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

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Failure to successfully monetize data management investments sets up an unfortunate loop of fixing symptoms without addressing the underlying problems. As organizations begin to understand poor data ...

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

Check out more of our webinars: http://www.datablueprint.com/resource-center/webinar-schedule/

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Data-Ed: Show Me the Money: Monetizing Data Management Data-Ed: Show Me the Money: Monetizing Data Management Presentation Transcript

  • Show Me The Money: Monetizing Data Management Failure to successfully monetize data management investments sets up an unfortunate loop of fixing symptoms without addressing the underlying problems. As organizations begin to understand poor data management practices as the root causes of many of their business problems, they become more willing to make the required investments in our profession. This presentation uses specific examples to illustrate the costs of poor data management and how it impacts business objectives. Join us and learn how you can better align your data management projects with business objectives to justify funding and gain management approval. Date: Time: Presenter: October 8, 2013 2:00 PM ET/11:00 AM PT Peter Aiken, Ph.D. MONETIZING DATA MANAGEMENT Unlocking the Value in Your Organization’s Most Important Asset. PETER AIKEN WITH JUANITA BILLINGS FOREWORD BY JOHN BOTTEGA 1 Copyright 2013 by Data Blueprint
  • Executive Editor at DATAVERSITY.net Shannon Kempe 2 Copyright 2013 by Data Blueprint
  • Commonly Asked Questions 1) Will I get copies of the slides after the event? 1) Is this being recorded so I can view it afterwards? 3 Copyright 2013 by Data Blueprint
  • Get Social With Us! Live Twitter Feed Like Us on Facebook Join the Group Join the conversation! www.facebook.com/ datablueprint Data Management & Business Intelligence Follow us: @datablueprint @paiken Ask questions and submit your comments: #dataed Post questions and comments Ask questions, gain insights and collaborate with fellow data management Find industry news, insightful professionals content and event updates. 4 Copyright 2013 by Data Blueprint
  • Peter Aiken, PhD • • • • • • • • 25+ years of experience in data management Multiple international awards & recognition Founder, Data Blueprint (datablueprint.com) Associate Professor of IS, VCU (vcu.edu) President, DAMA International (dama.org) 8 books and dozens of articles Experienced w/ 500+ data management practices in 20 countries Multi-year immersions with organizations as diverse as the US DoD, Nokia, Deutsche Bank, Wells Fargo, and the Commonwealth of Virginia 5 Copyright 2013 by Data Blueprint 2
  • MONETIZING DATA MANAGEMENT Show Me The Money Unlocking the Value in Your Organization’s Most Important Asset. Monetizing Data Management Presented by Peter Aiken, Ph.D. PETER AIKEN WITH JUANITA BILLINGS FOREWORD BY JOHN BOTTEGA 10124 W. Broad Street, Suite C Glen Allen, Virginia 23060 804.521.4056
  • Outline 1. Data Management Overview 2. Book Motivations & Survey Results 3. Leveraging Data 4. Monetary ROI (6 cases) 5. Non-Monetary ROI (2 cases) 6. Legal Considerations 7. Take Aways and Q&A Tweeting now: #dataed 7 Copyright 2013 by Data Blueprint
  • Outline 1. Data Management Overview 2. Book Motivations & Survey Results 3. Leveraging Data 4. Monetary ROI (6 cases) 5. Non-Monetary ROI (2 cases) 6. Legal Considerations 7. Take Aways and Q&A 8 Copyright 2013 by Data Blueprint
  • Five Integrated DM Practice Areas Data management processes and infrastructure Implementation Organizational Strategies Data Program Coordination Guidance Goals Organizational Data Integration Combining multiple assets to produce extra value Organizational-entity subject area data integration Integrated Models Achieve sharing of data within a business area Data Stewardship Standard Data Application Models & Designs Provide reliable data access Direction Data Support Operations Feedback Leverage data in organizational activities Data Development Business Data Data Asset Use Business Value 9 Copyright 2013 by Data Blueprint
  • Five Integrated DM Practice Areas Manage data coherently. Data Program Coordination Share data across boundaries. Organizational Data Integration Data Development Data Stewardship Assign responsibilities for data. Engineer data delivery systems. Data Support Operations Maintain data availability. 10 Copyright 2013 by Data Blueprint
  • Hierarchy of Data Management Practices (after Maslow) • • 5 Data management practices areas / data management basics ... Advanced Data Practices ... are necessary but insufficient • MDM prerequisites to • Mining • Big Data organizational data • Analytics leveraging • Warehousing applications that is • SOA self actualizing data or advanced data Basic Data Management Practices practices – – – – – Data Program Management Organizational Data Integration Data Stewardship Data Development Data Support Operations http://3.bp.blogspot.com/-ptl-9mAieuQ/T-idBt1YFmI/AAAAAAAABgw/Ib-nVkMmMEQ/s1600/maslows_hierarchy_of_needs.png Copyright 2013 by Data Blueprint
  • Outline 1. Data Management Overview 2. Book Motivations & Survey Results 3. Leveraging Data 4. Monetary ROI (6 cases) 5. Non-Monetary ROI (2 cases) 6. Legal Considerations 7. Take Aways and Q&A 12 Copyright 2013 by Data Blueprint
  • Outline 1. Data Management Overview 2. Book Motivations & Survey Results 3. Leveraging Data 4. Monetary ROI (6 cases) 5. Non-Monetary ROI (2 cases) 6. Legal Considerations 7. Take Aways and Q&A 13 Copyright 2013 by Data Blueprint
  • Motivation ... • • • Task: helping our community better articulate the importance of what we do Until we can meaningfully communicate in monetary or other terms equally important to the C-suite, we will continue to struggle to articulate the value of its role Today’s business executives – – – • MONETIZING DATA MANAGEMENT Smart, talented and experienced experts Executive decision-makers being far removed and insufficiently data knowledgeable Too many decisions about data have been poor. Four Parts – – – – Unique perspective to the practice of leveraging data 11 cases where leveraging data has produced positive financial results Five instance non-monetary outcomes of critical important to the C-suite Interaction of data management practices and both IT projects and legal responsibilities Unlocking the Value in Your Organization’s Most Important Asset. PETER AIKEN WITH JUANITA BILLINGS FOREWORD BY JOHN BOTTEGA 14 Copyright 2013 by Data Blueprint
  • 2013 Monetizing Data Management Survey Results 15 Copyright 2013 by Data Blueprint
  • 2013 Monetizing Data Management Survey Results • Soon to be released: white paper & survey results 16 Copyright 2013 by Data Blueprint
  • Outline 1. Data Management Overview 2. Book Motivations & Survey Results 3. Leveraging Data 4. Monetary ROI (6 cases) 5. Non-Monetary ROI (2 cases) 6. Legal Considerations 7. Take Aways and Q&A 17 Copyright 2013 by Data Blueprint
  • Outline 1. Data Management Overview 2. Book Motivations & Survey Results 3. Leveraging Data 4. Monetary ROI (6 cases) 5. Non-Monetary ROI (2 cases) 6. Legal Considerations 7. Take Aways and Q&A 18 Copyright 2013 by Data Blueprint
  • Strategic Information Use: Prerequisites Wisdom & knowledge are often used synonymously Intelligence Data Information Strategic Use Data Request Data Data Data Meaning Fact Data 1. 2. 3. 4. 5. 6. Data Each FACT combines with one or more MEANINGS. Each specific FACT and MEANING combination is referred to as a DATUM. An INFORMATION is one or more DATA that are returned in response to a specific REQUEST INFORMATION REUSE is enabled when one FACT is combined with more than one MEANING. INTELLIGENCE is INFORMATION associated with its STRATEGIC USES. DATA/INFORMATION must formally arranged into an ARCHITECTURE. [Built on definitions from Dan Appleton 1983] 19 Copyright 2013 by Data Blueprint
  • Leverage is an Engineering Concept • Using proper engineering techniques, a human can lift a bulk that is weighs much more than the human 20 Copyright 2013 by Data Blueprint
  • Data Leverage is an Engineering Concept Process Organizational Data Managers Organizational Data People Less Data ROT -> Technologies • Note: Reducing ROT increases data leverage 21 Copyright 2013 by Data Blueprint
  • Why Is Data Management Important? • Too much data leads directly to wasted productivity – Eighty percent (80%) of organizational data is redundant, obsolete or trivial (ROT) • Underutilized data leads directly to poorly leveraged organizational resources – – – – Manpower – costs associated with labor resources and market share Money – costs associated with management of financial resources Methods – costs associated with operational processes and product delivery Machines – costs associated with hardware, software applications and data to enhance production capability 22 Copyright 2013 by Data Blueprint
  • Incorrect Educational Focus • Building new systems – – – 80% of IT costs are spent rebuilding and evolving existing systems and only 20% of costs are spent building and acquiring new systems Putting fresh graduates on new projects makes this proposition more ridiculous Only the most experienced professionals should be allowed to participate in new systems development. • Who is responsible for managing data assets? – – Business thinks IT is taking care of it - it is called IT after all? IT thinks if you can sign on to the system their job is complete • System development practices – – Data evolution is separate from, external to and must precede system development life cycle activities! Data is not a project - it has no distinct beginning and end 23 Copyright 2013 by Data Blueprint
  • Evolving Data is Different than Creating New Systems Common Organizational Data Future State (and corresponding data needs requirements) Evolve (Version +1) Data evolution is separate from, external to, and precedes system development life cycle activities! Systems Development Activities Create New Organizational Capabilities 24 Copyright 2013 by Data Blueprint
  • Application-Centric Development • In support of strategy, organizations develop specific goals/objectives • The goals/objectives drive the development of specific systems/applications • Development of systems/applications leads to network/infrastructure requirements • Data/information are typically considered after the systems/applications and network/ infrastructure have been articulated • Problems with this approach: – Ensures data is formed to the applications and not around the organizational-wide information requirements – Process are narrowly formed around applications – Very little data reuse is possible Strategy Goals/ Objectives Systems/ Applications Network/ Infrastructure Data/ Information Original articulation from Doug Bagley @ Walmart 25 Copyright 2013 by Data Blueprint
  • Typical System Evolution Payroll Data (database) Finance Application (3rd GL, batch system, no source) Payroll Application (3rd GL) Finance Data (indexed) Marketing Data (external database) Marketing Application (4rd GL, query facilities, no reporting, very large) Personnel Data (database) R&D Data (raw) Personnel App. (20 years old, un-normalized data) R& D Applications (researcher supported, no documentation) Mfg. Data (home grown database) Mfg. Applications (contractor supported) 26 Copyright 2013 by Data Blueprint
  • Data-Centric Development • • • • • In support of strategy, the organization develops specific goals/objectives The goals/objectives drive the development of specific data/information assets with an eye to organization-wide usage Network/infrastructure components are developed to support organization-wide use of data Development of systems/applications is derived from the data/network architecture Advantages of this approach: – Data/information assets are developed from an organization-wide perspective – Systems support organizational data needs and compliment organizational process flows – Maximum data/information reuse Strategy Goals/ Objectives Data/ Information Network/ Infrastructure Systems/ Applications Original articulation from Doug Bagley @ Walmart 27 Copyright 2013 by Data Blueprint
  • Polling Question #1 • Who or what department(s) makes the decision on investing in data management initiatives? A) IT B) Supported business area C) IT and the supported business area together D) Office of Chief Data Officer or Enterprise Data Office/Equivalent 28 Copyright 2013 by Data Blueprint
  • Outline 1. Data Management Overview 2. Book Motivations & Survey Results 3. Leveraging Data 4. Monetary ROI (6 cases) 5. Non-Monetary ROI (2 cases) 6. Legal Considerations 7. Take Aways and Q&A 29 Copyright 2013 by Data Blueprint
  • Outline 1. Data Management Overview 2. Book Motivations & Survey Results 3. Leveraging Data 4. Monetary ROI (6 cases) 5. Non-Monetary ROI (2 cases) 6. Legal Considerations 7. Take Aways and Q&A 30 Copyright 2013 by Data Blueprint
  • Monitization: Time & Leave Tracking At Least 300 employees are spending 15 minutes/week tracking leave/time 31 Copyright 2013 by Data Blueprint
  • Capture Cost of Labor/Category 32 Copyright 2013 by Data Blueprint
  • Compute Labor Costs District-L (as an example) Employees Leave Tracking Time Accounting 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 $898.49 $5,727.89 $21,563.83 $137,469.40 Total semi-monthly timekeeper cost Annual cost 33 Copyright 2013 by Data Blueprint
  • 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 34 Copyright 2013 by Data Blueprint
  • International Chemical Company Engine Testing • $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! 35 Copyright 2013 by Data Blueprint
  • Overview of Existing Data Management Process 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 36 33 Copyright 2013 by Data Blueprint
  • Data Integration Solution • Integrated the existing systems to easily search on and find similar or identical tests • Results: – Reduced expenses – Improved competitive edge and customer service – Time savings and improve operational capabilities • According to our client’s internal business case development, they expect to realize a $25 million gain each year thanks to this data integration 37 Copyright 2013 by Data Blueprint
  • Vocabulary is Important-Tank, Tanks, Tankers, Tanked 38 Copyright 2013 by Data Blueprint
  • How one inventory item proliferates data throughout the chain 555  Subassemblies  &  subcomponents System 1: 18,214 Total items 75 Attributes/ item 1,366,050 Total attributes 17,659  Repair  parts  or  Consumables System  2 47  Total  items 15+  A>ributes/item 720  Total  a>ributes System  3 16,594  Total  items 73  A>ributes/item 1,211,362  Total  a>ributes System  4 8,535  Total  items 16    A>ributes/item 136,560  Total  a>ributes System  5 15,959    Total  items 22    A>ributes/item 351,098  Total  a>ributes Total  for  the  five  systems  show  above: 59,350  Items 179  Unique  a>ributes 3,065,790  values Copyright 2013 by Data Blueprint 39
  • Business Implications • National Stock Number (NSN) Discrepancies – – • Serial Number Duplication – – • 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 If multiple items are assigned the same serial number in RTLS, the traceability of those items is severely impacted Approximately $531 million of SAC 3 items have duplicated serial numbers On-Hand Quantity Discrepancies – – If the LUAF O/H QTY and number of items serialized in RTLS conflict, there can be no clear answer as to how many items a unit actually has on-hand Approximately $5 billion of equipment does not tie out between the LUAF & RTLS Copyright 2013 by Data Blueprint
  • 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 41 Copyright 2013 by Data Blueprint
  • Determining Diminishing Returns Week # 1 Unmatched Items (% Total) 31.47% Ignorable Items (% Total) 1.34% Items Matched (% Total) N/A 2 21.22% 6.97% N/A 3 20.66% 7.49% N/A 4 32.48% 11.99% 55.53% … … … … 14 9.02% 22.62% 68.36% 15 9.06% 22.62% 68.33% 16 9.53% 22.62% 67.85% 17 9.50% 22.62% 67.88% 18 7.46% 22.62% 69.92% 42 Copyright 2013 by Data Blueprint
  • Quantitative Benefits Time needed to review all NSNs once over the life of the project: NSNs Average time to review & cleanse (in minutes) Total Time (in minutes) 2,000,000 5 10,000,000 Time available per resource over a one year period of time: Work weeks in a year Work days in a week Work hours in a day Work minutes in a day Total Work minutes/year 48 5 7.5 450 108,000 Person years required to cleanse each NSN once prior to migration: Minutes needed Minutes available person/year Total Person-Years 10,000,000 108,000 92.6 Resource Cost to cleanse NSN's prior to migration: Avg Salary for SME year (not including overhead) Projected Years Required to Cleanse/Total DLA Person Year Saved Total Cost to Cleanse/Total DLA Savings to Cleanse NSN's: $60,000.00 93 $5.5 million 43 Copyright 2013 by Data Blueprint
  • Seven Sisters (from British Telecom) 44 Copyright 2013 by Data Blueprint Thanks to Dave Evans
  • Polling Question #2 • Is it hard to obtain funding for your data management projects? A) Yes, because it is hard to show value B) Yes, because we have not aligned with the business objectives C) Yes, because no precedent has been set D) No, because we can clearly demonstrate value 45 Copyright 2013 by Data Blueprint
  • Outline 1. Data Management Overview 2. Book Motivations & Survey Results 3. Leveraging Data 4. Monetary ROI (6 cases) 5. Non-Monetary ROI (2 cases) 6. Legal Considerations 7. Take Aways and Q&A 46 Copyright 2013 by Data Blueprint
  • Outline 1. Data Management Overview 2. Book Motivations & Survey Results 3. Leveraging Data 4. Monetary ROI (6 cases) 5. Non-Monetary ROI (2 cases) 6. Legal Considerations 7. Take Aways and Q&A 47 Copyright 2013 by Data Blueprint
  • In one of the more horrifying incidents I've read about, U.S. soldiers and allies were killed in December 2001 because of a stunningly poor design of a GPS receiver, plus "human error." http://www.washingtonpost.com/wp-dyn/articles/A8853-2002Mar23.html A U.S. Special Forces air controller was calling in GPS positioning from some sort of battery-powered device. He "had used the GPS receiver to calculate the latitude and longitude of the Taliban position in minutes and seconds for an airstrike by a Navy F/A-18." According to the *Post* story, the bomber crew "required" a "second calculation in 'degree decimals'" -- why the crew did not have equipment to perform the minutes-seconds conversion themselves is not explained. The air controller had recorded the correct value in the GPS receiver when the battery died. Upon replacing the battery, he called in the degree-decimal position the unit was showing -- without realizing that the unit is set up to reset to its *own* position when the battery is replaced. The 2,000-pound bomb landed on his position, killing three Special Forces soldiers and injuring 20 others. If the information in this story is accurate, the RISKS involve replacing memory settings with an apparently-valid default value instead of blinking 0 or some other obviously-wrong display; not having a backup battery to hold values in memory during battery replacement; not equipping users to translate one coordinate system to another; and using a device with such flaws in a combat situation Friendly Fire deaths traced to Dead Battery 48 Copyright 2013 by Data Blueprint
  • Suicide Mitigation 49 Copyright 2013 by Data Blueprint
  • Data Suicide Mitigation Mapping Deploy ments Soldier Work History Legal Issues Mental illness DMSS DMDC G1 Abuse Suicide Analysis FAP CID MDR Data objects complete? All sources identified? Best source for each object? How reconcile differences between sources? 12 Copyright 2013 by Data Blueprint 50
  • Senior Army Official • A very heavy dose of management support • Any questions as to future data ownership, "they should make an appointment to speak directly with me!" • Empower the team – The conversation turned from "can this be done?" to "how are we going to accomplish this?" – Mistakes along the way would be tolerated – Implement a workable solution in prototype form 51 Copyright 2013 by Data Blueprint
  • Communication Patterns Source: The Challenge and the Promise: Strengthening the Force, Preventing Suicide and Saving Lives - The Final Report of the Department of Defense Task Force on the Prevention of Suicide by Members of the Armed Forces - August 2010 52 Copyright 2013 by Data Blueprint
  • Polling Question #3 • What percentage of your data projects are successful? A) All B) 25% C) 75% D) none 53 Copyright 2013 by Data Blueprint
  • Outline 1. Data Management Overview 2. Book Motivations & Survey Results 3. Leveraging Data 4. Monetary ROI (6 cases) 5. Non-Monetary ROI (2 cases) 6. Legal Considerations 7. Take Aways and Q&A 54 Copyright 2013 by Data Blueprint
  • Outline 1. Data Management Overview 2. Book Motivations & Survey Results 3. Leveraging Data 4. Monetary ROI (6 cases) 5. Non-Monetary ROI (2 cases) 6. Legal Considerations 7. Take Aways and Q&A 55 Copyright 2013 by Data Blueprint
  • Messy Sequencing Towards Arbitration Plaintiff (Company X) Defendant (Company Y) April Requests a recommendation from ERP Vendor Responds indicating "Preferred Specialist" status July Contracts Defendant to implement ERP and convert legacy data Begins implementation Realizes a key milestone has been missed Stammers an explanation of "bad" data Slows then stops Defendant invoice payments Removes project team January July Files arbitration request as governed by contract with Defendant 56 Copyright 2013 by Data Blueprint
  • • • • • • • Points of Contention Who owned the risks? Who was the project manager? Was the data of poor quality? Did the contractor (Company Y) exercise due diligence? Was their methodology adequate? Were required standards of care followed and were the work products of required quality? 57 Copyright 2013 by Data Blueprint
  • Expert Reports Expert  Report Ours provided evidence that : 1. Company Y's conversion code introduced errors into the data 2. Some data that Company Y converted was of measurably lower quality than the quality of the data before the conversion 3. Company Y caused harm by not performing an analysis of the Company X's legacy systems and that that the required analysis was not a part of any project plan used by Company Y 4. Company Y caused harm by withholding specific information relating to the perception of the on-site consultants' views on potential project success 58 Copyright 2013 by Data Blueprint
  • FBI & Canadian Social Security Gender Codes 1. 2. 3. 4. 5. 6. 7. 8. 9. Male If column 1 in source = "m" Female • then set Formerly male now female value of target data Formerly female now male to "male" Uncertain • else set value of Won't tell target data to "female" Doesn't know Male soon to be female Female soon to be male 51 Copyright 2013 by Data Blueprint
  • 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 ! First it checks if the applicant/employee exists on ! ! the PeopleSoft database using the SSN. The defendant knew to prevent duplicate SSNs !************************************************************************ Begin-Procedure 230-Assign-PS-Emplid move 'N' to $found_in_PS !DAR 01/14/04 move 'N' to $found_on_XXX !DAR 01/14/04 BEGIN-SELECT -Db'DSN=HR83PRD;UID=PS_DEV;PWD=psdevelopment' NID.EMPLID NID.NATIONAL_ID move 'Y' to $found_in_PS !DAR 01/14/04 move &NID.EMPLID to $ps_emplid The exclamation point prevents this line from looking for duplicates, so no check is made for a duplicate SSN/National ID FROM PS_PERS_NID NID !WHERE NID.NATIONAL_ID = $ps_ssn WHERE NID.AJ_APPL_ID = $applicant_id END-SELECT if $found_in_PS = 'N' do 231-Check-XXX-for-Empl !DAR 01/14/04 !DAR 01/14/04 if $found_on_XXX = 'N' !DAR 01/14/04 add 1 to #last_emplid Legacy systems business rules allowed employees to have more than one 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 End-Procedure 230-Assign-PS-Emplid 60 Copyright 2013 by Data Blueprint
  • 61 Copyright 2013 by Data Blueprint
  • Identified & Quantified Risks 62 Copyright 2013 by Data Blueprint
  • Risk Response “Risk response development involves defining enhancement steps for opportunities and threats.” Page 119, Duncan, W., A Guide to the Project Management Body of Knowledge, PMI, 1996 Tasks Hours New Year Conversion Tax and payroll balance conversion General Ledger conversion Total 120 120 80 320 Resource Hours G/L Consultant Project Manager Recievables Consultant HRMS Technical Consultant Technical Lead Consultant HRMS Consultant Financials Technical Consultant Total 40 40 40 40 40 40 40 280 "The go-live date may need to be extended due to certain critical path deliverables not being met. This extension will require additional tasks and resources. The decision of whether or not to extend the go-live date should be made by Monday, November 3, 20XX so that resources can be allocated to the additional tasks." Delay Weekly Resources Weeks Tasks Cumulative January (5 weeks) 280 5 320 1720 February (4 weeks) 280 4 1120 Total 2840 63 Copyright 2013 by Data Blueprint
  • Project Management Planning Process Planning Area Scope Planning Scope Definition Activity Definition Activity Sequencing Activity Duration Estimation Schedule Development Resource Planning Cost Estimating Cost Budgeting Project Plan Development Quality Planning Communication Planning Risk Identification Risk Quantification Risk Response Organizational Planning Staff Acquisition Company Y Methodology Demonstrated √ √ √ √ √ √ √ √ √ √ √ √ ? ? √ √ √ √ √ √ ? √ √ √ Company X Lead ? ? 64 Copyright 2013 by Data Blueprint
  • Inadequate Standard of Care - Tasks without Predecessors 65 Copyright 2013 by Data Blueprint
  • Inadequate Standard of Care 66 Copyright 2013 by Data Blueprint
  • 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. 67 Copyright 2013 by Data Blueprint
  • The Defense's "Industry Standards" • Question: – What are the industry standards that you are referring to? • Answer: – There is nothing written or codified, but it is the standards which are recognized by the consulting firms in our (industry). • Question: – I understand from what you told me just a moment ago that the industry standards that you are referring to here are not written down anywhere; is that correct? • Answer: – That is my understanding. • Question: – Have you made an effort to locate these industry standards and have simply not been able to do so? • Answer: – I would not know where to begin to look. 68 Copyright 2013 by Data Blueprint
  • Outline 1. Data Management Overview 2. Book Motivations & Survey Results 3. Leveraging Data 4. Monetary ROI (6 cases) 5. Non-Monetary ROI (2 cases) 6. Legal Considerations 7. Take Aways and Q&A 69 Copyright 2013 by Data Blueprint
  • Outline 1. Data Management Overview 2. Book Motivations & Survey Results 3. Leveraging Data 4. Monetary ROI (6 cases) 5. Non-Monetary ROI (2 cases) 6. Legal Considerations 7. Take Aways and Q&A 70 Copyright 2013 by Data Blueprint
  • Monetizing Data Management • State Agency Time & Leave Tracking – Time and leave tracking • • International Chemical Company – – • Transformation of non-tabular data $5 million annually Person Centuries British Telecom Project Rollout – £250 (small investment) Non-Monetary Examples – – • DATA MANAGEMENT Data management: Test results $25 million UDS annually • • • MONETIZING ERP Implementation – • $1 million USD annually Friendly Fire Suicide Mitigation Legal – ERP Implementation Legal Case • Unlocking the Value in Your Organization’s Most Important Asset. PETER AIKEN WITH JUANITA BILLINGS FOREWORD BY JOHN BOTTEGA $ 5,355,450 CAN damages/penalties 71 Copyright 2013 by Data Blueprint
  • Questions? + = It’s your turn! Use the chat feature or Twitter (#dataed) to submit your questions to Peter now. 72 Copyright 2013 by Data Blueprint
  • Upcoming Events November Webinar: Unlock Business Value Through Reference & MDM Novemeber 12, 2013 @ 2:00 PM – 3:30 PM ET (11:00 AM-12:30 PM PT) December: Unlock Business Value Through Document & Content Management December 10, 2013 @ 2:00 PM – 3:30 PM ET (11:00 AM-12:30 PM PT) Sign up here: • www.datablueprint.com/webinar-schedule • www.Dataversity.net Brought to you by: 73 Copyright 2013 by Data Blueprint