Copyright 2013 by Data Blueprint
Unlock Business Value through Data Governance
1
• If your organization understands your function, they see you as an investment. If your organization does not understand what you do, they are likely to perceive you as a cost. The goal of this webinar is to provide you with concrete ideas for how to reinforce the first mindset at your organization. Success stories must be used to ensure continued organizational support. 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. For example: using specific common terms (and narratives) when referencing organizational mishaps, e.g. The Chocolate Story.
Copyright 2013 by Data Blueprint
Unlock Business Value through Data Governance
Date: April 9, 2013Time: 2:00 PM ETPresented by: Peter Aiken, PhD
2
If your organization understands your function, they see you as an investment. If your organization does not understand what you do, they are likely to perceive you as a cost. The goal of this webinar is to provide you with concrete ideas for how to reinforce the first mindset at your organization. Success stories must be used to ensure continued organizational support. 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. For example: using specific common terms (and narratives) when referencing organizational mishaps, e.g. The Chocolate Story.
Copyright 2013 by Data Blueprint
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?
3
Copyright 2013 by Data Blueprint
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Copyright 2013 by Data Blueprint
Meet Your Presenter: Peter Aiken, Ph.D.
• Internationally recognized thought-leader in the data management field - 30 years of experience– Recipient of multiple international awards– Founder, Data Blueprint – 7 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, Deutsche Bank, Nokia, Wells Fargo, and the Commonwealth of Virginia
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• #nowthatcherisdead
• #now thatcher is dead
• #now that cher is dead
• #now t hatcher is dead
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Motivation
• Context: What is Data Management/DAMA/DM BoK/CDMP?
• What is Data Governance and why is it Important?– Organizational -> IT -> Data– Requirements for Effective Data
Governance• Data Governance
– Frameworks– Checklists – Worst Practices– Building Blocks
• Data Governance in Action:– Securities example– Retail example
• Take Aways/References/Q&A
Unlock Business Value through Data Governance
Copyright 2013 by Data Blueprint
Tweeting now: #dataed
9
• Context: What is Data Management/DAMA/DM BoK/CDMP?
• What is Data Governance and why is it Important?– Organiza*onal -‐> IT -‐> Data– Requirements for Effec*ve Data Governance
• Data Governance – Frameworks– Checklists – Worst Prac*ces– Building Blocks
• Data Governance in Action:– Securi*es example– Retail example
• Take Aways/References/Q&A
• Context: What is Data Management/DAMA/DM BoK/CDMP?
• What is Data Governance and why is it Important?– Organizational -> IT -> Data– Requirements for Effective Data
Governance• Data Governance
– Frameworks– Checklists – Worst Practices– Building Blocks
• Data Governance in Action:– Securities example– Retail example
• Take Aways/References/Q&A
Unlock Business Value through Data Governance
Copyright 2013 by Data Blueprint
Tweeting now: #dataed
10
Copyright 2013 by Data Blueprint
Data Management is an Integrated System of Five Practice Areas
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#dataed
Copyright 2013 by Data Blueprint
Five Integrated DM Practices
12
Manage data coherently.
Share data across boundaries.
Assign responsibilities for data.Engineer data delivery systems.
Maintain data availability.
Data Program Coordination
Organizational Data Integration
Data Stewardship Data Development
Data Support Operations
#dataed
Copyright 2013 by Data Blueprint
• 5 Data Management Practices Areas / Data Management Basics
• Are necessary but insufficient prerequisites to organizational data leveraging applications (that is Self Actualizing Data or Advanced Data Practices)
Data Management Practices Hierarchy (after Maslow)
Basic Data Management 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
Advanced Data Practices• Cloud• MDM• Mining• Analytics• Warehousing• Big
• 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
Copyright 2013 by Data Blueprint
DAMA DM BoK & CDMP
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#dataed
Data Management Func-ons
• Context: What is Data Management/DAMA/DM BoK/CDMP?
• What is Data Governance and why is it Important?– Organizational -> IT -> Data– Requirements for Effective Data
Governance• Data Governance
– Frameworks– Checklists – Worst Practices– Building Blocks
• Data Governance in Action:– Securities example– Retail example
• Take Aways/References/Q&A
Unlock Business Value through Data Governance
Copyright 2013 by Data Blueprint
Tweeting now: #dataed
15
• Context: What is Data Management/DAMA/DM BoK/CDMP?
• What is Data Governance and why is it Important?– Organizational -> IT -> Data– Requirements for Effective Data
Governance• Data Governance
– Frameworks– Checklists – Worst Practices– Building Blocks
• Data Governance in Action:– Securities example– Retail example
• Take Aways/References/Q&A
Unlock Business Value through Data Governance
Copyright 2013 by Data Blueprint
Tweeting now: #dataed
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Copyright 2013 by Data Blueprint
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Data Strategy in Context
Organiza)onal
IT Strategy
Data StrategyOnly 1 is 10 organiza/ons has a board approved data
strategy!
Copyright 2013 by Data Blueprint
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.
18
Copyright 2013 by Data Blueprint
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)
According to the IT Governance Institute, there are five areas of focus: • Strategic Alignment• Value Delivery• Resource Management• Risk Management• Performance Measures
19
Copyright 2013 by Data Blueprint
No clear connection exists between to business priorities and IT initiatives
20
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
Copyright 2013 by Data Blueprint
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
21
Copyright 2013 by Data Blueprint
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
22
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Data Governance from the DMBOK
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Decision Making Needs
Data Quality/Inventory Management
Organizational Strategy Formulation/Implementation
Operational Data Delivery Performance
Data Security Planning/Implementation
Copyright 2013 by Data Blueprint
What is the Difference Between DG and DM?
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• 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
Copyright 2013 by Data Blueprint
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
26
Copyright 2013 by Data Blueprint
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?
27
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
• Context: What is Data Management/DAMA/DM BoK/CDMP?
• What is Data Governance and why is it Important?– Organizational -> IT -> Data– Requirements for Effective Data
Governance• Data Governance
– Frameworks– Checklists – Worst Practices– Building Blocks
• Data Governance in Action:– Securities example– Retail example
• Take Aways/References/Q&A
Unlock Business Value through Data Governance
Copyright 2013 by Data Blueprint
Tweeting now: #dataed
28
• Context: What is Data Management/DAMA/DM BoK/CDMP?
• What is Data Governance and why is it Important?– Organizational -> IT -> Data– Requirements for Effective Data
Governance• Data Governance
– Frameworks– Checklists – Worst Practices– Building Blocks
• Data Governance in Action:– Securities example– Retail example
• Take Aways/References/Q&A
Unlock Business Value through Data Governance
Copyright 2013 by Data Blueprint
Tweeting now: #dataed
29
Copyright 2013 by Data Blueprint
Getting Started
30
Assess context
Define DG roadmap
Secure executive mandate
Assign Data Stewards
Execute plan
Evaluate results
Revise plan
Apply change management
(Occurs once) (Repeats)
ClassificationNames
ModelNames
*Horizontal integration lines are shown for example purposes only and are not a complete set. Composite, integrative rela-tionships connecting every cell horizontally potentially exist.
AudiencePerspectives
EnterpriseNames
ClassificationNames
AudiencePerspectives
© 1987-2011 John A. Zachman, all rights reserved. Zachman® and Zachman International® are registered trademarks of John A. Zachman
™
C o m p o s i t e I n t e g r a t i o n s
Alignment
Transformations
C o m p o s i t e I n t e g r a t i o n s
Alignment
Transformations
C o m p o s i t e I n t e g r a t i o n s C o m p o s i t e I n t e g r a t i o n s
Alignment
Transformations
Alignment
Transformations
Version 3.0
A l i g n m e n t
A l i g n m e n t
How Where Who WhenWhat Why
ProcessFlows
DistributionNetworks
ResponsibilityAssignments
TimingCycles
InventorySets
MotivationIntentions
Operations
Instances
(Implementations)
TheEnterprise
TheEnterprise
Enterprise
Perspective
(Users)
Executive
Perspective
(Business ContextPlanners)
Business Mgmt
Perspective
(Business Concept Owners)
Architect
Perspective
(Business LogicDesigners)
Engineer
Perspective
(Business Physics Builders)
Technician
Perspective
(Business ComponentImplementers)
Scope
Contexts
(Scope Identification Lists)
Business
Concepts
(Business Definition Models)
System
Logic
(SystemRepresentation Models)
Technology
Physics
(TechnologySpecification Models)
Tool
Components
(Tool Configuration Models)
e.g. e.g. e.g. e.g. e.g. e.g.
e.g. e.g. e.g. e.g. e.g. e.g.
e.g. e.g. e.g. e.g. e.g. e.g.
e.g. e.g. e.g. e.g. e.g. e.g.e.g.: primitive e.g.: composite model:
model:
Forecast Sales
Plan Production
Sell Products
Take Orders
Train Employees
Assign Territories
Develop Markets
Maintain Facilities
Repair Products
Record Transctns
Material Supply Ntwk
Product Dist. Ntwk
Voice Comm. Ntwk
Data Comm. Ntwk
Manu. Process Ntwk
2I¿FH�:UN�)ORZ�1WZN
Parts Dist. Ntwk
Personnel Dist. Ntwk
etc., etc.
General Mgmt
Product Mgmt
Engineering Design
Manu. Engineering
Accounting
Finance
Transportation
Distribution
Marketing
Sales
Product Cycle
Market Cycle
Planning Cycle
Order Cycle
Employee Cycle
Maint. Cycle
Production Cycle
Sales Cycle
Economic Cycle
Accounting Cycle
Products
Product Types
:DUHKRXVHV
Parts Bins
Customers
Territories
Orders
Employees
Vehicles
Accounts
New Markets
Revenue Growth
Expns Reduction
Cust Convenience
Customer Satis.
Regulatory Comp.
New Capital
Social Contribution
Increased Yield
Increased Qualitye.g. e.g. e.g. e.g. e.g. e.g.
Operations TransformsOperations In/Outputs
Operations LocationsOperations Connections
Operations RolesOperations Work Products
Operations IntervalsOperations Moments
Operations EntitiesOperations Relationships
Operations EndsOperations Means
Process
Instantiations
Distribution
Instantiations
Responsibility
Instantiations
Timing
Instantiations
Inventory
Instantiations
Motivation
Instantiations
List: Timing Types
Business IntervalBusiness Moment
List: Responsibility Types
Business RoleBusiness Work Product
List: Distribution Types
Business LocationBusiness Connection
List: Process Types
Business TransformBusiness Input/Output
System TransformSystem Input /Output
System LocationSystem Connection
System RoleSystem Work Product
System IntervalSystem Moment
Technology TransformTechnology Input /Output
Technology LocationTechnology Connection
Technology RoleTechnology Work Product
Technology IntervalTechnology Moment
Tool TransformTool Input /Output
Tool LocationTool Connection
Tool RoleTool Work Product
Tool IntervalTool Moment
List: Inventory Types
Business EntityBusiness Relationship
System EntitySystem Relationship
Technology EntityTechnology Relationship
Tool EntityTool Relationship
List: Motivation Types
Business EndBusiness Means
System EndSystem Means
Technology EndTechnology Means
Tool EndTool Means
Timing IdentificationResponsibility IdentificationDistribution IdentificationProcess Identification
Timing DefinitionResponsibility DefinitionDistribution DefinitionProcess Definition
Process Representation Distribution Representation Responsibility Representation Timing Representation
Process Specification Distribution Specification Responsibility Specification Timing Specification
Inventory Identification
Inventory Definition
Inventory Representation
Inventory Specification
Inventory Configuration Process Configuration Distribution Configuration Responsibility Configuration Timing Configuration
Motivation Identification
Motivation Definition
Motivation Representation
Motivation Specification
Motivation Configuration
Copyright 2013 by Data Blueprint
Data Governance Frameworks
31
• A system of ideas for guiding analyses
• A means of organizing project data
• Data integration priorities decision making framework
• A means of assessing progress
Copyright 2013 by Data Blueprint
Data Governance Institute
-‐ datablueprint.com 1/26/2010 © Copyright this and previous years by Data Blueprint -‐ all rights reserved!8 http://www.datagovernance.com/http://www.datagovernance.com/http://www.datagovernance.com/http://www.datagovernance.com/
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IBM Data Governance Council
88
http://www-01.ibm.com/software/data/system-z/data-governance/workshops.html
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Elements of Effective Data Governance
88
See IBM Data Governance Council, http://www-01.ibm.com/software/tivoli/ governance/servicemanagement/ data-governance.html.
Copyright 2013 by Data Blueprint
Data Governance from the DM BoK
1313
Illustration from The DAMA Guide to the Data Management Body of Knowledge p. 37 © 2009 by DAMA International
Copyright 2013 by Data Blueprint
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.”
40
Source: “Data Governance Checklist for Educators” by Angela Guess; http://www.dataversity.net/archives/5198
• 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
Copyright 2013 by Data Blueprint
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
41
Source: “Data Governance Checklist for Educators” by Angela Guess; http://www.dataversity.net/archives/5198
Copyright 2013 by Data Blueprint
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
42Source: “Data Governance Checklist for Educators” by Angela Guess; http://www.dataversity.net/archives/5198
Copyright 2013 by Data Blueprint
Largely Ineffective DG Investments
43
• Approximately, 10% percent of organizations achieve parity and (potential positive returns) on their DM investments.
• Only 30% of DM investments achieve tangible returns at all.
• Seventy percent of organizations have very small or no tangible return on their DM investments.
Copyright 2013 by Data Blueprint
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.
44
from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
• 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
Copyright 2013 by Data Blueprint
Data Governance Activities
45
from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
Copyright 2013 by Data Blueprint
Data Governance Primary Deliverables• Data Policies
• Data Standards
• Resolved Issues
• Data Management Projects and Services
• Quality Data and Information
• Recognized Data Value46
from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
Copyright 2013 by Data Blueprint
Data Governance Roles and ResponsibilitiesParticipants:• 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
47
from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
Copyright 2013 by Data Blueprint
Data Governance Technologies• Intranet Website
• Metadata Tools
• Metadata Repository
• Issue Management Tools
• Data Governance KPI Dashboard48
from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
Copyright 2013 by Data Blueprint
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
49
from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
• Context: What is Data Management/DAMA/DM BoK/CDMP?
• What is Data Governance and why is it Important?– Organizational -> IT -> Data– Requirements for Effective Data
Governance• Data Governance
– Frameworks– Checklists – Worst Practices– Building Blocks
• Data Governance in Action:– Securities example– Retail example
• Take Aways/References/Q&A
Unlock Business Value through Data Governance
Copyright 2013 by Data Blueprint
Tweeting now: #dataed
50
• Context: What is Data Management/DAMA/DM BoK/CDMP?
• What is Data Governance and why is it Important?– Organizational -> IT -> Data– Requirements for Effective Data
Governance• Data Governance
– Frameworks– Checklists – Worst Practices– Building Blocks
• Data Governance in Action:– Securities example– Retail example
• Take Aways/References/Q&A
Unlock Business Value through Data Governance
Copyright 2013 by Data Blueprint
Tweeting now: #dataed
51
Copyright 2013 by Data Blueprint
Data Governance Examples, cont’d
52
Formalizing the Role of U.S. Army IT Governance/Compliance
Suicide MitigationData 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
Copyright 2013 by Data Blueprint
54
Copyright 2013 by Data Blueprint
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
55
Copyright 2013 by Data Blueprint
Communication Patterns
56Source: 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
Copyright 2013 by Data Blueprint
Example of Poor Data Governance
57
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).
Copyright 2013 by Data Blueprint
Diaper Story
58
Old New
Shipping Semi BestTerms 2/10 net 30 ?Turns 5 50Risks same JIT
• Context: What is Data Management/DAMA/DM BoK/CDMP?
• What is Data Governance and why is it Important?– Organizational -> IT -> Data– Requirements for Effective Data
Governance• Data Governance
– Frameworks– Checklists – Worst Practices– Building Blocks
• Data Governance in Action:– Securities example– Retail example
• Take Aways/References/Q&A
Unlock Business Value through Data Governance
Copyright 2013 by Data Blueprint
Tweeting now: #dataed
59
• Context: What is Data Management/DAMA/DM BoK/CDMP?
• What is Data Governance and why is it Important?– Organizational -> IT -> Data– Requirements for Effective Data
Governance• Data Governance
– Frameworks– Checklists – Worst Practices– Building Blocks
• Data Governance in Action:– Securities example– Retail example
• Take Aways/References/Q&A
Unlock Business Value through Data Governance
Copyright 2013 by Data Blueprint
Tweeting now: #dataed
60
Copyright 2013 by Data Blueprint
Take Aways• Need for DG is increasing• DG is a new discipline
– Must conform to constraints– No one best way
• Comparing DG frameworks can be useful• DG directs data management efforts• DG interacts directly and indirectly with the
organization• Process improvement can improve DG
practices
61
Copyright 2013 by Data Blueprint
10 DG Worst Practices in Detail1. 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
62Source: “Data Governance Worst Practices” by Angela Guess; http://www.dataversity.net/archives/4895
Copyright 2013 by Data Blueprint
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
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Source: “Data Governance Worst Practices” by Angela Guess; http://www.dataversity.net/archives/4895
Copyright 2013 by Data Blueprint
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
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Source: “Data Governance Worst Practices” by Angela Guess; http://www.dataversity.net/archives/4895
Copyright 2013 by Data Blueprint
10 DG Worst Practices in Detail7. 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
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Source: “Data Governance Worst Practices” by Angela Guess; http://www.dataversity.net/archives/4895
Copyright 2013 by Data Blueprint
10 DG Worst Practices in Detail9. 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
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Source: “Data Governance Worst Practices” by Angela Guess; http://www.dataversity.net/archives/4895
Copyright 2013 by Data Blueprint
ReferencesWebsites
• Data Governance Book
Data Governance Book
Compliance Book
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Copyright 2013 by Data Blueprint
Questions?
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