Data-Ed Online Webinar: Data Governance Strategies
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Transcript of Data-Ed Online Webinar: Data Governance Strategies
Data Governance Strategies• Date: April 14, 2015 • 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.
• Learning Objectives – Understanding why data governance can be tricky for most organizations – Steps for improving data governance within your organization – Guiding principles & lessons learned – Understanding foundational data governance concepts based on the
DAMA DMBOK
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Shannon Kempe
Executive Editor at DATAVERSITY.net
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Peter Aiken, Ph.D.
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• 30+ years in data management • Repeated international recognition • Founder, Data Blueprint (datablueprint.com) • Associate Professor of IS (vcu.edu)
• DAMA International (dama.org) • 9 books and dozens of articles • Experienced w/ 500+ data
management practices • Multi-year immersions:
- US DoD - Nokia - Deutsche Bank- Wells Fargo - Walmart
• DAMA International President 2009-2013
• DAMA International Achievement Award 2001 (with Dr. E. F. "Ted" Codd
• DAMA International Community Award 2005
PETER AIKEN WITH JUANITA BILLINGSFOREWORD BY JOHN BOTTEGA
MONETIZINGDATA MANAGEMENT
Unlocking the Value in Your Organization’s
Most Important Asset.
The Case for theChief Data OfficerRecasting the C-Suite to LeverageYour Most Valuable Asset
Peter Aiken andMichael Gorman
We believe ...
Data Assets
Financial Assets
RealEstate Assets
Inventory Assets
Non-depletable
Available for subsequent
use
Can be used up
Can be used up
Non-degrading √ √ Can degrade
over timeCan degrade
over time
Durable Non-taxed √ √
Strategic Asset √ √ √ √
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Copyright 2015 by Data Blueprint
• Today, data is the most powerful, yet underutilized and poorly managed organizational asset
• Data is your – Sole – Non-depleteable – Non-degrading – Durable – Strategic
• Asset – Data is the new oil! – Data is the new (s)oil! – Data is the new bacon!
• Our mission is to unlock business value by – Strengthening your data management capabilities – Providing tailored solutions, and – Building lasting partnerships
Asset: A resource controlled by the organization as a result of past events or transactions and from which future economic benefits are expected to flow [Wikipedia]
Presented By Peter Aiken, Ph.D.
Data Governance Strategies
“If you don't know where you are going, any road will get you there.” - Lewis Carroll
Motivation
Beth Jacobs abruptly resigned in March
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Reported Home Depot data breach could exceed Target hack
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9Copyright 2015 by Data Blueprint
• 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
Data Governance Strategies
Tweeting now: #dataed
• 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
10Copyright 2015 by Data Blueprint
• 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
Data Governance Strategies
Tweeting now: #dataed
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]
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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!
– “a pattern in a stream of decisions”
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– “a pattern in a stream of decisions”
Strategy in Action: Napoleon defeats a larger enemy
Copyright 2014 by Data Blueprint
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Wayne Gretzky’s Strategy
He skates to where he thinks the puck will be ...
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Data Strategy in Context• Organizational Strategy
• IT Strategy
• Data Governance Strategy
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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.
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Definition of IT GovernanceIT 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 processesprovide 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
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No clear connection exists between to business priorities and IT initiatives
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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
Cus
tom
er
Per
spec
tive 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
Inte
rnal
P
ersp
ectiv
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
Fina
ncia
l P
ersp
ectiv
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
Leverage Growth Return
Gross Margin Improvement
CE
O P
ersp
ectiv
e
Attract more customers & have customer purchasing more
Associate Productivity
Customer Insights
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
Corporate Data
Inventory Mgmt
Tran
sfor
mat
ion
Port
folio
Supply Chain
Multi ChannelMerchant Tools Supply Chain
Strategic Initiatives
AcctingSales
Transactional Processing
Logistics Associate Locations and Codes
Item
Customer Suppliers
Retail Planning
( Alignment Gap )
Adapted from John Ladley
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
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Division/Group/Project
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
Inno
vatio
n
Only 1 is 10 organizations has a board approved data strategy!
Data Governance Strategy Choices
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Supplemental: CMMI Data Strategy ElementsThe 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).
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22Copyright 2015 by Data Blueprint
• 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
Data Governance Strategies
Tweeting now: #dataed
23Copyright 2015 by Data Blueprint
• 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
Data Governance Strategies
Tweeting now: #dataed
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
24Copyright 2014 by Data Blueprint
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
25Copyright 2014 by Data Blueprint
Data Management Functions
5 Requirements for Effective DGData 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?
26Copyright 2014 by Data Blueprint
Source: “5 Steps to Effective Data Governance” by Angela Guess; http://www.dataversity.net/archives/5160
• Integrity • Accountability • Transparency • Strategic alignment • Standardization • Organizational change
management • Data architecture • Stewardship/Quality • Protection
Organizational Data Governance Purpose Statement• What does data governance
mean to my organization? – Managing data with guidance
– 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
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• Getting access to data around here is like that Catherine Zeta Jones scene where she is having to get thru all those lasers …
Use Their Language ...
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Practice Articulating How DG Solves Problems
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Decision Making Needs
Data Quality/Inventory Management
Organizational Strategy Formulation/Implementation
Operational Data Delivery Performance
Data Security Planning/Implementation
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
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DMM℠ Structure
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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
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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 – PerformedOur DM practices are ad hoc and dependent upon "heroes" and heroic efforts
2 – ManagedWe 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 – MeasuredWe 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
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Industry Focused Results
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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"
Data Management Strategy
Data Governance
Data Platform & Architecture
Data Quality
Data Operations
0 1 2 3 4 5Client Industry Competition All Respondents
Comparative Assessment ResultsChallenge
Challenge
Challenge
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1
2
3
4
5
Data
Prog
ram
Coor
dinati
on
Orga
nizati
onal
Data
Integ
ratio
n
Data
Stew
ards
hip
Data
Deve
lopme
nt
Data
Supp
ort O
pera
tions
2007 Maturity Levels 2012 Maturity Levels
Comparison of DM Maturity 2007-2012
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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
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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.
38Copyright 2014 by Data Blueprint
from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
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
39Copyright 2014 by Data Blueprint
from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
Supplemental: Data Governance Primary Deliverables• Data Policies
• Data Standards
• Resolved Issues
• Data Management Projects and Services
• Quality Data and Information
• Recognized Data Value
40Copyright 2014 by Data Blueprint
from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
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
41Copyright 2014 by Data Blueprint
from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
Supplemental: Data Governance Technologies
• Intranet Website • E-Mail • Metadata Tools • Metadata Repository • Issue Management Tools • Data Governance KPI
Dashboard
42Copyright 2014 by Data Blueprint
from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
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
43Copyright 2014 by Data Blueprint
from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
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
44Copyright 2014 by Data Blueprint
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
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Application-Centric Development
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
Network/Infrastructure
Systems/Applications
Goals/Objectives
Strategy
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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
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Evolving Data is Different than Creating New Systems
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Common Organizational Data (and corresponding data needs requirements)
New Organizational Capabilities
Systems Development
Activities
Create
Evolve
Future State
(Version +1)
Data evolution is separate from, external to, and precedes system development life cycle activities!
Data-Centric Development
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
Network/Infrastructure
Data/Information
Goals/Objectives
Strategy
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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
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PETER AIKEN WITH JUANITA BILLINGSFOREWORD BY JOHN BOTTEGA
MONETIZINGDATA MANAGEMENT
Unlocking the Value in Your Organization’s
Most Important Asset.
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
51Copyright 2015 by Data Blueprint
• 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
Data Governance Strategies
Tweeting now: #dataed
52Copyright 2015 by Data Blueprint
• 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
Data Governance Strategies
Tweeting now: #dataed
Getting Started
53Copyright 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)
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
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Data Governance from the DMBOK
55Copyright 2014 by Data Blueprint
from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
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
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http://www.datagovernance.com/
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
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http://www.kikconsulting.com/
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
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http://www-01.ibm.com/software/data/system-z/data-governance/workshops.html
Elements of Effective Data Governance
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See IBM Data Governance Council, http://www-01.ibm.com/software/tivoli/ governance/servicemanagement/ data-governance.html.
Baseline Consulting (sas.com)
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American College Personnel Association
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Supplemental: NASCIO DG Implementation Process
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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
63Copyright 2014 by Data Blueprint
Source: “Data Governance Checklist for Educators” by Angela Guess; http://www.dataversity.net/archives/5198
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
64Copyright 2014 by Data Blueprint
Source: “Data Governance Checklist for Educators” by Angela Guess; http://www.dataversity.net/archives/5198
Supplemental: NASCIO Scorecard
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Supplemental: 10 DG Worst Practices1. 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”
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67Copyright 2015 by Data Blueprint
• 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
Data Governance Strategies
Tweeting now: #dataed
68Copyright 2015 by Data Blueprint
• 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
Data Governance Strategies
Tweeting now: #dataed
Simon Sinek: How great leaders inspire action
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Copyright 2014 by Data Blueprint
http://www.ted.com/talks/simon_sinek_how_great_leaders_inspire_action.html
What
How
Why
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
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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."
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• "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
Formalizing the Role of U.S. Army IT Governance/Compliance
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Suicide Mitigation
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Data Mapping
12
Mental illness
Deployments
Work History
Soldier Legal Issues
Abuse
Suicide Analysis
FAPDMSS G1 DMDC CID
Data objects complete?
All sources identified?
Best source for each object?
How reconcile differences between sources?
MDR
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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
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Communication Patterns
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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
Vocabulary is Important-Tank, Tanks, Tankers, Tanked
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How one inventory item proliferates data throughout the chain
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555 Subassemblies & subcomponents
17,659 Repair parts or Consumables
System 1:18,214 Total items
75 Attributes/ item1,366,050 Total attributes
System 2 47 Total items
15+ Attributes/item720 Total attributes
System 3 16,594 Total items 73 Attributes/item
1,211,362 Total attributes
System 4 8,535 Total items
16 Attributes/item136,560 Total attributes
System 5 15,959 Total items
22 Attributes/item351,098 Total attributes
Total for the five systems show above:59,350 Items
179 Unique attributes3,065,790 values
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
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Spreadsheet Interpretation
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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.
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Example of Poor Data GovernanceMizuho 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
83Copyright 2014 by Data Blueprint
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).
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Seven Sisters (from British Telecom)
http://www.datablueprint.com/thought-leaders/peter-aiken/book-monetizing-data-management/ [Thanks to Dave Evans]
Copyright 2013 by Data Blueprint 85
86Copyright 2015 by Data Blueprint
• 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
Data Governance Strategies
Tweeting now: #dataed
87Copyright 2015 by Data Blueprint
• 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
Data Governance Strategies
Tweeting now: #dataed
Maslow's Hierarchy of Needs
88Copyright 2014 by Data Blueprint
You can accomplish Advanced Data Practices without becoming proficient in the Foundational Data Management Practices however this will: • Take longer • Cost more • Deliver less • Present
greaterrisk(with thanks to Tom DeMarco)
Data Management Practices Hierarchy
Advanced Data
Practices • MDM • Mining • Big Data • Analytics • Warehousing • SOA
Foundational Data Management Practices
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2015by Data Blueprint
Data Platform/Architecture
Data Governance Data Quality
Data Operations
Data Management Strategy
Technologies
Capabilities
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
90Copyright 2014 by Data Blueprint
The File Naming Convention Committee's Output
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Data Governance Council Hotel
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PETER AIKEN WITH JUANITA BILLINGSFOREWORD BY JOHN BOTTEGA
MONETIZINGDATA MANAGEMENT
Unlocking the Value in Your Organization’s
Most Important Asset.
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
94Copyright 2014 by Data Blueprint
Source: “Data Governance Checklist for Educators” by Angela Guess; http://www.dataversity.net/archives/5198
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
95Copyright 2014 by Data Blueprint
Source: “Data Governance Checklist for Educators” by Angela Guess; http://www.dataversity.net/archives/5198
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
96Copyright 2014 by Data Blueprint
Source: “Data Governance Worst Practices” by Angela Guess; http://www.dataversity.net/archives/4895
Supplemental: 10 DG Worst Practices in Detail3. 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
97Copyright 2014 by Data Blueprint
Source: “Data Governance Worst Practices” by Angela Guess; http://www.dataversity.net/archives/4895
Source: “Data Governance Worst Practices” by Angela Guess; http://www.dataversity.net/archives/4895
Supplemental: 10 DG Worst Practices in Detail5. 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
98Copyright 2014 by Data Blueprint
Source: “Data Governance Worst Practices” by Angela Guess; http://www.dataversity.net/archives/4895Source: “Data Governance Worst Practices” by Angela Guess; http://www.dataversity.net/archives/4895
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
99Copyright 2014 by Data Blueprint
Source: “Data Governance Worst Practices” by Angela Guess; http://www.dataversity.net/archives/4895
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
100Copyright 2014 by Data Blueprint
Source: “Data Governance Worst Practices” by Angela Guess; http://www.dataversity.net/archives/4895
ReferencesWebsites
• Data Governance Book
Data Governance Book
Compliance Book
101Copyright 2014 by Data Blueprint
IT Governance Books
102Copyright 2014 by Data Blueprint
Interdependencies
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Data Governance
Master Data Data Quality
makes the case and is
responsible for
is a necessary but insufficient prerequisite
to success
MD capabilities constrain governance
effectiveness
Upcoming EventsMay Webinar: Monetizing Data Management May 12, 2015 @ 2:00 PM ET
June Webinar: Go Small before going Big (Data) Subtitle: A Framework for Implementing NoSQL, Hadoop June 9, 2015 @ 2:00 PM ET
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