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Data Quality Success Stories

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<p>Good data is like good water: best served fresh, and ideally well-filtered. Data Management strategies can produce tremendous procedural improvements and increased profit margins across the board, but only if the data being managed is of a high quality. Determining how Data Quality should be engineered provides a useful framework for utilizing Data Quality Management effectively in support of business strategy, which in turn allows for speedy identification of business problems, delineation between structural and practice-oriented defects in Data Management, and proactive prevention of future issues. Organizations must realize what it means to utilize Data Quality engineering in support of business strategy. This webinar will illustrate how organizations with chronic business challenges often can trace the root of the problem to poor Data Quality. Showing how Data Quality should be engineered provides a useful framework in which to develop an effective approach. This in turn allows organizations to more quickly identify business problems as well as data problems caused by structural issues versus practice-oriented defects and prevent these from re-occurring.</p>
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<p>Learning Objectives:</p>
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<ul><li>Understand foundational Data Quality concepts based on the DAMA Guide to Data Management Book of Knowledge (DAMA DMBOK), as well as guiding principles, best practices, and steps for improving Data Quality at your organization</li><li>Recognize how chronic business challenges for organizations are often rooted in poor Data Quality</li><li>Share case studies illustrating the hallmarks and benefits of Data Quality success</li></ul>
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Data Quality Success Stories

  1. 1. Data Quality Success Stories 1Copyright 2019 by Data Blueprint Slide # Time: • timeliness • currency • frequency • time period Form: • clarity • detail • order • presentation • media Content: • accuracy • relevance • completeness • conciseness • scope • performance Starting point for new system development data performance metadata data architecture data architecture and data models shared data updated data corrected data architecture refinements facts & meanings Metadata & Data Storage Starting point for existing systems Metadata Refinement • Correct Structural Defects • Update Implementation Metadata Creation • Define Data Architecture • Define Data Model Structures Metadata Structuring • Implement Data Model Views • Populate Data Model Views Data Refinement • Correct Data Value Defects • Re-store Data Values Data Manipulation • Manipulate Data • Updata Data Data Utilization • Inspect Data • Present Data Data Creation • Create Data • Verify Data Values Data Assessment • Assess Data Values • Assess Metadata Data & Data Relationships Hypotheses, Rules and Quantifications Queries and Reports High Probability Data Quality Problem Cause Formulation Raw Data Good data is like good water: best served fresh, and ideally well-filtered. Data Management strategies can produce tremendous procedural improvements and increased profit margins across the board, but only if the data being managed is of a high quality. Determining how Data Quality should be engineered provides a useful framework for utilizing Data Quality Management effectively in support of business strategy, which in turn allows for speedy identification of business problems, delineation between structural and practice-oriented defects in Data Management, and proactive prevention of future issues. Organizations must realize what it means to utilize Data Quality engineering in support of business strategy. This webinar will illustrate how organizations with chronic business challenges often can trace the root of the problem to poor Data Quality. Showing how Data Quality should be engineered provides a useful framework in which to develop an effective approach. This in turn allows organizations to more quickly identify business problems as well as data problems caused by structural issues versus practice-oriented defects and prevent these from re-occurring. Learning Objectives • Help you understand foundational data quality concepts based on the DAMA Guide to Data Management Book of Knowledge (DAMA DMBOK), as well as guiding principles, best practices, and steps for improving data quality at your organization • Demonstrate how chronic business challenges for organizations 
 are often rooted in poor data quality • Share case studies illustrating the hallmarks and benefits of 
 data quality success Date: October 8, 2019 Time: 2:00 PM ET/11:00 AM PT UTC-4 Presenter: Peter Aiken, Ph.D. 2Copyright 2019 by Data Blueprint Slide # Shannon Kempe Chief Digital Manager at Dataversity.net Commonly 
 Asked 
 Questions 3Copyright 2019 by Data Blueprint Slide # 1) Will I get copies of the slides after the event? 2) Is this being recorded? Get Social With Us! 4Copyright 2019 by Data Blueprint Slide # 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 Live Twitter Feed Join the conversation! Follow us: @datablueprint @paiken Ask questions and submit your comments: #dataed • DAMA International President 2009-2013 / 2018 • DAMA International Achievement Award 2001 
 (with Dr. E. F. "Ted" Codd • DAMA International Community Award 2005 Peter Aiken, Ph.D. 5Copyright 2019 by Data Blueprint Slide # • I've been doing this a long time • My work is recognized as useful • Associate Professor of IS (vcu.edu) • Founder, Data Blueprint (datablueprint.com) • DAMA International (dama.org) • 10 books and dozens of articles • Experienced w/ 500+ data management practices worldwide • Multi-year immersions – US DoD (DISA/Army/Marines/DLA) – Nokia – Deutsche Bank – Wells Fargo – Walmart – … PETER AIKEN WITH JUANITA BILLINGS FOREWORD BY JOHN BOTTEGA MONETIZING DATA MANAGEMENT Unlocking the Value in Your Organization’s Most Important Asset. Data Quality Success Stories Copyright 2019 by Data Blueprint Slide # 6Peter Aiken, PhD
  2. 2. Who is Joan Smith? http://www.dataflux.com 7Copyright 2019 by Data Blueprint Slide # Challenges • Purchased an A4 on June 15 2007 • Had not done business with the dealership prior • "makes them seem sleazy when I get a letter in the mail before I've even made the first payment on the car advertising lower payments than I got" 8Copyright 2019 by Data Blueprint Slide # Letter from the Bank … so please continue to open your mail from either Chase or Bank One P.S. Please be on the lookout for any upcoming communications from either Chase or Bank One regarding your Bank One credit card and any other Bank One product you may have. Problems • I initially discarded the letter! • I became upset after reading it • It proclaimed that Chase has data quality challenges 9Copyright 2019 by Data Blueprint Slide # How to solve this data quality problem using just tools? Retail price for the unit was $40 10Copyright 2019 by Data Blueprint Slide # A congratulations letter from another bank Problems • Bank did not know it made an error • Tools alone could not have prevented this error • Lost confidence in the ability of the bank to manage customer funds 11Copyright 2019 by Data Blueprint Slide # 12Copyright 2019 by Data Blueprint Slide # DropTable
  3. 3. 13Copyright 2019 by Data Blueprint Slide # 14Copyright 2019 by Data Blueprint Slide # Data Quality Success Stories - Program Overview 1. Data quality must be understood as 
 an engineering challenge 2. Putting a price on data quality 3. DM BoK components compliment 
 each other well 4. Savings based stories 5. Innovation based stories 6. Non-monetary stories 7. Takeaways and Q&A Four ways to make your data sparkle! 1.Prioritize the task – Cleaning data is costly and time consuming – Identify mission critical/non-mission critical data 2.Involve the data owners – Seek input of business units on what constitutes "dirty" data 3.Keep future data clean – Incorporate processes and technologies that check every zip code and area code 4.Align your staff with business – Align IT staff with business units (Source: CIO JULY 1 2004) 15Copyright 2019 by Data Blueprint Slide # 16Copyright 2019 by Data Blueprint Slide # • Information transparency • Analytics • Business Intelligence • Increasing efficiencies • Decreasing costs • Driving holistic decision-making across the organization High Quality Data is Critical • SQL Server – 47,000,000,000,000 bytes – Largest table 34 billion records • Informix – 1,800,000,000 queries/day – 65,000,000 tables / 517,000 databases • Teradata – 117 billion records – 23 TBs for one table • DB2 – 29,838,518,078 daily queries • SQL Server – 47,000,000,000,000 bytes – Largest table 34 billion records • Informix – 1,800,000,000 queries/day – 65,000,000 tables / 517,000 databases • Teradata – 117 billion records – 23 TBs for one table • DB2 – 29,838,518,078 daily queries Data Footprints 17Copyright 2019 by Data Blueprint Slide # Repeat 100s, thousands, millions, billions of times ... 18Copyright 2019 by Data Blueprint Slide #
  4. 4. Death by 1000 Cuts 19Copyright 2019 by Data Blueprint Slide # 20Copyright 2019 by Data Blueprint Slide # Garbage In ➜ Garbage Out! My most profound lesson! (so far) 21Copyright 2019 by Data Blueprint Slide # Perfect 
 Model Garbage 
 Data Garbage 
 Results Data Warehouse Machine Learning Business Intelligence Block ChainAIMDM Data Governance AnalyticsTechnology GI➜GO! 22Copyright 2019 by Data Blueprint Slide # Perfect 
 Model Garbage 
 Data Garbage 
 Results Data Warehouse Machine Learning Block Chain AI MDM Analytics Technology Data Governance GI➜GO! Business Intelligence 23Copyright 2019 by Data Blueprint Slide # Perfect 
 Model Quality 
 Data
 is
 founda- tional Garbage 
 Results Data Warehouse Machine Learning Block Chain AI MDM Analytics Technology Data Governance GI➜GO! Business Intelligence 24Copyright 2019 by Data Blueprint Slide # Perfect 
 Model Quality 
 Data
 is
 founda- tional Garbage 
 Results Data Warehouse Machine Learning Business Intelligence Block Chain AI MDM Analytics Technology Data Governance GI➜GO!
  5. 5. 25Copyright 2019 by Data Blueprint Slide # Perfect 
 Model Quality 
 Data
 is
 founda- tional Garbage 
 Results Data Warehouse Machine Learning Block Chain AI MDM Analytics Technology Data Governance GI➜GO! Business Intelligence 26Copyright 2019 by Data Blueprint Slide # Perfect 
 Model Quality 
 Data
 is
 founda- tional Good 
 Results Data Warehouse Machine Learning Business Intelligence Block Chain AI MDM Analytics Technology Data Governance Quality In ➜ Quality Out! Data Knowledge is insufficient and informal • Data management happens 'pretty well' at 
 the workgroup level – Defining characteristic of a workgroup – Without guidance, what are the chances that all 
 workgroups are pulling toward the same objectives? – Consider the time spent attempting informal practices • Data chaff becomes sand in the machinery – Preventing smooth interoperation and exchanges – Difficulties that have been hard to account for • Organizations and individuals lack – Skills – Knowledge (architecture) – Data Engineering (how) – Data Strategy (why) 27Copyright 2019 by Data Blueprint Slide # Standard data Data supply Data literacy Making a Better Data Sandwich 28Copyright 2019 by Data Blueprint Slide # Data literacy Standard data Data supply Making a Better Data Sandwich 29Copyright 2019 by Data Blueprint Slide # Standard data Data supply Data literacy Making a Better Data Sandwich 30Copyright 2019 by Data Blueprint Slide # Standard data Data supply Data literacy This cannot happen without engineering and architecture! Quality engineering/
 architecture work products 
 do not happen accidentally!
  6. 6. Making a Better Data Sandwich 31Copyright 2019 by Data Blueprint Slide # Standard data Data supply Data literacy This cannot happen without data engineering and architecture! Quality data engineering/
 architecture work products 
 do not happen accidentally! Our barn had to pass a foundation inspection 32Copyright 2019 by Data Blueprint Slide # Engineering Standards 33Copyright 2019 by Data Blueprint Slide # USS Midway & Pancakes 34Copyright 2019 by Data Blueprint Slide # • It is tall • It has a clutch • It was built in 1942 • It is cemented to the floor • It is still in regular use! Why is this an excellent engineering example? 35Copyright 2019 by Data Blueprint Slide # Data Quality Success Stories - Program Overview 1. Data quality must be understood as 
 an engineering challenge 2. Putting a price on data quality 3. DM BoK components compliment 
 each other well 4. Savings based stories 5. Innovation based stories 6. Non-monetary stories 7. Takeaways and Q&A Hidden Data Factories are expensive https://hbr.org/2016/09/bad-data-costs-the-u-s-3-trillion-per-year • Consider these two questions: – Were your systems explicitly designed to 
 be integrated or otherwise work together? – If not then what is the likelihood that they 
 will just happen to work well together? • Data must function at the most granular 
 interaction or it results in things that: – Take longer (end-of-day job runs 45 hours) – Cost more (the wrong assets are transferred) – Deliver less (features are not delivered) – Present greater risk (billing delayed 30 days, monthly) • 20-40% of IT budgets are spent evolving data: – Data migration (changing the location from one place to another) – Data conversion (changing it into another form, state, or product) – Data improvement (inspecting, manipulating it, preparing for subsequent use) 36Copyright 2019 by Data Blueprint Slide # "The choice of data structure and algorithm can make the difference between software running in a few seconds or many days."
 http://slideplayer.com/slide/7664141/
  7. 7. 
 
 
 
 
 DQ challenges are context specific!
 
 
 
 
 
 
 37Copyright 2019 by Data Blueprint Slide # 38Copyright 2019 by Data Blueprint Slide # Much more analysis is required before we can implement repeatable solutions to today's data quality challenges! TWITTER USERS SEND 473400TWEETS SKYPEUSERS MAKE 176220CALLS INSTAGRAM GIPHY USERS POST PHOTOSSPOTIFYSTREAMS OVER 750,000 SONGS TUMBLR USERS PUBLISH POSTS USERS WATCH VIDEOS SHIPS PACKAGES SNAPCHATTHEWEATHER CHANNEL NETFLIX USERS STREAM 97222HRS OF VIDEO VENMO PROCESSES $68493 PEER-TO-PEER TRANSACTIONS TINDER AMAZON USERS MATCH TIMES TEXTS SENTNEW COMMENTS RECEIVES USERS SHARE SNAPS YOUTUBE LINKEDINGAINS 120+NEW 2083333 , 4333560, , 1388889 ,79740, BITCOIN NEW FORECAST REQUESTS 1.25 ARE CREATED RECEIVES , 49380,AMERICANS USE OF INTERNET DATA 3138420, , GB,6940, 18055555,, 1111 UBERUSERS TAKE RIDES 1389, 1944, SERVES UP GIFS 12986111,, PROFESSIONALS GOOGLECONDUCTS SEARCHES 3877140, , ,, , , REDDIT MINUTE every DAY of the PRESENTED BY DOMO 2018 , 39Copyright 2019 by Data Blueprint Slide # https://www.domo.com/learn/data-never-sleeps-6 How much Data,
 by the minute! For the entirety of 2018, every minute of every day: • 18 million weather forecast requests • Netflix streams almost 100,000 hours of video • LinkedIn adds 120+ individuals • 1,300 Uber rides • (almost) a half million tweets • 7,000 Tinder matches • 1.25 new cryptocurrencies are created • ... Great inspiration towards valuation ... • How to Measure Anything: Finding the Value of 
 Intangibles in Business by Douglas Hubbard (ISBN: 0470539399) • Measurement is a reduction in uncertainty • Formalizing stuff forces clarity • Whatever your measurement problem is, – it's been done before • You have more data than you think • You need less data than you think • Getting data is more economical than you think • You probably need different data than you think • Special shout out to Chapter 7 – Measuring the value of additional information to a decision 40Copyright 2019 by Data Blueprint Slide # Sheena's in color Activity-Based Costing Kills Someone 41Copyright 2019 by Data Blueprint Slide # Enrico Fermi (Nobel Prize Physics 1938) 42Copyright 2019 by Data Blueprint Slide # • Tuners in Chicago ≈ Population/people per household times % households with tuned pianos times tunings per year divided by (tunings per tuner per day times workdays/year) • How many piano tuners in the city of Chicago? – Without using existing lists such as yellow pages, google ... – Current population of Chicago (3 million at the time) – Average number of people per household (2 or 3) – Share of households with regularly tuned pianos (1 in 3) – Required frequency of tuning (1/year) – How many pianos can a tuner tune daily? (4 or 5) – How many days/year are worked (250)
  8. 8. Monitization: Time & Leave Tracking At Least 300 employees are spending 15 minutes/week tracking leave/time 43Copyright 2019 by Data Blueprint Slide # Capture Cost of Labor/Category 44Copyright 2019 by Data Blueprint Slide # 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 Cost per timekeeper $12.31 $114.56 Total timekeeper cost $898.49 $5,727.89 Monthly cost $21,563.83 $137,469.40 Compute Labor Costs 45Copyright 2019 by Data Blueprint Slide # Annual Organizational Totals • $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 46Copyright 2019 by Data Blueprint Slide # 47Copyright 2019 by Data Blueprint Slide # Data Quality Success Stories - Program Overview 1. Data quality must be understood as 
 an engineering challenge 2. Putting a price on data quality 3. DM BoK components compliment 
 each other well 4. Savings based stories 5. Innovation based stories 6. Non-monetary stories 7. Takeaways and Q&A 48Copyright 2019 by Data Blueprint Slide # Data Quality Success Stories - Program Overview 1. Data quality must be understood as 
 an engineering challenge 2. Putting a price on data quality 3. DM BoK components compliment 
 each other well 4. Savings based stories 5. Innovation based stories 6. Non-monetary stories 7. Takeaways and Q&A
  9. 9. The Data Management 
 Body of 
 Knowledge 49Copyright 2019 by Data Blueprint Slide # Data 
 Management 
 Practice Areas fromTheDAMAGuidetotheDataManagementBodyofKnowledge©2009byDAMAInternational Overview: Data Quality Engineering 50Copyright 2019 by Data Blueprint Slide # Definitions • Quality Data – Fit for purpose meets the requirements of its authors, users, 
 and administrators (adapted from Martin Eppler) – Synonymous with information quality, since poor data quality 
 results in inaccurate information and poor business performance • Data Quality Management – Planning, implementation and control activities that apply quality 
 management techniques to measure, assess, improve, and 
 ensure data quality – Entails the "establishment and deployment of roles, responsibilities 
 concerning the acquisition, maintenance, dissemination, and 
 disposition of data" http://www2.sas.com/proceedings/sugi29/098-29.pdf ✓ Critical supporting process from change management ✓ Continuous process for defining acceptable levels of data quality to meet business needs and for ensuring that data quality meets these levels • Data Quality Engineering – Recognition that data quality solutions cannot not managed but must be engineered – Engineering is the application of scientific, economic, social, and practical knowledge in order to design, build, and maintain solutions to data quality challenges – Engineering concepts are generally not known and understood within IT or business! 51Copyright 2019 by Data Blueprint Slide # Spinach/Popeye story from http://it.toolbox.com/blogs/infosphere/spinach-how-a-data-quality-mistake-created-a-myth-and-a-cartoon-character-10166 Why isn't aren't my data problems solved by a data warehouse? 52Copyright 2019 by Data Blueprint Slide # Version 1 53Copyright 2019 by Data Blueprint Slide # Data Strategy Data Governance Data 
 Quality Improving operations in 3 data management practice areas BI Warehouse Version 2 54Copyright 2019 by Data Blueprint Slide # Data Strategy Data Governance BI Warehouse Metadata Improving operations in 3 data management practice areas
  10. 10. Version 3 55Copyright 2019 by Data Blueprint Slide # Data Strategy Data Governance BI/ Warehouse Reference & Master Data Perfecting operations in 3 data management practice areas 56Copyright 2019 by Data Blueprint Slide # Data Quality Success Stories - Program Overview 1. Data quality must be understood as 
 an engineering challenge 2. Putting a price on data quality 3. DM BoK components compliment 
 each other well 4. Savings based stories 5. Innovation based stories 6. Non-monetary stories 7. Takeaways and Q&A 
 Improve Operations Innovation Data quality focus should be sequenced 57Copyright 2019 by Data Blueprint Slide # 58Copyright 2019 by Data Blueprint Slide # 59Copyright 2019 by Data Blueprint Slide # Ubiquitous Mystery Object 60Copyright 2019 by Data Blueprint Slide #
  11. 11. Complex Data Quality Problems • Agency manages (4,000,000 data items) – Executive in charge requested 
 a conversion update – Was told verbally the conversion was "going well" – Demanded specifics • Question: "How many items did you attempt to convert?" • Answer: "100 items" • Question: "How many were actually converted?" • Answer: "5" • Problems – Not reporting the "right results" – These "problems" were discovered too late in the project – Unsophisticated contractor 61Copyright 2019 by Data Blueprint Slide # Improving Data Quality during System Migration • 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 62Copyright 2019 by Data Blueprint Slide # 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% Determining Diminishing Returns 63Copyright 2019 by Data Blueprint Slide # Before After Time needed to review all NSNs once over the life of the project: NSNs 2,000,000 Average time to review & cleanse (in minutes) 5 Total Time (in minutes) 10,000,000 Time available per resource over a one year period of time: Work weeks in a year 48 Work days in a week 5 Work hours in a day 7.5 Work minutes in a day 450 Total Work minutes/year 108,000 Person years required to cleanse each NSN once prior to migration: Minutes needed 10,000,000 Minutes available person/year 108,000 Total Person-Years 92.6 Resource Cost to cleanse NSN's prior to migration: Avg Salary for SME year (not including overhead) $60,000.00 Projected Years Required to Cleanse/Total DLA Person Year Saved 93 Total Cost to Cleanse/Total DLA Savings to Cleanse NSN's: $5.5 million Quantitative Benefits 64Copyright 2019 by Data Blueprint Slide # Time needed to review all NSNs once over the life of the project: NSNs 2,000,000 Average time to review & cleanse (in minutes) 5 Total Time (in minutes) 10,000,000 Time available per resource over a one year period of time: Work weeks in a year 48 Work days in a week 5 Work hours in a day 7.5 Work minutes in a day 450 Total Work minutes/year 108,000 Person years required to cleanse each NSN once prior to migration: Minutes needed 10,000,000 Minutes available person/year 108,000 Total Person-Years 92.6 Resource Cost to cleanse NSN's prior to migration: Avg Salary for SME year (not including overhead) $60,000.00 Projected Years Required to Cleanse/Total DLA Person Year Saved 93 Total Cost to Cleanse/Total DLA Savings to Cleanse NSN's: $5.5 million Quantitative Benefits 65Copyright 2019 by Data Blueprint Slide # Time needed to review all NSNs once over the life of the project: NSNs 150,000 Average time to review & cleanse (in minutes) 5 Total Time (in minutes) 750,000 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 750,000 Minutes available person/year 108,000 Total Person-Years 7 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 7 Total Cost to Cleanse/Total DLA Savings to Cleanse NSN's: $420,000 Time needed to review all NSNs once over the life of the project: NSNs 2,000,000 Average time to review & cleanse (in minutes) 5 Total Time (in minutes) 10,000,000 Time available per resource over a one year period of time: Work weeks in a year 48 Work days in a week 5 Work hours in a day 7.5 Work minutes in a day 450 Total Work minutes/year 108,000 Person years required to cleanse each NSN once prior to migration: Minutes needed 10,000,000 Minutes available person/year 108,000 Total Person-Years 92.6 Resource Cost to cleanse NSN's prior to migration: Avg Salary for SME year (not including overhead) $60,000.00 Projected Years Required to Cleanse/Total DLA Person Year Saved 93 Total Cost to Cleanse/Total DLA Savings to Cleanse NSN's: $5.5 million Quantitative Benefits 66Copyright 2019 by Data Blueprint Slide #
  12. 12. Year 2000 (or Y2K) Bug 67Copyright 2019 by Data Blueprint Slide # • Before the internet – Computing resources were expensive – It was worth the tradeoff to represent the year field using two digits – 1959 was represented to the computer as 59 – Subtracting 59 from 99 yields the correct answer 40 (for dates prior to 2000/01/01!) – No one expected those programs to still be in use – Documentation was poorly created/maintained • If all these fields were not expanded to four digits before 2000/01/01 then date calculations will not give correct results – Subtracting 59 from 00 yields the incorrect answer -41 – No one knew how long this would take or 
 cost–only when it must be completed! On the OFFICIAL Clock of the United States at 1 second BEFORE Midnight showed: December 31, 1999 11:59:59 One SECOND later the OFFICIAL Clock of the United States showed: January 1, 19100 00:00:01 • with a PhD in Chemical Engineering • have to know whether this product was
 Y2K compliant? Why should a knowledge worker 68Copyright 2019 by Data Blueprint Slide # International Chemical Company Engine Testing 69Copyright 2019 by Data Blueprint Slide # • $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! 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 70Copyright 2019 by Data Blueprint Slide # Data Integration Solution 71Copyright 2019 by Data Blueprint Slide # • 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 Lockheed Martin • 20 years of project email – Example from Doug Laney 72Copyright 2019 by Data Blueprint Slide #
  13. 13. Logistics Company • Fortune 450 • Room of 100 associates • Manually correcting every
 item on every customer invoice • Upon noting this to the 
 responsible manager - the reply was: – This is the best quarter – Of the best year – I've ever had – Perhaps I need 
 to double the 
 number in 
 that room? 73Copyright 2019 by Data Blueprint Slide # 74Copyright 2019 by Data Blueprint Slide # Data Quality Success Stories - Program Overview 1. Data quality must be understood as 
 an engineering challenge 2. Putting a price on data quality 3. DM BoK components compliment 
 each other well 4. Savings based stories 5. Innovation based stories 6. Non-monetary stories 7. Takeaways and Q&A US DoD Reverse Engineering Program Manager • "Your first project is to keep me from having to testify to a Congressional Hearing!" (Belkis Leon-Hong former ASD-C3I) • Problem: – 37 systems paid personnel within DoD – How many were needed? – How many potential losers? – What do you mean by employee? • Process modeling – Inconclusive results • Data reverse engineering - definitive – One legged engineer, 
 working in waist deep waters, 
 underneath rotating helicopter blades, 
 on overtime 75Copyright 2019 by Data Blueprint Slide # Reverse Engineering New Systems 76Copyright 2019 by Data Blueprint Slide # Reverse Engineering New Systems for Smooth Implementation. IEEE Software. March/April 1999 16(2):36-43 Platform: UniSys
 OS: OS
 1998 Age: 21 
 Data Structure: DMS (Network)
 Physical Records: 4,950,000
 Logical Records: 250,000
 Relationships: 62
 Entities: 57
 Attributes: 1478 Predicting Engineering Problem Characteristics New System Legacy System 
 #1: Payroll Legacy System 
 #2: Personnel Platform: Amdahl
 OS: MVS
 1998 Age: 15 
 Data Structure: VSAM/virtual 
 database tables
 Physical Records: 780,000
 Logical Records: 60,000
 Relationships: 64
 Entities: 4/350
 Attributes: 683 Characteristics Logical Physical
 Platform: WinTel Records: 250,000 600,000
 OS: Win'95 Relationships: 1,034 1,020
 1998 Age: new Entities: 1,600 2,706
 Data Structure: Client/Sever RDBMS Attributes: 15,000 7,073 77Copyright 2019 by Data Blueprint Slide # TheBudgetTrap(Parts1&2) 78Copyright 2019 by Data Blueprint Slide #
  14. 14. Actual Bid From Systems Integrator 79Copyright 2019 by Data Blueprint Slide # Extreme Data Engineers ... 2 person months = 40 person days 2,000 attributes mapped onto 15,000 2,000/40 person days = 500/person day
 or 500/8 hours = 62.5 attributes/hour and 15,000/40 person days = 375/person day
 or 375/8 hours = 46.875 attributes/hour Locate, identify, understand, map, transform, document 108 attributes/60 minutes 1.8 attributes/minute! 80Copyright 2019 by Data Blueprint Slide # What did Rolls Royce Learn • Old model – Sell jet engines • New model – Sell hours of thrust power – Power-by-the-hour – No payment for down time – Wing to wing – When was it invented? from Nascar? 81Copyright 2019 by Data Blueprint Slide # Fan Blade Sensor 82Copyright 2019 by Data Blueprint Slide # • 1 Sensor – Probabilistic (generalist) maintenance forecasts • 100 Sensors – Establish optimal monitoring targets – Finer tuned and safer maintenance – Mission Readiness ??? – Storage $$$ – Handling $$$ – Opportunity $$$ – Systemic $$$ – Maintenance $$$ – Total > $1.5 Billion 83Copyright 2019 by Data Blueprint Slide # Data Quality Success Stories - Program Overview 1. Data quality must be understood as 
 an engineering challenge 2. Putting a price on data quality 3. DM BoK components compliment 
 each other well 4. Savings based stories 5. Innovation based stories 6. Non-monetary stories 7. Takeaways and Q&A Armed Force Example • Lieutenant attempting to correct a 4 year underpayment 
 of his private's pay – Significant impact on moral – Immediate cash issues – Cost tens of man hours over months of time to resolve 84Copyright 2019 by Data Blueprint Slide # Nugee, R. and R. S. Seiner (2010, 6/1/2010). "TDAN.com Interview with Brigadier Richard Nugee – The British Army." 2013, from http://www.tdan.com/view-special- features/13897 and personal communications.
  15. 15. Friendly Fire deaths traced to Dead Battery • Date: Tue, 26 Mar 2002 10:47:52 -0500
 From: 
 Subject: Friendly Fire deaths traced to dead battery
 
 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 (reminiscent of the Mars Climate
 Orbiter slamming into the planet when ground crews confused English with
 metric); and using a device with such flaws in a combat situation 85Copyright 2019 by Data Blueprint Slide # Formalizing the 
 Role of U.S. Army 
 Data Governance 86Copyright 2019 by Data Blueprint Slide # 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 87Copyright 2019 by Data Blueprint Slide # 88Copyright 2019 by Data Blueprint Slide # Business Implications • 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 systems 89Copyright 2019 by Data Blueprint Slide # Best approaches combines manual and automation Humans Generally Better Machines Generally Better • Sense low level stimuli • Detect stimuli in noisy background • Recognize constant patterns in varying situations • Sense unusual and unexpected events • Remember principles and strategies • Retrieve pertinent details without a priori connection • Draw upon experience and adapt decision to situation • Select alternatives if original approach fails • Reason inductively; generalize from observations • Act in unanticipated emergencies and novel situations • Apply principles to solve varied problems • Make subjective evaluations • Develop new solutions • Concentrate on important tasks when overload occurs • Adapt physical response to changes in situation • Sense stimuli outside human's range • Count or measure physical quantities • Store quantities of coded information accurately • Monitor prespecified events, especially infrequent • Make rapid and consisted responses to input signals • Recall quantities of detailed information accurately • Retrieve pertinent detailed without a priori connection • Process quantitative data in prespecified ways • Perform repetitive preprogrammed actions reliably • Exert great, highly controlled physical force • Perform several activities simultaneously • Maintain operations under heavy operation load • Maintain performance over extended periods of time 90Copyright 2019 by Data Blueprint Slide #
  16. 16. 91Copyright 2019 by Data Blueprint Slide # Potential Data Sources 92Copyright 2019 by Data Blueprint Slide # 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 93Copyright 2019 by Data Blueprint Slide # 94Copyright 2019 by Data Blueprint Slide # 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 95Copyright 2019 by Data Blueprint Slide # Senior Army Official • Room full of Stewards • A very heavy dose of management support • Advised the group of his opinion on the matter • Any questions as to future direction – "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 96Copyright 2019 by Data Blueprint Slide #
  17. 17. 97Copyright 2019 by Data Blueprint Slide # Managing Data with Guidance? • Federal employees • 44 users from whitehouse.gov • Thousands of military and 
 government e-mails • Canadian citizens • One-fifth of Quebec 98Copyright 2019 by Data Blueprint Slide # 
 Ashley
 Madison
 37,000,000 
 
 25,000,000
 OPM 
 
 
 70,000,000
 Target 99Copyright 2019 by Data Blueprint Slide # Target Corporation's Database Contents 100Copyright 2019 by Data Blueprint Slide # • Your age • Marital status • Part of town you live in • How long it takes you to drive to work • Estimated salary • If you have recently moved • Credit cards carried in your wallet • What websites you visit • Your ethnicity • Your job history • The magazines you read • Work commute • Sexual preferences • If you’ve ever declared bankruptcy or got divorced • The year you bought (or lost) your house • Where you went to school(s) • What kinds of topics you talk about online • Whether you prefer certain brands of coffee, paper towels, cereal or applesauce • Your political leanings, reading habits, charitable giving and • The number of cars you own 101Copyright 2019 by Data Blueprint Slide # https://oversight.house.gov/report/opm-data-breach-government-jeopardized-national-security-generation/ How the Government Jeopardized Our National Security for More than a Generation • Preventable • Leadership failed – To heed repeated recommendations – To sufficiently respond to growing threats of sophisticated cyber attacks, and – To prioritize resources for cybersecurity • 2014 data breaches were likely connected and possibly coordinated to the 2015 data breach • OPM misled the public on the extent of the damage of the breach and made false statements to Congress Key Findings 102Copyright 2019 by Data Blueprint Slide # Data Quality Success Stories - Program Overview 1. Data quality must be understood as 
 an engineering challenge 2. Putting a price on data quality 3. DM BoK components compliment 
 each other well 4. Savings based stories 5. Innovation based stories 6. Non-monetary stories 7. Takeaways and Q&A
  18. 18. • Quality data requires a context specific definition • Most business problems have data challenges (hidden data factories) at their root • All advanced data practices depend on quality data • AI/ML are suffering from lack of training data • Few 'easy' fixes exist • Data quality engineering works well when combined with other DM BoK 'pie wedges' • Successful data quality stories demonstrate – Tangible ongoing savings – Innovative data uses – Outcomes more important than money Take Aways 103Copyright 2019 by Data Blueprint Slide # + = 104Copyright 2019 by Data Blueprint Slide # Questions? 10124 W. Broad Street, Suite C Glen Allen, Virginia 23060 804.521.4056 Copyright 2019 by Data Blueprint Slide # 105

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