Data-Ed Online - Making the Case for Data Governance

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01/24/12© Copyright this and previous years by Data Blueprint - all rights reserved!

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Welcome!

Date: January 24, 2012Time: 2:00 PM ETPresenter: Dr. Peter Aiken

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Making the Case for Data Governance



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Meet Your Presenter: Dr. Peter Aiken

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• Internationally recognized thought-leader in the data management field with more than 30 years of experience

• Recipient of the 2010 International Stevens Award

• Founding Director of Data Blueprint (http://datablueprint.com)

• Associate Professor of Information Systems at Virginia Commonwealth University (http://vcu.edu)

• President of DAMA International (http://dama.org)

• DoD Computer Scientist, Reverse Engineering Program Manager/Office of the Chief Information Officer

• Visiting Scientist, Software Engineering Institute/Carnegie Mellon University

• 7 books and dozens of articles• Experienced w/ 500+ data management practices in 20 countries

01/24/12DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION

Making the Case for Data

Governance

Dr. Peter Aiken: Making the Case for Data Governance



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Making the Case for Data Governance

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When thinking about data management, data governance is not one of those topics that immediately come to mind. Although neglected and often poorly performed, it is a vital function of data management and it is absurd to even consider managing data without some form of formal guidance. Data governance is central to “defining, coordinating, resourcing, implementing, and monitoring organizational data program strategies, policies, and plans as a coherent set of activities.”

This presentation provides you with a clear and concise 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 and confusion that often surround initial discussions and implement effective data governance and stewardship programs that manage data in support of organizational strategy.



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Outline• Data Management Overview• What is Data Governance?• Why is Data Governance Important?• 5 Requirements for Effective Data

Governance• Data Governance Frameworks &

Checklists • Data Governance Worst Practices• Data Governance Building Blocks• Data Governance in Action:

Examples• Take Aways & References• Q&A

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The DAMA Guide to the Data Management Body of Knowledge

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Data Management Functions

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

• Organized around several environmental elements



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The DAMA Guide to the Data Management Body of Knowledge

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Environmental Elements

Amazon:http://www.amazon.com/DAMA-Guide-Management-Knowledge-DAMA-DMBOK/dp/0977140083Or enter the terms "dama dm bok" at the Amazon search engine



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What is the 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

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Data Management

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Data Management

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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



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Outline• Data Management Overview• What is Data Governance?• Why is Data Governance

Important?• 5 Requirements for Effective Data


Checklists • Data Governance Worst Practices• Data Governance Building Blocks• Data Governance in Action: Examples• Take Aways & References• Q&A

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Data Governance – Various Definitions• 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

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• 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

• The exercise of authority and control over the management of data assets – DM BoK



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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

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Governance Umbrella Components•Integrity, Accountability, Transparency•Strategy alignment•Standardization through processes and procedures •Organizational change management (education/knowledge transfer)•Data architecture (integration, development)•Stewardship/Quality•Protection (security, backup, BCP/DR, media catalog)(Note: Governance, change management and optimization are perpetual)

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Assess context

Define DG roadmap

Secure executive mandate

Assign Data Stewards

Execute plan

Evaluate results

Revise plan

Apply change management

(Occurs once) (Repeats)



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Data Governance from the DMBOK

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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



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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 management solutions to business challenges

What is the Difference Between DG and DM?

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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

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5 Requirements for Effective DGData governance is a set of well-defined policies and practices designed to ensure that data is:

1. Accessible2. Secure3. Consistent4. High quality5. Auditable

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Source: “5 Steps to Effective Data Governance” by Angela Guess; http://www.dataversity.net/archives/5160



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5 Requirements for Effective DG, cont’d1. 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?

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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?

Source: “5 Steps to Effective Data Governance” by Angela Guess; http://www.dataversity.net/archives/5160



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Names

Names

*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.Names

Names

© 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

A l i g n m e n t

A l i g n m e n t

Where Why

Operations

Instances

TheEnterprise

TheEnterprise

Enterprise

Perspective

Executive

Perspective

Business Mgmt

Perspective

Architect

Perspective

Engineer

Perspective

Technician

Perspective

Scope

Contexts

Business

Concepts

System

Logic

Technology

Physics

Tool

Components

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

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

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.

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



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Data Governance Frameworks

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• 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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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/http://www.datagovernance.com/



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KiK Consulting

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http://www.kikconsulting.com/



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IBM Data Governance Council

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http://www-01.ibm.com/software/data/system-z/data-governance/workshops.html



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Data Governance from the DM BoK

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Illustration from The DAMA Guide to the Data Management Body of Knowledge p. 37 © 2009 by DAMA International



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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.”

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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



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Data Governance Checklist, cont’d

1. Decision-Making Authority2. Standard Policies and

Procedures3. Data Inventories4. Data Content Management5. Data Records Management6. Data Quality7. Data Access8. Data Security and Risk

Management

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Data Governance Checklist, cont’d1. Decision-Making Authority

• Assign appropriate levels of authority to data stewards• Proactively define scope and limitations of that authority

2. 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

3. 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

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Data Governance Checklist, cont’d4. 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

5. 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

6. 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

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Data Governance Checklist, cont’d7. 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

8. 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

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Example of Poor Data Governance

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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).



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Largely Ineffective DM Investments

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• 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.



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10 Data Governance Worst Practices1. Buy-in but not committing2. Ready, fire, aim3. Trying to solve world hunger or boil

the ocean4. The Goldilocks syndrome5. Committee overload6. Failure to implement7. Not dealing with change management8. Assuming that technology alone is

the answer9. Not building sustainable and ongoing

processes10. Ignoring “data shadow systems”

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Source: “Data Governance Worst Practices” by Angela Guess; http://www.dataversity.net/archives/4895



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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

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10 DG Worst Practices in Detail, cont’d3. 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 disaster4. 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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10 DG Worst Practices in Detail, cont’d5. 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 their old habits

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10 DG Worst Practices in Detail, cont’d7. 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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10 DG Worst Practices in Detail, cont’d9. 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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Data Governance Building Blocks

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from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International



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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.

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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

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Data Governance Primary Deliverables• Data Policies

• Data Standards

• Resolved Issues

• Data Management Projects and Services

• Quality Data and Information

• Recognized Data Value

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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

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Data Governance Technologies• Intranet Website

• E-Mail

• Metadata Tools

• Metadata Repository

• Issue Management Tools

• Data Governance KPI Dashboard

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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

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Data Governance Examples

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SUBJECT: Data Sharing in a Net-Centric Department of Defense• "It is DoD policy that:• Data is an essential enabler of network-centric warfare (NCW) … • Data assets shall be made visible by creating and associating metadata … • Data assets shall be made accessible by making data available in shared spaces

… • Data assets shall be made understandable by publishing associated semantic and

structural metadata in a federated DoD metadata registry … "



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Data Governance Examples, cont’d

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Formalizing the Role of U.S. Army IT Governance/Compliance



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Formalizing Governance Responsibilities

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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

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ReferencesWebsites

• Data Governance Book

Data Governance Book

Compliance Book

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IT Governance Books

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Questions?

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It’s your turn! Use the chat feature to submit your questions to Peter now.

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February Webinar:Building a Solid Foundation: Data/Information ArchitectureFebruary 14, 2012 @ 2:00 PM ET

March Webinar:A Practical Approach to Data ModelingMarch 13, 2012 @ 2:00 PM ET

Sign up here:• www.datablueprint.com/webinar-schedule • www.Dataversity.net

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