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    Monetizing data management 09162010 Monetizing data management 09162010 Document Transcript

    • 10/4/2010 Monetizing Data Management Dr. Peter Aiken CEO and Founding Director, Data Blueprint President, DAMA International Associate Professor of Information Systems, Virginia Commonwealth University Abstract: Monetizing Data Management Organizations have lost millions due to poor data management practices, but remain unaware of the root causes of their losses. Unless IT professionals can monetize these lost opportunities and their related costs, gaining executive-level approval for basic data management investments will continue to be difficult. This sets up an unfortunate loop: executive management is focused on fixing symptoms, but cannot address the underlying problems. This talk illustrates how to identify specific costs of poor data management practices using examples from HR, Financial, Supply Chain, and Compliance. As organizations understand poor data management practices as the root cause of many of their problems, they will be more than willing to make the required investments in our profession. PAGE 210/4/2010 © Copyright this and previous years by Data Blueprint - all rights reserved! 1
    • 10/4/2010 Speaker Bio Dr. Peter Aiken is an award-winning, internationally recognized thought leader in the areas of organizational data management, architecture, and engineering. As a practicing data manager, consultant, author and researcher, he has been actively performing and studying these areas for more than 25 years. He has held leadership positions with the US Department of Defense and consulted with more than 50 organizations in 17 different counties. Dr. Aiken is the current president of DAMA International, Associate Professor in Virginia Commonwealth University’s Information Systems Department and the Founding Director of Data Blueprint, an IT consulting and data management firm based in Richmond, Virginia. PAGE 310/4/2010 © Copyright this and previous years by Data Blueprint - all rights reserved! Monetizing - from Wikipedia • Monetization is the process of converting or establishing something into legal tender. • It usually refers to the printing of banknotes by central banks, but things such as gold, diamonds, emerald and art can also be monetized. • Even intrinsically worthless items can be made into money, as long as they are difficult to make or acquire. PAGE 410/4/2010 © Copyright this and previous years by Data Blueprint - all rights reserved! 2
    • 10/4/2010 Root Cause Analysis • Symptom of the problem – The weed – Above the surface – Obvious • The underlying Cause – The root – Below the surface – Not obvious • Poor Information Management Practices – Did not hire Adastra! PAGE 510/4/2010 © Copyright this and previous years by Data Blueprint - all rights reserved! Expanding DM Scope DataBase Administration (DBA) ≈ 1950-1970 Data Enterprise Data Administration Data Management Database design Database operation (DA) Administration (DM) ≈ 1970- (EDA) ≈ 2000- 1990 ≈ 1990-2000 Data requirements analysis Data modeling Organization-wide DM coordination Organization-wide data integration Data stewardship, Data use Data Governance, Data Quality, Data Security, Analytics, Data Compliance, Data Mashups, Business Rules (more ...) PAGE 610/4/2010 © Copyright this and previous years by Data Blueprint - all rights reserved! 3
    • 10/4/2010 Data Management Involvement Data Warehousing XML Data Quality Customer Relationship Management Master Data Management Customer Data Integration Enterprise Resource Planning Enterprise Application Integration Value Title Initiative Leader Initiative Involvement Not Involved PAGE 710/4/2010 © Copyright this and previous years by Data Blueprint - all rights reserved!Niccolo Machiavelli(1469-1527) 1469- He who doesn’t lay his foundations before hand, may by great abilities do so afterward, although with great trouble to the architect and danger to the building. Machiavelli, Niccolo. The Prince. 19 Mar. 2004 http://pd.sparknotes.com/philosophy/prince PAGE 810/4/2010 © Copyright this and previous years by Data Blueprint - all rights reserved! 4
    • 10/4/2010Look Familiar? PAGE 910/4/2010 © Copyright this and previous years by Data Blueprint - all rights reserved! A Model Specifying Relationships Among Important Terms Wisdom & knowledge are often used synonymously Intelligence Data Information Use Data Data Request Data Data Fact Meaning Data Data1. 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 USES. [Built on definition by Dan Appleton 1983] PAGE 1010/4/2010 © Copyright this and previous years by Data Blueprint - all rights reserved! 5
    • 10/4/2010 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." 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. Friendly Fire The air controller had recorded the correct value in the GPS receiver when the battery died. Upon replacing the battery, he called in the deaths traced 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. to Dead 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 PAGE 1110/4/2010 © Copyright this and previous years by Data Blueprint - all rights reserved! Academic Research Findings A 10% improvement in data usability on productivity (increases sales per employee by 14.4% or $55,900)Measuring the Business Impacts of Effective Data by Anitesh Barua, Deepa Mani, Rajiv Mukherjee PAGE 1210/4/2010 © Copyright this and previous years by Data Blueprint - all rights reserved! 6
    • 10/4/2010 Academic Research Findings Projected increase in sales (in $M) due to 10% improvement in data usability on productivity (sales per employee) Measuring the Business Impacts of Effective Data by Anitesh Barua, Deepa Mani, Rajiv Mukherjee PAGE 1310/4/2010 © Copyright this and previous years by Data Blueprint - all rights reserved! Academic Research Findings Projected impact of a 10% improvement in data quality and sales mobility on Return on Equity Measuring the Business Impacts of Effective Data by Anitesh Barua, Deepa Mani, Rajiv Mukherjee PAGE 1410/4/2010 © Copyright this and previous years by Data Blueprint - all rights reserved! 7
    • 10/4/2010 Academic Research Findings Projected Impact of a 10% increase in intelligence and accessibility of data on Return on AssetsMeasuring the Business Impacts of Effective Data by Anitesh Barua, Deepa Mani, Rajiv Mukherjee PAGE 1510/4/2010 © Copyright this and previous years by Data Blueprint - all rights reserved! Monetization: Time & Leave Tracking At Least 300 employees are spending 15 minutes/week tracking leave/time PAGE 1610/4/2010 © Copyright this and previous years by Data Blueprint - all rights reserved! 8
    • 10/4/2010 Capture Cost of Labor/Category PAGE 1710/4/2010 © Copyright this and previous years by Data Blueprint - all rights reserved! Computer Labor as Overhead 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 timekeeper $12.31 $114.56 Total semi-monthly timekeeper cost $898.49 $5,727.89 Annual cost $21,563.83 $137,469.40 PAGE 1810/4/2010 © Copyright this and previous years by Data Blueprint - all rights reserved! 9
    • 10/4/2010 Annual Organization TotalsRange $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/annuallyAwareness of the cost of things considered overhead! PAGE 1910/4/2010 © Copyright this and previous years by Data Blueprint - all rights reserved!Challenge • "Green screen" legacy system to be replaced with Windows Icons Mice Pointers (WIMP) interface; and • Major changes to operational processes – 1 screen to 23 screens • Management didnt think workforce could adjust to simultaneous changes – Question: "How big a change will it be to replace all instances of person_identifier with social_security_number?" • Answer: – (from "big" consultants) "Not a very big change." PAGE 2010/4/2010 © Copyright this and previous years by Data Blueprint - all rights reserved! 10
    • 10/4/2010 Reverse Engineering PeopleSoft implementation Component representation metadata integration Metadata Uses • System Structure Installed PeopleSoft Metadata -• Queries to System requirements PeopleSoft workflow metadata verification and Internals system change TheMAT analysis system structure metadata • Data Metadata - data• PeopleSoft post conversion, data external derivation security,and user RDBM metadata analysis training Tables and integration • Workflow Metadata - business practice• Printed analysis and PeopleSoft realignment Datamodel PAGE 21 data metadata10/4/2010 © Copyright this and previous years by Data Blueprint - all rights reserved! 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 Name Business Process Component Step PAGE 2210/4/2010 © Copyright this and previous years by Data Blueprint - all rights reserved! 11
    • 10/4/2010Example Query Outputs PAGE 2310/4/2010 - datablueprint.com © Copyright this and previous years by Data Blueprint - all rights reserved! 9/8/2010 © Copyright this and previous years by Data Blueprint - all rights reserved!Data processes homepages menugroups (39) (7) (8)Metadata (41) (8)Structure (182) (86) components stepnames menunames (180) (822) (86) (949) (847) (281) panels menuitems menubars (1421) (1149) (31) (1916) (1259) (25906) (5873) (264) fields records parents (7073) (2706) (264) (708) (647) (647) (347) reports children (347) (647) PAGE 2410/4/2010 © Copyright this and previous years by Data Blueprint - all rights reserved! 12
    • 10/4/2010 Resolution Quantity System Time to make Labor Hours Component 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 PAGE 2510/4/2010 © Copyright this and previous years by Data Blueprint - all rights reserved! An Iterative Approach to MDM Structuring Unmatched Unmatched Ignorable Ignorable Avg Items Matched Items Items Items ExtractedRev (% Total) NSNs (% Total) Items Per Item (% Total) Items# Matched Extracted 1 329948 31.47% 14034 1.34% N/A N/A N/A 264703 2 222474 21.22% 73069 6.97% N/A N/A N/A 286675 3 216552 20.66% 78520 7.49% N/A N/A N/A 287196 4 340514 32.48% 125708 11.99% 582101 1.1000221 55.53% 640324 … … … … … … … … … 14 94542 9.02% 237113 22.62% 716668 1.1142914 68.36% 798577 15 94929 9.06% 237118 22.62% 716276 1.1139281 68.33% 797880 16 99890 9.53% 237128 22.62% 711305 1.1153007 67.85% 793319 17 99591 9.50% 237128 22.62% 711604 1.1154392 67.88% 793751 18 78213 7.46% 237130 22.62% 732980 1.2072812 69.92% 884913 PAGE 2610/4/2010 © Copyright this and previous years by Data Blueprint - all rights reserved! 13
    • 10/4/2010 Quantitative BenefitsTime needed to review all NSNs once over the life of the project:NSNs 2,000,000Average time to review & cleanse (in minutes) 5Total Time (in minutes) 10,000,000Time available per resource over a one year period of time:Work weeks in a year 48Work days in a week 5Work hours in a day 7.5Work minutes in a day 450Total Work minutes/year 108,000Person years required to cleanse each NSN once prior to migration:Minutes needed 10,000,000Minutes available person/year 108,000Total Person-Years 92.6Resource Cost to cleanse NSNs prior to migration:Avg Salary for SME year (not including overhead) $60,000.00Projected Years Required to Cleanse/Total DLA Person Year Saved 93Total Cost to Cleanse/Total DLA Savings to Cleanse NSNs: $5.5 million PAGE 2710/4/2010 © Copyright this and previous years by Data Blueprint - all rights reserved! 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 Stammers an milestone has been explanation of "bad" missed data July Slows then stops Removes project team Defendant invoice payments Files arbitration request as governed by contract with Defendant PAGE 2810/4/2010 © Copyright this and previous years by Data Blueprint - all rights reserved! 14
    • 10/4/2010 FBI & Canadian Social Security Gender Codes 1. Male 2. Female 3. Formerly male now female If column 1 in source = "m" 4. Formerly female now male •then set value 5. Uncertain of target data to "male" 6. Wont tell •else set value of target 7. Doesnt know data to 8. Male soon to be female "female" 9. Female soon to be male Hypothesized extensions contributed by a Chicago DAMA Member 10.Psychologically female, biologically male 11.Psychologically male, biologically female 12.Both soon to be female 13.Both soon to be male PAGE 2910/4/2010 © Copyright this and previous years by Data Blueprint - all rights reserved!220-220-Process_Emp_DataMore Examples - State An exclamation point indicates ! if $state = or $state = that anything to the right will not ! move State to $blank_field be executed (“commented out”) ! move Y to $blank_state ! do 221-Blank-Field-Error ! end-if To protect data quality if $state = the program should use the 221-Blank-Field-Error move to $state Procedure end-if If there is no state, then this code makes the state a space PAGE 3010/4/2010 © Copyright this and previous years by Data Blueprint - all rights reserved! 15
    • 10/4/2010 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 move N to $found_in_PS !DAR 01/14/04 The exclamation point 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 NID.NATIONAL_ID no check is made for a move Y to $found_in_PS !DAR 01/14/04 duplicate SSN/National move &NID.EMPLID to $ps_emplid ID FROM PS_PERS_NID NID !WHERE NID.NATIONAL_ID = $ps_ssn WHERE NID.AJ_APPL_ID = $applicant_id END-SELECT Legacy systems business if $found_in_PS = N do 231-Check-XXX-for-Empl !DAR 01/14/04 !DAR 01/14/04 rules allowed employees to if $found_on_XXX = N !DAR 01/14/04 have more than one add 1 to #last_emplid let $last_emplid = to_char(#last_emplid) AJ_APPL_ID. let $last_emplid = lpad($last_emplid,6,0) let $ps_emplid = AJ || $last_emplid end-if end-if !DAR 01/14/04 PAGE 31 End-Procedure 230-Assign-PS-Emplid10/4/2010 © Copyright this and previous years by Data Blueprint - all rights reserved! PAGE 3210/4/2010 © Copyright this and previous years by Data Blueprint - all rights reserved! 16
    • 10/4/2010 Identified & Quantified Risks PAGE 3310/4/2010 © Copyright this and previous years by Data Blueprint - all rights reserved! 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 Technical Lead Consultant 40 by Monday, November 3, HRMS Consultant 40 20XX so that resources can be Financials Technical Consultant 40 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 PAGE 34 Total 284010/4/2010 © Copyright this and previous years by Data Blueprint - all rights reserved! 17
    • 10/4/2010 Professional & Workmanlike Manner Defendant warrants that the services it provides hereunder will be performed in a professional and workmanlike manner in accordance with industry standards. PAGE 3510/4/2010 © Copyright this and previous years by Data Blueprint - all rights reserved! 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. PAGE 3610/4/2010 © Copyright this and previous years by Data Blueprint - all rights reserved! 18
    • 10/4/2010 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 PAGE 3710/4/2010 © Copyright this and previous years by Data Blueprint - all rights reserved! IEEE Code of Ethics We, the members of the IEEE, in recognition of the importance of our technologies in affecting the quality of life throughout the world, and in accepting a personal obligation to our profession, its members and the communities we serve, do hereby commit ourselves to the highest ethical and professional conduct and agree: To accept responsibility in making engineering decisions consistent with the safety, health and welfare of the public, and to disclose promptly factors that might endanger the public or the environment; To avoid real or perceived conflicts of interest whenever possible, and to disclose them to affected parties when they do exist; To be honest and realistic in stating claims or estimates based on available data; To reject bribery in all its forms; To improve the understanding of technology, its appropriate application, and potential consequences; To maintain and improve our technical competence and to undertake technological tasks for others only if qualified by training or experience, or after full disclosure of pertinent limitations; To seek, accept, and offer honest criticism of technical work, to acknowledge and correct errors, and to credit properly the contributions of others; To treat fairly all persons regardless of such factors as race, religion, gender, disability, age, or national origin; To avoid injuring others, their property, reputation, or employment by false or malicious action; To assist colleagues and co-workers in their professional development and to support them in following this code of ethics. [Approved by the IEEE Board of Directors, August 1990] PAGE 38 http://www.ieee.org/portal/site/mainsite/menuitem.818c0c39e85ef176fb2275875bac26c8/index.jsp?&p Name=corp_level1&path=about/whatis&file=code.xml&xsl=generic.xsl accessed on 4/10/04.10/4/2010 © Copyright this and previous years by Data Blueprint - all rights reserved! 9/8/2010 © Copyright this and previous years by Data Blueprint - all rights reserved! 19
    • 10/4/2010 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. PAGE 39 http://www.acm.org/constitution/code.html10/4/2010 © Copyright this and previous years by Data Blueprint - all rights reserved! 9/8/2010 © Copyright this and previous years by Data Blueprint - all rights reserved! Outcome Sep 8, 2010 PAGE 4010/4/2010 © Copyright this and previous years by Data Blueprint - all rights reserved! 20
    • 10/4/2010http://peteraiken.net Contact Information: Peter Aiken, Ph.D. Department of Information Systems School of Business Virginia Commonwealth University 1015 Floyd Avenue - Room 4170 Richmond, Virginia 23284-4000 Data Blueprint Maggie L. Walker Business & Technology Center 501 East Franklin Street Richmond, VA 23219 804.521.4056 http://datablueprint.com office :+1.804.883.759 cell:+1.804.382.5957 e-mail:peter@datablueprint.com PAGE 41 http://peteraiken.net10/4/2010 © Copyright this and previous years by Data Blueprint - all rights reserved! Questions? PAGE 42 21