOTTEGA MONETIZING DATA MANAGEMENT Unlocking the Value in Your Organization’s Most Important Asset. Outline 36
  37. 37. 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 PETER AIKEN WITH JUANITA BILLINGS FOREWORD BY JOHN BOTTEGA MONETIZING DATA MANAGEMENT Unlocking the Value in Your Organization’s Most Important Asset. Outline 37
  38. 38. Copyright 2013 by Data Blueprint Monitization: Time & Leave Tracking 38 At Least 300 employees are spending 15 minutes/week tracking leave/time
  39. 39. Copyright 2013 by Data Blueprint 39 Capture Cost of Labor/Category
  40. 40. District-L (as an example) Leave Tracking Time Accounting Employees 73 50 Number of documents 1000 2040 Timesheet/employee 13.7 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 40 Compute Labor Costs
  41. 41. • 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 41 Annual Organizational Totals
  42. 42. Copyright 2013 by Data Blueprint International Chemical Company Engine Testing 42 • $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!
  43. 43. Copyright 2013 by Data Blueprint 43 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
  44. 44. 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 44
  45. 45. Copyright 2013 by Data Blueprint Vocabulary is Important-Tank, Tanks, Tankers, Tanked 45
  46. 46. Copyright 2013 by Data Blueprint How one inventory item proliferates data throughout the chain 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+ Attributes/item
 720 Total attributes System 3 16,594 Total items 73 Attributes/item 1,211,362 Total attributes System 4
 8,535 Total items
 16 Attributes/item
 136,560 Total attributes System 5
 15,959 Total items
 22 Attributes/item
 351,098 Total attributes Total for the five systems show above:
 59,350 Items
 179 Unique attributes
 3,065,790 values
  47. 47. • 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
  48. 48. Copyright 2013 by Data Blueprint Improving Data Quality during System Migration 48 • 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
  49. 49. 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 9.5% 22.62% 67.88% 18 7.46% 22.62% 69.92% Copyright 2013 by Data Blueprint Determining Diminishing Returns 49
  50. 50. Time needed to review all NSNs once over the life of the project: NSNs 2,000,000 Average time to review & cleanse (in minutes) 5 Total Time (in minutes) 10,000,000 Time available per resource over a one year period of time: Work weeks in a year 48 Work days in a week 5 Work hours in a day 7.5 Work minutes in a day 450 Total Work minutes/year 108,000 Person years required to cleanse each NSN once prior to migration: Minutes needed 10,000,000 Minutes available person/year 108,000 Total Person-Years 92.6 Resource Cost to cleanse NSN's prior to migration: Avg Salary for SME year (not including overhead) $60,000.00 Projected Years Required to Cleanse/Total DLA Person Year Saved 93 Total Cost to Cleanse/Total DLA Savings to Cleanse NSN's: $5.5 million Copyright 2013 by Data Blueprint 50 Quantitative Benefits
  51. 51. Copyright 2013 by Data Blueprint Seven Sisters (from British Telecom) 51 Thanks to Dave Evans http://www.datablueprint.com/thought-leaders/peter-aiken/book-monetizing-data-management/
  52. 52. 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 52
  53. 53. 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 PETER AIKEN WITH JUANITA BILLINGS FOREWORD BY JOHN BOTTEGA MONETIZING DATA MANAGEMENT Unlocking the Value in Your Organization’s Most Important Asset. Outline 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 PETER AIKEN WITH JUANITA BILLINGS FOREWORD BY JOHN BOTTEGA MONETIZING DATA MANAGEMENT Unlocking the Value in Your Organization’s Most Important Asset. Outline 54
  55. 55. 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
 55
  56. 56. Suicide Mitigation Copyright 2013 by Data Blueprint 56
  57. 57. 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 57
  58. 58. 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 58
  59. 59. Copyright 2013 by Data Blueprint Communication Patterns 59 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
  60. 60. Copyright 2013 by Data Blueprint Polling Question #3 • What percentage of your data projects are successful? A) All B) 25% C) 75% D) none 60
  61. 61. 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 PETER AIKEN WITH JUANITA BILLINGS FOREWORD BY JOHN BOTTEGA MONETIZING DATA MANAGEMENT Unlocking the Value in Your Organization’s Most Important Asset. Outline 61
  62. 62. 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 PETER AIKEN WITH JUANITA BILLINGS FOREWORD BY JOHN BOTTEGA MONETIZING DATA MANAGEMENT Unlocking the Value in Your Organization’s Most Important Asset. Outline 62
  63. 63. 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 63
  64. 64. 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? 64
  65. 65. 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 65
  66. 66. 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
  67. 67. 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. 67
  68. 68. Copyright 2013 by Data Blueprint 68
  69. 69. Copyright 2013 by Data Blueprint Identified & Quantified Risks 69
  70. 70. 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 70
  71. 71. Process Planning Area Company 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 71
  72. 72. Copyright 2013 by Data Blueprint Inadequate Standard of Care - Tasks without Predecessors 72
  73. 73. Copyright 2013 by Data Blueprint Inadequate Standard of Care
 73
  74. 74. Copyright 2013 by Data Blueprint Professional & Workmanlike Manner 74 Defendant warrants that the services it provides hereunder will be performed in a professional and workmanlike manner in accordance with industry standards.
  75. 75. 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. 75
  76. 76. 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 PETER AIKEN WITH JUANITA BILLINGS FOREWORD BY JOHN BOTTEGA MONETIZING DATA MANAGEMENT Unlocking the Value in Your Organization’s Most Important Asset. Outline 76
  77. 77. 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 PETER AIKEN WITH JUANITA BILLINGS FOREWORD BY JOHN BOTTEGA MONETIZING DATA MANAGEMENT Unlocking the Value in Your Organization’s Most Important Asset. Outline 77
  78. 78. Monetizing Data Management Copyright 2013 by Data Blueprint 78 • 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.
  79. 79. Copyright 2014 by Data Blueprint Questions? 79 + = It’s your turn! Use the chat feature or Twitter (#dataed) to submit your questions to Peter now.

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