Data-Ed Webinar: Data Governance Strategies

Data-Ed Webinar: Data Governance Strategies
Data Governance Strategies • Date: September 9, 2014 • Time: 2:00 PM ET • Presented by: Peter Aiken, PhD • The data governance function exercises authority and control over the management of your mission critical assets and guides how all other data management functions are performed. When selling data governance to organizational management, it is useful to concentrate on the specifics that motivate the initiative. This means developing a specific vocabulary and set of narratives to facilitate understanding of your organizational business concepts. This webinar provides you with an understanding of what data governance functions are required and how they fit with other data management disciplines. Understanding these aspects is a necessary pre-requisite to eliminate the ambiguity that often surrounds initial discussions and implement effective data governance and stewardship programs that manage data in support of organizational strategy. 1 Copyright 2014 by Data Blueprint 1
Commonly Asked Questions 1)Will I get copies of the slides after the event? 2) Is this being recorded so I can view it afterwards? 2 Copyright 2014 by Data Blueprint 2
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Data Governance Strategies “If you don't know where you are going, any road will get you there.” Presented By Peter Aiken, Ph.D. - Lewis Carroll 4
MONETIZING DATA MANAGEMENT Unlocking the Value in Your Organization’s Most Important Asset. PETER AIKEN WITH JUANITA BILLINGS FOREWORD BY JOHN BOTTEGA Peter Aiken, Ph.D. • 30+ years of experience in data management • Multiple international awards & recognition • Founder, Data Blueprint (datablueprint.com) • Associate Professor of IS, VCU (vcu.edu) • (Past) President, DAMA Int. (dama.org) • 9 books and dozens of articles • Experienced w/ 500+ data management practices in 20 countries • Multi-year immersions with organizations as diverse as the US DoD, Nokia, Deutsche Bank, Wells Fargo, Walmart, and the Commonwealth of Virginia 5 Copyright 2014 by Data Blueprint The Case for the Chief Data Officer Recasting the C-Suite to Leverage Your Most Valuable Asset Peter Aiken and Michael Gorman 5
Motivation Beth Jacobs abruptly resigned in March 6 Copyright 2014 by Data Blueprint 6
Reported Home Depot data breach could exceed Target hack 7 Copyright 2014 by Data Blueprint 7
8 Copyright 2014 by Data Blueprint Data Governance Strategies • Strategy – Term of Recent Usage – Context: Organizational -> IT -> Data – Difficult Choices • Data Governance – What is it? – Why is it important? – Requirements for Effective Data Governance • Data Governance Components – Frameworks – Building Blocks – Checklists – Worst Practices • Data Governance (Storytelling) in Action • Take Aways/References/Q&A Tweeting now: #dataed 8
What is Strategy? • Current use derived from military • "a pattern in a stream of decisions" [Henry Mintzberg] • "a system of finding, formulating, and developing a doctrine that will ensure long-term success if followed faithfully [Vladimir Kvint] 10 Copyright 2014 by Data Blueprint 10
Strategy in Action: Napoleon defeats a larger enemy • Question? – How to I defeat the competition when their forces are bigger than mine? • Answer: – Divide and conquer! – of decisions” 11 Copyright 2014 by Data Blueprint – “a pattern in a stream 11
Strategy in Action: Napoleon defeats a larger enemy Copyright 2014 by Data Blueprint 12 12
Wayne Gretzky’s Strategy He skates to where he thinks the puck will be ... 13 Copyright 2014 by Data Blueprint 13
Data Strategy in Context • Organizational Strategy • IT Strategy • Data Governance Strategy 14 Copyright 2014 by Data Blueprint 14
Corporate Governance • "Corporate governance - which can be defined narrowly as the relationship of a company to its shareholders or, more broadly, as its relationship to society….", Financial Times, 1997. • "Corporate governance is about promoting corporate fairness, transparency and accountability" James Wolfensohn, World Bank, President Financial Times, June 1999. • “Corporate governance deals with the ways in which suppliers of finance to corporations assure themselves of getting a return on their investment”, The Journal of Finance, Shleifer and Vishny, 1997. 15 Copyright 2014 by Data Blueprint 15
Definition of IT Governance IT Governance: • "putting structure around how organizations align IT strategy with business strategy, ensuring that companies stay on track to achieve their strategies and goals, and implementing good ways to measure IT’s performance. • It makes sure that all stakeholders’ interests are taken into account and that processes provide measurable results. • An IT governance framework should answer some key questions, such as how the IT department is functioning overall, what key metrics management needs and what return IT is giving back to the business from the investment it’s making." CIO Magazine (May 2007) IT Governance Institute, five areas of focus: • Strategic Alignment • Value Delivery • Resource Management • Risk Management • Performance Measures 16 Copyright 2014 by Data Blueprint 16
No clear connection exists between to business priorities and IT initiatives 17 Leverage Growth Return Copyright 2014 by Data Blueprint Grow expenses slower than sales Grow operating income faster than sales Pass on savings Drive efficiency with technology Leverage scale globally Leverage expertise Deploy new formats Grow productivity of existing assets Attract new members Expand into new channels Enter new markets Make acquisitions Produce significant free cash flow Drive ROI performance Deliver greater shareholder value Customer Perspectiv e Open new stores Develop new, innovative formats Appeal to new demographics Integrate shopping experience Develop new, innovative formats Remain relevant to all customers Increase "Green" Image Internal Perspectiv e Create competitive advantages Improve use of information Strengthen supply chain Improve Associate productivity Making acquisitions Increase benefit from our global expertise Present consistent view and experience Integrate channels Match staffing to store needs Increase sell through Financial Perspectiv e Reduce expenses Inventory Management Human and Intell. Capital investment Manage new facilities Improve Sales and margin by facilities Increased member-base revenues Revenue growth Cash flow Return on Capital Walmart Strategy Map See more uniform brand and retail experience Gross Margin Improvement CEO Perspective Attract more customers & have customer purchasing more ( Alignment Gap ) Associate Productivity Customer Insights Supply Chain Merchant Tools Multi Channel Human Capital Corp. Reputation Acquisition Strategic Planning Real estate CRM CRM Analytic and reporting processes Corporate Reputation - Risk Management, Compliance, Marketing, IT and Data Governance Corporate Processes Retail Planning Corporate Data Inventory Mgmt Transformation Portfolio Supply Chain Strategic Initiatives Sales Accting Transactional Processing Logistics Locations and Codes Associate Item Suppliers Customer Adapted from John Ladley 17
Strategy is Difficult to Perceive at the IT Project Level • If they exist ... • A singular organizational strategy and set of goals/objectives ... • Are not perceived as such at the project level and ... • What does exist is confused, inaccurate, and incomplete • IT projects do not well reflect organizational strategy Organizational Strategy Set of Organizational Goals/Objectives Organizational IT 18 Copyright 2014 by Data Blueprint Division/Group/Project 18
Data Governance Strategy Choices ! Q1 Keeping the doors open (little or no proactive data management) Q2 Increasing organizational efficiencies/effectiveness Q3 Using data to create strategic opportunities Q4 Both Improve Operations Innovation Only 1 is 10 organizations has a board approved data strategy! 19 Copyright 2014 by Data Blueprint 19
Supplemental: CMMI Data Strategy Elements The data management strategy defines the overall framework of the program. A data management strategy typically includes: • A vision statement, which includes core operating principles; goals and objectives; priorities, based on a synthesis of factors important to the organization, such as business value, degree of support for strategic initiatives, level of effort, and dependencies • Program scope – including both key business areas (e.g. Customer Accounts); data management priorities (e.g. Data Quality); and key data sets (e.g. Customer Master Data) • Business benefits – The selected data management framework and how it will be used – High-level roles and responsibilities – Governance needs – Description of the approach used to develop the data management program – Compliance approach and measures – High-level sequence plan (roadmap). 20 Copyright 2014 by Data Blueprint 20
7 Data Governance Definitions • The formal orchestration of people, process, and technology to enable an organization to leverage data as an enterprise asset. - The MDM Institute • A convergence of data quality, data management, business process management, and risk management surrounding the handling of data in an organization – Wikipedia • A system of decision rights and accountabilities for information-related processes, executed according to agreed-upon models which describe who can take what actions with what information, and when, under what circumstances, using what methods – Data Governance Institute • The execution and enforcement of authority over the management of data assets and the performance of data functions – KiK Consulting • A quality control discipline for assessing, managing, using, improving, monitoring, maintaining, and protecting organizational information – IBM Data Governance Council • Data governance is the formulation of policy to optimize, secure, and leverage information as an enterprise asset by aligning the objectives of multiple functions – Sunil Soares • The exercise of authority and control over the management of data assets – DM BoK 23 Copyright 2014 by Data Blueprint 23
DAMA DM BoK & CDMP • 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 (more at dama.org) – Organized around several environmental elements • 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 24 Data Management Functions Copyright 2014 by Data Blueprint 24
5 Requirements for Effective DG Data governance is a set of well-defined policies and practices designed to ensure that data is: 1. Accessible – Can the people who need it access the data they need? – Does the data match the format the user requires? 2. Secure – Are authorized people the only ones who can access the data? – Are non-authorized users prevented from accessing it? 3. Consistent – When two users seek the "same" piece of data, is it actually the same data? – Have multiple versions been rationalized? 4. High Quality – Is the data accurate? – Has it been conformed to meet agreed standards 5. Auditable – Where did the data come from? – Is the lineage clear? – Does IT know who is using it and for what purpose? • Integrity • Accountability • Transparency • Strategic alignment • Standardization • Organizational change management • Data architecture • Stewardship/Quality • Protection Source: “5 Steps to Effective Data Governance” by Angela Guess; http://www.dataversity.net/archives/5160 25 Copyright 2014 by Data Blueprint 25
Organizational Data Governance Purpose Statement • What does data governance mean to my organization? – Getting some individuals (whose opinions matter) – To form a body (needs a formal purpose/authority) – Who will advocate/evangelize for (not dictate, enforce, rule) – Increasing scope and rigor of – Data-centric development practices 26 Copyright 2014 by Data Blueprint 26
Use Their Language ... • Getting access to data around here is like that Catherine Zeta Jones scene where she is having to get thru all those lasers … 27 Copyright 2014 by Data Blueprint 27
Practice Articulating How DG Solves Problems 28 Copyright 2014 by Data Blueprint Organizational Strategy Formulation/Implementation Data Security Planning/Implementation Operational Data Delivery Performance Data Quality/Inventory Management Decision Making Needs 28
What is the Difference Between DG and DM? • Data Governance – Policy level guidance – Setting general guidelines and direction – Example: All information not marked public should be considered confidential • Data Management – The business function of planning for, controlling and delivering data/information assets – Example: Delivering data to solve business challenges 29 Copyright 2014 by Data Blueprint 29
DMM℠ Structure 30 Copyright 2014 by Data Blueprint 30
One concept for process improvement, others include: • Norton Stage Theory • TQM • TQdM • TDQM • ISO 9000 ! and focus on understanding current processes and determining where to make improvements. DMM℠ Capability Maturity Model Levels Our DM practices are informal and ad hoc, dependent upon "heroes" and heroic efforts Performed (1) Managed (2) Our DM practices are defined and documented processes performed at the business unit level Our DM efforts remain aligned with business strategy using standardized and consistently implemented practices Defined (3) Measured (4) We manage our data as a asset using advantageous data governance practices/structures Optimized (5) DM is strategic organizational capability, most importantly we have a process for improving our DM capabilities 31 Copyright 2014 by Data Blueprint 31
• Assessment Components Data Management Practice Areas Data Management Strategy DM is practiced as a coherent and coordinated set of activities Data Quality Delivery of data is support of organizational objectives – the currency of DM Data Governance Designating specific individuals caretakers for certain data Data Platform/ Architecture Efficient delivery of data via appropriate channels Data Operations Ensuring reliable access to data Capability Maturity Model Levels Examples of practice maturity 1 – Performed Our DM practices are ad hoc and dependent upon "heroes" and heroic efforts 2 – Managed We have DM experience and have the ability to implement disciplined processes 3 – Defined We have standardized DM practices so that all in the organization can perform it with uniform quality 4 – Measured We manage our DM processes so that the whole organization can follow our standard DM guidance 5 – Optimized We have a process for improving our DM capabilities 32 Copyright 2014 by Data Blueprint 32
Industry Focused Results 33 Copyright 2014 by Data Blueprint Data Management Strategy Data Governance Platform & Architecture Data Quality Data Operations Optimized (V) Measured (IV) Defined (III) Managed (II) Initial (I) • CMU's Software Engineering Institute (SEI) Collaboration • Results from hundreds organizations in various industries including: ✓ Public Companies ✓ State Government Agencies ✓ Federal Government ✓ International Organizations • Defined industry standard • Steps toward defining data management "state of the practice" 33
Comparative Assessment Results Data Management Strategy Data Governance Data Platform & Architecture Data Quality Data Operations Challenge Challenge Challenge 0 1 2 3 4 5 Client Industry Competition All Respondents 34 Copyright 2014 by Data Blueprint 34
5 4 3 2 1 Comparison of DM Maturity 2007-2012 Data Program Coordination Organizational Data Integration Data Stewardship Data Development Data Support Operations 2007 Maturity Levels 2012 Maturity Levels 35 Copyright 2014 by Data Blueprint 35
2012 London Summer Games • 60 GB of data/second • 200,000 hours of big data will be generated testing systems • 2,000 hours media coverage/daily • 845 million Facebook users averaging 15 TB/ day • 13,000 tweets/second • 4 billion watching • 8.5 billion devices connected 36 Copyright 2014 by Data Blueprint 36
Supplemental: Data Governance Goals and Principles • To define, approve, and communicate data strategies, policies, standards, architecture, procedures, and metrics. • To track and enforce regulatory compliance and conformance to data policies, standards, architecture, and procedures. • To sponsor, track, and oversee the delivery of data management projects and services. • To manage and resolve data related issues. • To understand and promote the value of data assets. from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International 37 Copyright 2014 by Data Blueprint 37
Supplemental: Data Governance Activities • Understand Strategic Enterprise Data Needs • Develop and Maintain the Data Strategy • Establish Data Professional Roles and Organizations • Identify and Appoint Data Stewards • Establish Data Governance and Stewardship Organizations • Develop and Approve Data Policies, Standards, and Procedures • Review and Approve Data Architecture • Plan and Sponsor Data Management Projects and Services • Estimate Data Asset Value and Associated Costs from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International 38 Copyright 2014 by Data Blueprint 38
Supplemental: Data Governance Primary Deliverables • Data Policies • Data Standards • Resolved Issues • Data Management Projects and Services • Quality Data and Information • Recognized Data Value from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International 39 Copyright 2014 by Data Blueprint 39
Supplemental: Data Governance Roles and Responsibilities • Participants: – Executive Data Stewards – Coordinating Data Stewards – Business Data Stewards – Data Professionals – DM Executive – CIO • Suppliers: – Business Executives – IT Executives – Data Stewards – Regulatory Bodies • Consumers: – Data Producers – Knowledge Workers – Managers and Executives – Data Professionals – Customers from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International 40 Copyright 2014 by Data Blueprint 40
Supplemental: Data Governance Technologies • Intranet Website • E-Mail • Metadata Tools • Metadata Repository • Issue Management Tools • Data Governance KPI Dashboard from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International 41 Copyright 2014 by Data Blueprint 41
Supplemental: Data Governance Practices and Techniques • Data Value • Data Management Cost • Achievement of Objectives • # of Decisions Made • Steward Representation/Coverage • Data Professional Headcount • Data Management Process Maturity from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International 42 Copyright 2014 by Data Blueprint 42
Why is Data Governance Important? Cost organizations millions each year in • Productivity • Redundant and siloed efforts • Poorly thought out hardware and software purchases • Reactive instead of proactive initiatives • Delayed decision making using inadequate information • 20-40% of IT spending can be reduced through better data governance 43 Copyright 2014 by Data Blueprint 43
Largely Ineffective Investments • Approximately, 10% percent of organizations achieve parity and (potential positive returns) on their investments • Only 30% of investments achieve tangible returns at all • Seventy percent of organizations have very small or no tangible return on their investments 44 Copyright 2014 by Data Blueprint 44
Application-Centric Development Strategy Goals/ Objectives Systems/ Applications Network/ Infrastructure Original articulation from Doug Bagley @ Walmart • 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 Data/ Information 45 Copyright 2014 by Data Blueprint 45
What does it mean to treat data as an organizational asset? • An asset is a resource controlled by the organization as a result of past events or transactions and from which future economic benefits are expected to flow to the organization [Wikipedia] • Assets are economic resources – Must own or control – Must use to produce value – Value can be converted into cash • As assets: – Formalize the care and feeding of data – Put data to work in unique and significant ways 46 Copyright 2014 by Data Blueprint 46
Evolving Data is Different than Creating New Systems 47 Copyright 2014 by Data Blueprint Common Organizational Data (and corresponding data needs requirements) Evolve New Organizational Capabilities Systems Development Activities Create Future State (Version +1) Data evolution is separate from, external to, and precedes system development life cycle activities! 47
Data-Centric Development Strategy Goals/ Objectives Data/ Information Network/ Infrastructure Original articulation from Doug Bagley @ Walmart • 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 Systems/ Applications 48 Copyright 2014 by Data Blueprint 48
The special nature of DCD • An architectural focus • Practice extension • Personality/organizational challenges unrecognized • Technical engineering requires different skills • Extra attention required to communication • Scarcity of professionals • Need for a specialist discipline MONETIZING DATA MANAGEMENT Unlocking the Value in Your Organization’s 49 Copyright 2014 by Data Blueprint Most Important Asset. PETER AIKEN WITH JUANITA BILLINGS FOREWORD BY JOHN BOTTEGA When our organizations transform to a data-centric approach, we begin to measure success differently than we did before—same project, same process, but with different measures that include: • asking if our data is correct; • valuing data more than valuing "on time and within budget;" • valuing correct data more than correct process; and • auditing data rather than project documents. - Linda Bevolo 49
Getting Started 52 Copyright 2014 by Data Blueprint Assess context Define DG roadmap Secure executive mandate Assign Data Stewards Execute plan Evaluate results Revise plan Apply change management (Occurs once) (Repeats) 52
Data Governance Frameworks • A system of ideas for guiding analyses • A means of organizing project data • Priorities for data decision making • A means of assessing progress – Don’t put up walls until foundation inspection is passed – Put the roof on ASAP • Make it all dependent upon continued funding 53 Copyright 2014 by Data Blueprint 53
from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International Data Governance from the DMBOK 54 Copyright 2014 by Data Blueprint 54
Data Governance Institute • A system of ideas for guiding analyses • A means of organizing project data • Data integration priorities decision making framework • A means of assessing progress 55 Copyright 2014 by Data Blueprint http://www.datagovernance.com/ 55
KiK Consulting • A system of ideas for guiding analyses • A means of organizing project data • Data integration priorities decision making framework • A means of assessing progress http://www.kikconsulting.com/ 56 Copyright 2014 by Data Blueprint 56
http://www-01.ibm.com/software/data/system-z/data-governance/workshops.html IBM Data Governance Council • A system of ideas for guiding analyses • A means of organizing project data • Data integration priorities decision making framework • A means of assessing progress 57 Copyright 2014 by Data Blueprint 57
Elements of Effective Data Governance See IBM Data Governance Council, http://www-01.ibm.com/software/tivoli/ governance/servicemanagement/ data-governance.html. 58 Copyright 2014 by Data Blueprint 58
Baseline Consulting (sas.com) 59 Copyright 2014 by Data Blueprint 59
American College Personnel Association 60 Copyright 2014 by Data Blueprint 60
Supplemental: NASCIO DG Implementation Process 61 Copyright 2014 by Data Blueprint 61
Supplemental: Data Governance Checklist ✓ Decision-Making Authority ✓ Standard Policies and Procedures ✓ Data Inventories ✓ Data Content Management ✓ Data Records Management ✓ Data Quality ✓ Data Access ✓ Data Security and Risk Management Source: “Data Governance Checklist for Educators” by Angela Guess; http://www.dataversity.net/archives/5198 62 Copyright 2014 by Data Blueprint 62
Supplemental: Data Governance Checklist • The Privacy Technical Assistance Center has published a new checklist “to assist stakeholder organizations, such as state and local education agencies, with establishing and maintaining a successful data governance program to help ensure the individual privacy and confidentiality of education records.” • The five page paper offers a number of suggestions for implementing a successful data governance program that can be applied to a variety of business models beyond education. • For more information, please visit the Privacy Technical Assistance Center: http://ed.gov/ptac Source: “Data Governance Checklist for Educators” by Angela Guess; http://www.dataversity.net/archives/5198 63 Copyright 2014 by Data Blueprint 63
Supplemental: NASCIO Scorecard 64 Copyright 2014 by Data Blueprint 64
Supplemental: 10 DG Worst Practices 1. Buy-in but not Committing: Business vs. IT 2. Ready, Fire, Aim 3. Trying to Solve World Hunger or Boil the Ocean 4. The Goldilocks Syndrome 5. Committee Overload 6. Failure to Implement 7. Not Dealing with Change Management 8. Assuming that Technology Alone is the Answer 9. Not Building Sustainable and Ongoing Processes 10. Ignoring “Data Shadow Systems” 65 Copyright 2014 by Data Blueprint 65
Simon Sinek: How great leaders inspire action 68 Copyright 2014 by Data Blueprint http://www.ted.com/talks/simon_sinek_how_great_leaders_inspire_action.html Why How What 68
Attaching Stuff to the Engine • Detroit – 10 different bolts – 10 different wrenches – 10 different bolt inventories • Toyota – Same bolts used for all assemblies – 1 bolt inventory – 1 type of wrench 69 Copyright 2014 by Data Blueprint 69
70 Copyright 2014 by Data Blueprint 70
healthcare.gov • 55 Contractors! • 6 weeks from launch and requirements not finalized • "Anyone who has written a line of code or built a system from the ground-up cannot be surprised or even mildly concerned that Healthcare.gov did not work out of the gate," Standish Group International Chairman Jim Johnson said in a recent podcast. • "The real news would have been if it actually did work. The very fact that most of it did work at all is a success in itself." • "It was pretty obvious from the first look that the system hadn't been designed to work right," says Marty Abbott. "Any single thing that slowed down would slow everything down." • Software programmed to access data using traditional technologies • Data components incorporated "big data technologies" http://www.slate.com/articles/technology/bitwise/2013/10/ problems_with_healthcare_gov_cronyism_bad_management_and_too_ many_cooks.html 71 Copyright 2014 by Data Blueprint 71
Formalizing the Role of U.S. Army IT Governance/ Compliance 72 Copyright 2014 by Data Blueprint 72
Suicide Mitigation 73 Copyright 2014 by Data Blueprint 73
Data Mapping 12 Mental illness Deploy ments Work History Soldier Legal Issues Abuse Suicide Analysis DMSS G1 DMDC FAP CID Data objects complete? All sources identified? Best source for each object? How reconcile differences between sources? MDR 74 Copyright 2014 by Data Blueprint 74
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 75 Copyright 2014 by Data Blueprint 75
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 76 Copyright 2014 by Data Blueprint 76
Vocabulary is Important-Tank, Tanks, Tankers, Tanked 77 Copyright 2014 by Data Blueprint 77
How one inventory item proliferates data throughout the chain 78 Copyright 2014 by Data Blueprint 555 Subassemblies & subcomponents 17,659 Repair parts or Consumables System 1: 18,214 Total items 75 Attributes/ item 1,366,050 Total attributes System 2 47 Total items 15+ Attributes/item 720 Total attributes System 3 16,594 Total items 73 Attributes/item 1,211,362 Total attributes System 4 8,535 Total items 16 Attributes/item 136,560 Total attributes System 5 15,959 Total items 22 Attributes/item 351,098 Total attributes Total for the five systems show above: 59,350 Items 179 Unique attributes 3,065,790 values 78
Business Implications • National Stock Number (NSN) Discrepancies – If NSNs in LUAF, GABF, and RTLS are not present in the MHIF, these records cannot be updated in SASSY – Additional overhead is created to correct data before performing the real maintenance of records • Serial Number Duplication – If multiple items are assigned the same serial number in RTLS, the traceability of those items is severely impacted – Approximately $531 million of SAC 3 items have duplicated serial numbers • On-Hand Quantity Discrepancies – If the LUAF O/H QTY and number of items serialized in RTLS conflict, there can be no clear answer as to how many items a unit actually has on-hand – Approximately $5 billion of equipment does not tie out between the LUAF and RTLS 79 Copyright 2014 by Data Blueprint 79
Spreadsheet Interpretation 80 Copyright 2014 by Data Blueprint 80
Barclays Excel Spreadsheet Horror • Barclays preparing to buy Lehman’s Brothers assets. • 179 dodgy Lehman’s contracts were almost accidentally purchased by Barclays because of an Excel spreadsheet reformatting error • A first-year associate reformatted an Excel contracts spreadsheet – Predictably, this work was done long after normal business hours, just after 11:30 p.m... • The Lehman/Barclays sale closed on September 22nd • the 179 contracts were marked as “hidden” in Excel, and those entries became “un-hidden” when when globally reformatting the document. 81 Copyright 2014 by Data Blueprint 81
Example of Poor Data Governance Mizuho Securities Example • Wanted to sell 1 share for 600,000 yen • Sold 600,000 shares for 1 yen • $347 million loss • In-house system did not have limit checking • Tokyo stock exchange system did not have limit checking • And doesn't allow order cancellations CLUMSY typing cost a Japanese bank at least £128 million and staff their Christmas bonuses yesterday, after a trader mistakenly sold 600,000 more shares than he should have. The trader at Mizuho Securities, who has not been named, fell foul of what is known in financial circles as “fat finger syndrome” where a dealer types incorrect details into his computer. He wanted to sell one share in a new telecoms company called J Com, for 600,000 yen (about £3,000). 82 Copyright 2014 by Data Blueprint 82
83 Copyright 2014 by Data Blueprint 83
Seven Sisters from British Telecom 84 Copyright 2014 by Data Blueprint Thanks to Dave Evans 84
Maslow's Hierarchy of Needs 87 Copyright 2014 by Data Blueprint 87
Build a Solid Foundation for Advanced Solutions You can accomplish Advanced Data Practices without becoming proficient in the Basic Data Advanced Management Practices Data however this will: Practices • MDM • Take longer • Mining • Cost more • Big Data • Analytics • Deliver less • Warehousing • Present • SOA greater risk Basic Data Management Practices 88 Copyright 2014 by Data Blueprint Data Management Strategy Data Governance Data Management Function Metadata Management Data Quality Program 88
Data Management Practices Hierarchy Outcomes (tooth) Capabilities (tail) 89 Copyright 2014 by Data Blueprint 89
Take Aways • Need for DG is increasing – Increase in data volume – Lack of practice improvement • DG is a new discipline – Must conform to constraints – No one best way • DG must be driven by a data strategy complimenting organizational strategy • Comparing DG frameworks can be useful • DG directs data management efforts • The language of DG is metadata • Process improvement can improve DG practices 90 Copyright 2014 by Data Blueprint 90
The File Naming Convention Committee's Output 91 Copyright 2014 by Data Blueprint 91
Data Governance Council Hotel 92 Copyright 2014 by Data Blueprint 92
93 MONETIZING DATA MANAGEMENT Unlocking the Value in Your Organization’s Most Important Asset. PETER AIKEN WITH JUANITA BILLINGS FOREWORD BY JOHN BOTTEGA Copyright 2014 by Data Blueprint 93
Supplemental: Data Governance Checklist • Decision-Making Authority – Assign appropriate levels of authority to data stewards – Proactively define scope and limitations of that authority • Standard Policies and Procedures – Adopt and enforce clear policies and procedures in a written data stewardship plan to ensure that everyone understands the importance of data quality and security – Helps to motivate and empower staff to implement DG • Data Inventories – Conduct inventory of all data that require protection – Maintain up-to-date inventory of all sensitive records and data systems – Classify data by sensitivity to identify focus areas for security efforts • Data Content Management – Closely manage data content to justify the collection of sensitive data, optimize data management processes and ensure compliance with federal, state, and local regulations Source: “Data Governance Checklist for Educators” by Angela Guess; http://www.dataversity.net/archives/5198 94 Copyright 2014 by Data Blueprint 94
Supplemental: Data Governance Checklist, cont’d • Data Records Management – Specify appropriate managerial and user activities related to handling data to provide data stewards and users with appropriate tools for complying with an organization’s security policies • Data Quality – Ensure that data are accurate, relevant, timely, and complete for their intended purposes – Key to maintaining high quality data is a proactive approach to DG that requires establishing and regularly updating strategies for preventing, detecting, and correcting errors and misuses of data • Data Access – Define and assign differentiated levels of data access to individuals based on their roles and responsibilities – This is critical to prevent unauthorized access and minimize risk of data breaches • Data Security and Risk Management – Ensure the security of sensitive and personally identifiable data and mitigate the risks of unauthorized disclosure of these data – Top priority for effective data governance plan Source: “Data Governance Checklist for Educators” by Angela Guess; http://www.dataversity.net/archives/5198 95 Copyright 2014 by Data Blueprint 95
Supplemental: 10 DG Worst Practices in Detail 1. Buy-in but not Committing: Business vs. IT – Business needs to do more – Data governance tasks need to recognized as priority – Without a real business-resource commitment, data governance takes a backseat and will never be implemented effectively 2. Ready, Fire, Aim – Good: Create governance steering committee (business representatives from across enterprise) and separate governance working group (data stewards) – Problem: Often get the timing wrong: Panels are formed and people are assigned BEFORE they really understand the scope of the data governance and participants’ roles and responsibilities – Prematurely organize management framework and realize you need a do-over = Guaranteed way to stall DG initiative Source: “Data Governance Worst Practices” by Angela Guess; http://www.dataversity.net/archives/4895 96 Copyright 2014 by Data Blueprint 96
Supplemental: 10 DG Worst Practices in Detail 3. Trying to Solve World Hunger or Boil the Ocean • Trap 1: Trying to solve all organizational data problems in initial project phase • Trap 2: Starting with biggest data problems (highly political issues) • Almost impossible to establish a DG program while tacking data problems that have taken years to build up • Instead: “Think globally and act locally”: break data problems down into incremental deliverables • “Too big too fast” = Recipe for disaster 4. The Goldilocks Syndrome • Encountering things that are either one extreme or another • Either the program is too high-level and substantive issues are never dealt with or it attempts to create definitions and rules for every field and table • Need to find happy compromise that enables DG initiatives to create real business value Source: “Data Governance Worst Practices” by Angela Guess; http://www.dataversity.net/archives/4895 97 Copyright 2014 by Data Blueprint Source: “Data Governance Worst Practices” by Angela Guess; http://www.dataversity.net/archives/4895 97
Supplemental: 10 DG Worst Practices in Detail 5. Committee Overload • Good: People of various business units and departments get involved in the governance process • Bad: more people -> more politics -> more watered down governance responsibilities • To be successful, limit committee sizes to 6-12 people and ensure that members have decision-making authority ! 6. Failure to Implement • DG efforts won’t produce any business value if data definitions, business rules and KPIs are created but not used in any processes • Governance process needs to be a complete feedback loop in which data is defined, monitored, acted upon, and changed when appropriate • Also important: Establish ongoing communication about governance to prevent business users going back to old habits Source: “Data Governance Worst Practices” by Angela Guess; http://www.dataversity.Source: “Data Governance Worst Practices” by Angela Guess; http://www.dnaetat/vaercrshiitvye.nse/4t/8a9rc5hives/4895 98 Copyright 2014 by Data Blueprint 98
Supplemental: 10 DG Worst Practices in Detail 7.Not Dealing with Change Management • Business and IT processes need to be changed for enterprise DG to be successful • Need for change management is seldom addressed • Challenges: people/process issues and internal politics 8.Assuming that Technology Alone is the Answer • Purchasing MDM, data integration or data quality software to support DG programs is not the solution • Combination of vendor hype and high price tags set high expectations • Internal interactions are what make or break data governance efforts Source: “Data Governance Worst Practices” by Angela Guess; http://www.dataversity.net/archives/4895 99 Copyright 2014 by Data Blueprint 99
Supplemental: 10 DG Worst Practices in Detail 9.Not Building Sustainable and Ongoing Processes • Initial investment in time, money and people may be accurate • Many organizations don’t establish a budget, resource commitments or design DG processes with an eye toward sustaining the governance effort for the long term 10.Ignoring “Data Shadow Systems” • Common mistake: focus on “systems of record” and BI systems, assuming that all important data can be found there • Often, key information is located in “data shadow systems” scattered through organization • Don’t ignore such additional deposits of information Source: “Data Governance Worst Practices” by Angela Guess; http://www.dataversity.net/archives/4895 100 Copyright 2014 by Data Blueprint 100
References Websites ! ! • Data Governance Book ! Data Governance Book ! Compliance Book 101 Copyright 2014 by Data Blueprint 101
IT Governance Books 102 Copyright 2014 by Data Blueprint 102
Upcoming Events October Webinar: Trends in Data Modeling October 14, 2014 @ 2:00 PM ET ! November Webinar: Metadata Strategies November 11, 2014 @ 2:00 PM ET ! Sign up here: • www.datablueprint.com/webinar-schedule • www.Dataversity.net ! Brought to you by: 103 Copyright 2014 by Data Blueprint 103
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