Your SlideShare is downloading. ×
0
Data-Ed: Show Me the Money: The Business Value of Data and ROI
Data-Ed: Show Me the Money: The Business Value of Data and ROI
Data-Ed: Show Me the Money: The Business Value of Data and ROI
Data-Ed: Show Me the Money: The Business Value of Data and ROI
Data-Ed: Show Me the Money: The Business Value of Data and ROI
Data-Ed: Show Me the Money: The Business Value of Data and ROI
Data-Ed: Show Me the Money: The Business Value of Data and ROI
Data-Ed: Show Me the Money: The Business Value of Data and ROI
Data-Ed: Show Me the Money: The Business Value of Data and ROI
Data-Ed: Show Me the Money: The Business Value of Data and ROI
Data-Ed: Show Me the Money: The Business Value of Data and ROI
Data-Ed: Show Me the Money: The Business Value of Data and ROI
Data-Ed: Show Me the Money: The Business Value of Data and ROI
Data-Ed: Show Me the Money: The Business Value of Data and ROI
Data-Ed: Show Me the Money: The Business Value of Data and ROI
Data-Ed: Show Me the Money: The Business Value of Data and ROI
Data-Ed: Show Me the Money: The Business Value of Data and ROI
Data-Ed: Show Me the Money: The Business Value of Data and ROI
Data-Ed: Show Me the Money: The Business Value of Data and ROI
Data-Ed: Show Me the Money: The Business Value of Data and ROI
Data-Ed: Show Me the Money: The Business Value of Data and ROI
Data-Ed: Show Me the Money: The Business Value of Data and ROI
Data-Ed: Show Me the Money: The Business Value of Data and ROI
Data-Ed: Show Me the Money: The Business Value of Data and ROI
Data-Ed: Show Me the Money: The Business Value of Data and ROI
Data-Ed: Show Me the Money: The Business Value of Data and ROI
Data-Ed: Show Me the Money: The Business Value of Data and ROI
Data-Ed: Show Me the Money: The Business Value of Data and ROI
Data-Ed: Show Me the Money: The Business Value of Data and ROI
Data-Ed: Show Me the Money: The Business Value of Data and ROI
Data-Ed: Show Me the Money: The Business Value of Data and ROI
Data-Ed: Show Me the Money: The Business Value of Data and ROI
Data-Ed: Show Me the Money: The Business Value of Data and ROI
Data-Ed: Show Me the Money: The Business Value of Data and ROI
Data-Ed: Show Me the Money: The Business Value of Data and ROI
Data-Ed: Show Me the Money: The Business Value of Data and ROI
Data-Ed: Show Me the Money: The Business Value of Data and ROI
Data-Ed: Show Me the Money: The Business Value of Data and ROI
Data-Ed: Show Me the Money: The Business Value of Data and ROI
Data-Ed: Show Me the Money: The Business Value of Data and ROI
Data-Ed: Show Me the Money: The Business Value of Data and ROI
Data-Ed: Show Me the Money: The Business Value of Data and ROI
Data-Ed: Show Me the Money: The Business Value of Data and ROI
Data-Ed: Show Me the Money: The Business Value of Data and ROI
Data-Ed: Show Me the Money: The Business Value of Data and ROI
Data-Ed: Show Me the Money: The Business Value of Data and ROI
Data-Ed: Show Me the Money: The Business Value of Data and ROI
Data-Ed: Show Me the Money: The Business Value of Data and ROI
Data-Ed: Show Me the Money: The Business Value of Data and ROI
Data-Ed: Show Me the Money: The Business Value of Data and ROI
Data-Ed: Show Me the Money: The Business Value of Data and ROI
Data-Ed: Show Me the Money: The Business Value of Data and ROI
Data-Ed: Show Me the Money: The Business Value of Data and ROI
Data-Ed: Show Me the Money: The Business Value of Data and ROI
Data-Ed: Show Me the Money: The Business Value of Data and ROI
Data-Ed: Show Me the Money: The Business Value of Data and ROI
Data-Ed: Show Me the Money: The Business Value of Data and ROI
Data-Ed: Show Me the Money: The Business Value of Data and ROI
Data-Ed: Show Me the Money: The Business Value of Data and ROI
Data-Ed: Show Me the Money: The Business Value of Data and ROI
Data-Ed: Show Me the Money: The Business Value of Data and ROI
Data-Ed: Show Me the Money: The Business Value of Data and ROI
Data-Ed: Show Me the Money: The Business Value of Data and ROI
Data-Ed: Show Me the Money: The Business Value of Data and ROI
Data-Ed: Show Me the Money: The Business Value of Data and ROI
Data-Ed: Show Me the Money: The Business Value of Data and ROI
Data-Ed: Show Me the Money: The Business Value of Data and ROI
Data-Ed: Show Me the Money: The Business Value of Data and ROI
Upcoming SlideShare
Loading in...5
×

Thanks for flagging this SlideShare!

Oops! An error has occurred.

×
Saving this for later? Get the SlideShare app to save on your phone or tablet. Read anywhere, anytime – even offline.
Text the download link to your phone
Standard text messaging rates apply

Data-Ed: Show Me the Money: The Business Value of Data and ROI

1,463

Published on

This webinar originally aired on Tuesday, December 11, 2012. It is part of Data Blueprint's ongoing webinar series on data management with Dr. Aiken. …

This webinar originally aired on Tuesday, December 11, 2012. It is part of Data Blueprint's ongoing webinar series on data management with Dr. Aiken.

Sign up for future sessions at http://www.datablueprint.com/webinar-schedule.

Abstract:
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 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. Join us and learn how you can apply similar tactics at your organization to justify funding and gain management approval.

Published in: Technology
0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total Views
1,463
On Slideshare
0
From Embeds
0
Number of Embeds
1
Actions
Shares
0
Downloads
8
Comments
0
Likes
0
Embeds 0
No embeds

Report content
Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
No notes for slide

Transcript

  • 1. TITLE Welcome! Monetizing Data Management: Business Value of Data and ROI Date: December 11, 2012 Time: 2:00 PM ET Presenter: Dr. Peter Aiken PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 112/07/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • 2. TITLE Get Social With Us! Live Twitter Feed Like Us on Facebook Join the Group Join the conversation! www.facebook.com/ Data Management & Follow us: datablueprint Business Intelligence @datablueprint Post questions and Ask questions, gain insights comments and collaborate with fellow @paiken Find industry news, insightful data management Ask questions and submit content professionals your comments: #dataed and event updates. PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 212/07/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • 3. TITLE Meet Your Presenter: Dr. Peter Aiken • Internationally recognized thought-leader in the data management field with more than 30 years of experience • Recipient of the 2010 International Stevens Award • Founding Director of Data Blueprint (http://datablueprint.com) • Associate Professor of Information Systems at Virginia Commonwealth University (http://vcu.edu) • President of DAMA International (http://dama.org) • DoD Computer Scientist, Reverse Engineering Program Manager/ Office of the Chief Information Officer • Visiting Scientist, Software Engineering Institute/Carnegie Mellon University • 7 books and dozens of articles • Experienced w/ 500+ data management practices in 20 countries PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 312/07/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • 4. Monetizing Data Management: Business Value of Data and ROI Professional Development: Business Value of Data and ROIDATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION
  • 5. TITLE Outline 1. Data Management Overview 2. Root Cause Analysis 3. Ineffective Data Management Investments 4. Success Stories & Monetization Examples 5. Guiding Principles 6. Take Aways and Q&A Tweeting now: #dataed PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 512/07/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • 6. TITLE The DAMA Guide to the Data Management Body of Knowledge Published by DAMA International • The professional association for Data Managers (40 chapters worldwide) DMBoK organized around • Primary data management functions focused around data delivery to the organization • Organized around several environmental elements Data Management Functions PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 612/07/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • 7. TITLE The DAMA Guide to the Data Management Body of Knowledge Amazon: http:// www.amazon.com/ DAMA-Guide- Management- Knowledge-DAMA- DMBOK/dp/ 0977140083 Or enter the terms "dama dm bok" at the Amazon search engine Environmental Elements PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 712/07/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • 8. TITLE What is the CDMP? • Certified Data Management Professional • DAMA International and ICCP • Membership in a distinct group made up of your fellow professionals • Recognition for your specialized knowledge in a choice of 17 specialty areas • Series of 3 exams • For more information, please visit: – http://www.dama.org/i4a/pages/ index.cfm?pageid=3399 – http://iccp.org/certification/ designations/cdmp PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 812/07/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • 9. TITLE Data Management PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 912/07/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • 10. TITLE Data Management Manage data coherently. Data Program Coordination Share data across boundaries. Organizational Data Integration Data Stewardship Data Development Assign responsibilities for data. Engineer data delivery systems. Data Support Operations Maintain data availability. PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 1012/07/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • 11. TITLE Outline 1. Data Management Overview 2. Ineffective Data Management Investments 3. Root Cause Analysis 4. Success Stories & Monetization Examples 5. Guiding Principles 6. Take Aways and Q&A Tweeting now: #dataed PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 1112/07/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • 12. TITLE IT Project Failure Rates Recent IT project failure rates statistics can be summarized as follows: – Carr 1994 • 16% of IT Projects completed on time, within budget, with full functionality – OASIG Study (1995) • 7 out of 10 IT projects "fail" in some respect – The Chaos Report (1995) • 75% blew their schedules by 30% or more • 31% of projects will be canceled before they ever get completed • 53% of projects will cost over 189% of their original estimates • 16% for projects are completed on-time and on-budget – KPMG Canada Survey (1997) • 61% of IT projects were deemed to have failed – Conference Board Survey (2001) • Only 1 in 3 large IT project customers were very “satisfied” PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 1212/07/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • 13. TITLE More IT Project Failure Rates Recent IT project failure rates statistics can be summarized as follows: – Robbins-Gioia Survey (2001) • 51% of respondents viewed their large IT implementation project as unsuccessful – MacDonalds Innovate (2002) • Automate fast food network from fry temperature to # of burgers sold-$180M USD write-off – Ford Everest (2004) • Replacing internal purchasing systems-$200 million over budget – FBI (2005) • Blew $170M USD on suspected terrorist database-"start over from scratch" http://www.it-cortex.com/stat_failure_rate.htm; (accessed 9/14/02); New York Times 1/22/05 PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 1312/07/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • 14. 60% IT Project Failure Rates (moving average) 53% 53% 51% 49% 45% 46% 44% 40% 34% 33% 30% 31% 32% 29% 28% 28% 27% 26% 24% 23% 18% 15% 16% 15% Failed Challenged Succeeded 0% 1994 Click 1993 Master text styles 2000 to edit 1998 2002 2004 2009Source: Standish Chaos Reports as reported at: http://www.galorath.com/wp/software-project-failure-costs-billions-better-estimation-planning-can-help.php DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION
  • 15. TITLE % of DM Organizations labeled “successful” 0.45 0 Successful Partial Success Dont know/too soon to tell Unsuccessful Does not exist 1981 2007 PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 1512/07/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • 16. TITLE DM Origins – Which arrives first: DM or DBMS? • A key indicator of organizational awareness • 75% reacting instead of anticipating • Best practices are obvious PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 1612/07/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • 17. TITLE Data Management Involvement PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 1712/07/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • 18. TITLE Expanding DM Scope PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 1812/07/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • 19. TITLE $0 $125,000 $250,000 $375,000 $500,000 • Assessed 1200 migration projects! Median Project Expense – Surveyed only experienced migration specialists who have done at least four migration projects Median Project Cost • The median project costs over 10 times the amount planned! • Biggest Challenges: Bad Data; Missing Data; Duplicate Data • The survey did not consider projects that were cancelled largely due to data migration difficulties • "… problems are encountered rather than discovered" Joseph R. Hudicka "Why ETL and Data Migration Projects Fail" Oracle Developers Technical Users Group Journal June 2005 pp. 29-31 PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 1912/07/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • 20. TITLE Organizations Surveyed • Results from more than 500 International Organizations organizations 10%Local Government 4% • 32% government State Government Agencies • Appropriate 17% public company representation • Enough data to Federal Government demonstrate 11% European organization DM Public Companies practices are 58% generally more mature PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 2012/07/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • 21. TITLE Polling Question #1 What percentage of Data Management investments achieve tangible returns? a. 30% b. 10% c. 65% PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 2112/07/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • 22. TITLE Largely Ineffective EIM Investments • Approximately, 10% percent of organizations achieve parity and Investment <= Return (potential positive 10% returns) on their DM investments. • Only 30% of DM investments achieve Return ≈ 0 tangible returns at all. 70% Investment > Return 20% • Seventy percent of organizations have very small or no tangible return on their DM investments. PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 2212/07/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • 23. TITLE Outline 1. Data Management Overview 2. Ineffective Data Management Investments 3. Root Cause Analysis 4. Success Stories & Monetization Examples 5. Guiding Principles 6. Take Aways and Q&A Tweeting now: #dataed PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 2312/07/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • 24. Root Cause Analysis TITLE • Symptom of the problem – The weed – Above the surface – Obvious • The underlying Cause – The root – Below the surface – Not obvious • Poor Information Management Practices PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 2412/07/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • 25. Ishikawa Fishbone Diagrams TITLE • Why is infant mortality so high? • Why are so many organizational technology – Malnourished mothers experiences so poor? • Why are mothers malnourished? – Misunderstanding of datas role in IT – Substandard biology educations in high school • Why do so few understand datas role in IT? • Why do are biology programs substandard? – Little, if any, focus on enterprise-wide data – Poor education of high school biology teachers use in the educational system • Why do we have poor biology teacher education? – Biology profession unaware of consequences • Why is the educational system not addressing this gap? – Lack of recognition by the system • Why has the system not yet been made aware of this deficiency? – Lack of understanding at the C-level of these issues • Why do they not understand? – Little, if any, focus on enterprise-wide data use in the educational system Asking "why" repeatedly! PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 2512/07/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • 26. TITLE Toyota versus Detroit Engine Mounting Detroit • 3 different bolts • 3 different wrenches • 3 different bolt inventories Toyota • Same bolts used for all three assemblies • 1 bolt inventory • 1 type of wrench PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 2612/07/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • 27. TITLE Academic Research Findings 0% 12.500% 25.000% 37.500% 50.000% Retail 49.00% Consulting 39.00% Air Transportation 21.00% Food Products 20.00% Construction 20.00% Steel A 10% improvement in 20.00% Automobile data usability on Publishing 19.00% productivity 18.00% (increased sales per Industrial Instruments employee by 14.4% or Telecommunications 18.00% $55,900) 17.00% PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINTImpacts of Effective Data by Anitesh Barua,, Deepa Mani,, Rajiv Mukherjee Measuring the Business 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 2712/07/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • 28. TITLE Academic Research Findings TITLE Projected impact of a 10% improvement in data quality and sales mobility on Return on Equity PRODUCED BY Impacts of Effective Data by Anitesh Barua,, Deepa Mani,, Rajiv Mukherjee Measuring the Business PRODUCED BY CLASSIFICATION DATE CLASSIFICATION* DATE SLIDE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION EDUCATION 28 3012/07/12 12/7/12 © Copyright this and previous years byby Data Blueprint allall rights reserved! © Copyright this and previous years Data Blueprint - - rights reserved!
  • 29. Academic Research Findings TITLE TITLE Projected Impact of a 10% increase in intelligence and accessibility of data on Return on Assets Measuring the Business Impacts of Effective Data by Anitesh Barua,, Deepa Mani,, Rajiv Mukherjee PRODUCED BY CLASSIFICATION DATE SLIDE PRODUCED BY CLASSIFICATION* DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION EDUCATION 29 3112/07/1212/7/12 © Copyright this and previous years by Data Blueprint - all rights reserved! © Copyright this and previous years by Data Blueprint - all rights reserved!
  • 30. TITLE Outline 1. Data Management Overview 2. Ineffective Data Management Investments 3. Root Cause Analysis 4. Success Stories & Monetization Examples 5. Guiding Principles 6. Take Aways and Q&A Tweeting now: #dataed PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 3012/07/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • 31. TITLE TITLE Monitization: Time & Leave Tracking At Least 300 employees are spending 15 minutes/week tracking leave/time PRODUCED BY CLASSIFICATION* DATE SLIDE PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-CDataW. BROAD reserved! DATA BLUEPRINT 10124-C BROAD ST, GLEN ALLEN, VA 23060 W.Blueprint - all rights ST, GLEN ALLEN, VA 23060 EDUCATION EDUCATION 32 12/7/12 © Copyright this and previous years by 3112/07/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • 32. TITLE Capture Cost of Labor/Category PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 32 - datablueprint.com12/07/12 © Copyright this and previous years by Data Blueprint - all rights reserved! 1/4/2011 © Copyright this and previous years by Data Blueprint - all rights reserved! 33
  • 33. Computer Labor as Overhead TITLE Routine Data Entry 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 $12.31 $114.56 timekeeper Total semi-monthly $898.49 $5,727.89 timekeeper cost Annual cost $21,563.83 $137,469.40 PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 3312/07/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • 34. TITLE Annual Organizational Totals • 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 PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 3412/07/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • 35. TITLE ERP Implementation Success On time, within budget, as planned 10% 350% 300% 250% 200% 230% Overrun 55% 178% Cancelled 35% 150% 100% 59% 50% 100% 100% 41% 0% Cost Schedule Planned Functionality • Most ERP implementations today result in cost and schedule overruns; courtesy of the Standish Group PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 3512/07/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • 36. TITLE Predicting Engineering Problem Characteristics Platform: Amdahl OS: MVS Platform: UniSys 1998 Age: 15 OS: OS Data Structure: VSAM/virtual 1998 Age: 21 Legacy System Legacy System database tables Data Structure: DMS (Network) #1: Payroll #2: Personnel Physical Records: 780,000 Physical Records: 4,950,000 Logical Records: 60,000 Logical Records: 250,000 Relationships: 64 Relationships: 62 Entities: 4/350 Entities: 57 Attributes: 683 Attributes: 1478 Characteristics Logical Physical Platform: WinTel Records: 250,000 600,000 OS: Win95 Relationships: 1,034 1,020 1998 Age: new Entities: 1,600 2,706 Data Structure: Client/Sever RDBMS Attributes: 15,000 7,073 New System PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 3612/07/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • 37. TITLE "Extreme" Data Engineering • 2 person months = 40 person days • 2,000 attributes mapped onto 15,000 • 2,000/40 person days = 50 attributes per person day or 50 attributes/8 hour = 6.25 attributes/hour and • 15,000/40 person days = 375 attributes per person day or 375 attributes/8 hours = 46.875 attributes/hour • Locate, identify, understand, map, transform, document, QA at a rate of - • 52 attributes every 60 minutes or .86 attributes/minute! CLASSIFICATION DATE SLIDE PRODUCED BY DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 3712/07/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • 38. TITLE PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 3812/07/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • 39. TITLE Reverse Engineering PeopleSoft implementation Component representation metadata integration Metadata Uses • System Structure • Queries to Installed Metadata - PeopleSoft PeopleSoft System requirements Internals workflow metadata verification and system change TheMAT analysis • PeopleSoft system structure metadata • Data Metadata - data external post conversion, data RDBM derivation security, and user metadata training Tables analysis and integration • Workflow Metadata - • Printed business practice PeopleSoft analysis and Datamodel realignment data metadata PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 3912/07/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • 40. TITLE PeopleSoft Process Metadata Home Page Name Home Page (relates to one or more) Business Process Business Process Name Name (relates to one or more) Business Process Business Process Component Name Component (relates to one or more) Business Process Component Step Business Process Component Step Name PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 4012/07/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • 41. TITLE Example Query Outputs PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 41 - datablueprint.com12/07/12 © Copyright this and previous years by Data Blueprint - all rights reserved! 1/4/2011 © Copyright this and previous years by Data Blueprint - all rights reserved! 42
  • 42. TITLE PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 4212/07/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • 43. Resolution TITLE Quantity System Time to Labor Hours Component make change 1,400 Panels 15 minutes 350 1,500 Tables 15 minutes 375 984 Business 15 minutes 246 process component steps Total 971 X $200/hour $194,200 X 5 upgrades $1,000,000 PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 4312/07/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • 44. TITLE 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 PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 4412/07/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • 45. TITLE Determining Diminishing Returns Unmatched Ignorable Items 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% PRODUCED BY 18 7.46% DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 22.62% 69.92% CLASSIFICATION DATE EDUCATION SLIDE 4512/07/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • 46. Quantitative Benefits TITLE 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 NSNs prior to migration: Avg Salary for SME year (not including overhead) $60,000.00 Projected Years Required to Cleanse/Total DLA Person Year 93 Saved Total Cost to Cleanse/Total DLA Savings to Cleanse NSNs: CLASSIFICATION DATE $5.5SLIDE million PRODUCED BY DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 4612/07/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • 47. TITLE PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION12/07/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • 48. TITLE PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 4812/07/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • 49. Seven Sisters from British Telecom TITLE Thanks to Dave Evans PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 4912/07/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • 50. TITLE Date: Tue, 26 Mar 2002 10:47:52 -0500 From: Jamie McCarthy <jamie@mccarthy.vg> Subject: Friendly Fire deaths traced to dead battery In one of the more horrifying incidents Ive 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." Friendly Fire deaths 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 traced to Dead 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 Battery 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 PRODUCED BY CLASSIFICATION DATE CLASSIFICATION* DATE SLIDE SLIDE PRODUCED BY DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 DATACopyright this and previous years by DataW. BROAD reserved! BLUEPRINT 10124-C Blueprint - all rights ST, GLEN ALLEN, VA 23060 EDUCATION EDUCATION 5012/07/12 ©12/7/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • 51. TITLE Messy Sequencing Towards Arbitration Plaintiff Defendant (Company X) (Company Y) April Requests a Responds indicating recommendation from "Preferred Specialist" ERP Vendor status July Contracts Defendant to Begins implement ERP and implementation convert legacy data January Realizes a key milestone Stammers an has been missed explanation of "bad" data July Slows then stops Removes project team Defendant invoice payments Files arbitration request as governed by contract with Defendant PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 5112/07/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • 52. TITLE Expert Reports Expert Report Ours provided evidence that : 1. Company Ys 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 Xs 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 PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 5212/07/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • 53. TITLE TITLE AJHR0213_CAN_UPDATE.SQR !************************************************************************ ! Procedure Name: 230-Assign-PS-Emplid ! ! Description : This procedure generates a PeopleSoft Employee ID ! (Emplid) by incrementing the last Emplid processed by 1 The defendant knew to ! First it checks if the applicant/employee exists on ! the PeopleSoft database using the SSN. prevent duplicate SSNs ! !************************************************************************ Begin-Procedure 230-Assign-PS-Emplid The exclamation point move N to $found_in_PS !DAR 01/14/04 move N to $found_on_XXX !DAR 01/14/04 prevents this line from BEGIN-SELECT -DbDSN=HR83PRD;UID=PS_DEV;PWD=psdevelopment looking for duplicates, so NID.EMPLID no check is made for a NID.NATIONAL_ID duplicate SSN/National move Y to $found_in_PS move &NID.EMPLID to $ps_emplid !DAR 01/14/04 ID FROM PS_PERS_NID NID !WHERE NID.NATIONAL_ID = $ps_ssn WHERE NID.AJ_APPL_ID = $applicant_id END-SELECT Legacy systems business rules allowed employees to if $found_in_PS = N !DAR 01/14/04 do 231-Check-XXX-for-Empl !DAR 01/14/04 have more than one if $found_on_XXX = N add 1 to #last_emplid !DAR 01/14/04 AJ_APPL_ID. 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 72 End-Procedure 230-Assign-PS-Emplid PRODUCED BY CLASSIFICATION DATE CLASSIFICATION* DATE SLIDE SLIDE PRODUCED BY DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION EDUCATION 5312/07/12 © Copyright this and previous years by Data Blueprint - all rights reserved!12/7/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • 54. TITLE PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 5412/07/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • 55. TITLE PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 5512/07/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • 56. TITLE 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 Tasks Hours "The go-live date may need to New Year Conversion 120 Tax and payroll balance conversion 120 be extended due to certain General Ledger conversion 80 critical path deliverables not Total 320 being met. This extension will require additional tasks and Resource Hours G/L Consultant 40 resources. The decision of Project Manager 40 whether or not to extend the Recievables Consultant 40 go-live date should be made HRMS Technical Consultant 40 by Monday, November 3, Technical Lead Consultant 40 20XX so that resources can HRMS Consultant 40 Financials Technical Consultant 40 be allocated to the additional Total 280 tasks." Delay Weekly Resources Weeks Tasks Cumulative January (5 weeks) 280 5 320 1720 February (4 weeks) 280 4 1120 Total 2840 PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 5612/07/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • 57. TITLE Outline 1. Data Management Overview 2. Ineffective Data Management Investments 3. Root Cause Analysis 4. Success Stories & Monetization Examples 5. Guiding Principles 6. Take Aways and Q&A Tweeting now: #dataed PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 5712/07/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • 58. TITLE The Defenses "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. PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 5812/07/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • 59. TITLE Published Industry Standards Guidance Examples from the: • IEEE (365,000 members) – Institute of Electrical and Electronic Engineers – 150 countries, 40 percent outside the United States – 128 transactions, journals and magazines – 300 conferences • ACM (80,000+ members) – Association of Computing Machinery – 100 conferences annually • ICCP (50,000+ members) – Institute for Certification of Computing Professionals • DAMA International (3,500+ members) – Data Management Association – Largest Data/Metadata conference PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 5912/07/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • 60. TITLE PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 6012/07/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • 61. TITLE ACM Code of Ethics and Professional Conduct 1. General Moral Imperatives. 1.2 Avoid harm to others • Well-intended actions, including those that accomplish assigned duties, may lead to harm unexpectedly. In such an event the responsible person or persons are obligated to undo or mitigate the negative consequences as much as possible. One way to avoid unintentional harms is to carefully consider potential impacts on all those affected by decisions made during design and implementation. • To minimize the possibility of indirectly harming others, computing professionals must minimize malfunctions by following generally accepted standards for system design and testing. Furthermore, it is often necessary to assess the social consequences of systems to project the likelihood of any serious harm to others. If system features are misrepresented to users, coworkers, or supervisors, the individual computing professional is responsible for any resulting injury. PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 6112/07/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • 62. TITLE Outcome Jan 4, 2011 PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 6212/07/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • 63. TITLE Polling Question #2 Which is not a reason why data scientist add business value? a. Act as a data-to-business translator b. They work side-by-side with the IT department c. Conduct problem solving using a data-driven approach PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 6312/07/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • 64. TITLE 3 Ways Data Scientists Add Business Value 1. Refine target audiences. The more information that companies gather and analyze about their customers, the more they learn about their behaviors, needs, and preferences. This information also provides greater knowledge about the lifecycle stage that a particular set of customers is at (e.g. dual- income with children nearing college age). This type of information can help companies identify the most likely customers for certain products and services. Data analysts are masters at distilling this type of information. 2. Conduct problem solving using a quantifiable, data-driven approach. For years, executives have made million-dollar decisions based on gut instinct. But that’s no longer necessary with the volume of data that’s available from so many channels and market sources for decision makers to pore over. Not only can data analysts help senior leaders make the right decisions based on facts, they can also provide impartial, data-led guidance for critical decisions when the top brass are deadlocked on the right path to take. 3. Acting as a data-to-business translator. Many companies struggle with communicating and interpreting the results from analytics efforts. Data analysts can fill a critical role here by helping senior executives make sense of the data that’s being presented to them as well as by helping them understand how the information can be applied to various areas of the business. PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 6412/07/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • 65. TITLE Outline 1. Data Management Overview 2. Ineffective Data Management Investments 3. Root Cause Analysis 4. Success Stories & Monetization Examples 5. Guiding Principles 6. Take Aways and Q&A Tweeting now: #dataed PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 6512/07/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • 66. TITLE Data Management: Why is it Important to Your Organization? • Why is it important? – Concretizing • State Agency Time & Leave Tracking – $10 million USD annually • ERP Implementation $1 million USD on a large project • Data Warehouse Quality Analysis $5 billion USD US DoD (prevention) • MDM British Telecom rollout – £ 250 (small investment) • Non-Monetized Example – Different measures • ERP Implementation Legal Case $ 5,355,450 CAN damages/penalties PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 6612/07/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • 67. TITLE Questions? + = It’s your turn! Use the chat feature or Twitter (#dataed) to submit your questions to Peter now. PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 6712/07/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
  • 68. TITLE Upcoming Events January Webinar: Unlocking Business Value through Data Modeling and Data Architecture (Part I of II) 2013 @ 2:00 PM – 3:30 PM ET (11:00 AM-12:30 PM PT) February: Unlocking Business Value through Data Modeling and Data Architecture (Part II of II) 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: PRODUCED BY CLASSIFICATION DATE SLIDE DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 6812/07/12 © Copyright this and previous years by Data Blueprint - all rights reserved!

×