Data-Ed Online - Making the Case for Data Governance

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Welcome!
Making the Case for Data
Governance

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

PRODUCED BY CLASSIFICATION DATE SLIDE

DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 01/24/12 1
© Copyright this and previous years by Data Blueprint - all rights reserved!

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Meet Your Presenter: Dr. Peter Aiken
• 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


Making the Case
for Data
Governance

Dr. Peter Aiken: Making the Case for Data Governance
DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 01/24/12

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Making the Case for Data Governance
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
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

Data Management Functions


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The DAMA Guide to the Data Management Body of Knowledge

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

Environmental Elements


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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
Manage data coherently.

Data Program
Coordination
Share data across boundaries.
Organizational
Data Integration

Data Stewardship Data Development

Assign responsibilities for data.
Engineer data delivery systems.

Data Support
Operations

Maintain data availability.



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Outline
• Why is Data Governance
Important?
Governance
Checklists
• Data Governance in Action: Examples
• 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
• 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)

Assess context Execute plan

Define DG roadmap Evaluate results

Secure executive mandate Revise plan

Apply change management
Assign Data Stewards
(Occurs once) (Repeats)


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Data Governance from the DMBOK



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Data Governance from the DMBOK

Organizational Strategy Formulation/Implementation

Data Security Planning/Implementation

Operational Data Delivery Performance

Data Quality/Inventory Management

Decision Making Needs



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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
management solutions to
business challenges



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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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Outline
Governance
Checklists
Examples
• Q&A


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

1. Accessible
2. Secure
3. Consistent
4. High quality
5. Auditable

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’d
1. Accessible 4. High Quality
• Can the people who need it • Is the data accurate?
access the data they need? • Has it been conformed to meet
• Does the data match the format agreed standards
the user requires?
5. Auditable
2. Secure • Where did the data come from?
• Are authorized people the only • Is the lineage clear?
ones who can access the data? • Does IT know who is using it and
• Are non-authorized users for what purpose?
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?

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


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Outline
Governance
Checklists
Examples
• Q&A


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Data Governance Frameworks
• A system of ideas
for guiding analyses
• A means of
organizing project
data
• Data integration ™

priorities decision
Names Names
Where Why
Names
C o m p o s i t e I n t e g r a t i o n s A l i g n m e n t C o m p o s i t e I n t e g r a t i o n s

A A
Executive l
i Products Forecast Sales Material Supply Ntwk General Mgmt Product Cycle New Markets
l
i Scope
g g
Contexts
Product Types Plan Production Product Dist. Ntwk Product Mgmt Market Cycle Revenue Growth

Perspective
Sell Products Voice Comm. Ntwk Engineering Design Planning Cycle Expns Reduction

n Parts Bins
Customers
Take Orders
Train Employees
Data Comm. Ntwk
Manu. Process Ntwk
Manu. Engineering
Accounting
Order Cycle
Employee Cycle
Cust Convenience
Customer Satis. n
m
Territories Assign Territories Finance Maint. Cycle Regulatory Comp.
m Orders
Employees
Develop Markets
Maintain Facilities
Parts Dist. Ntwk
Personnel Dist. Ntwk
Transportation
Distribution
Production Cycle
Sales Cycle
New Capital
Social Contribution
e e.g.
Vehicles
Accounts e.g.
Repair Products
Record Transctns e.g.
etc., etc.
e.g.
Marketing
Sales e.g.
Economic Cycle
Accounting Cycle e.g.
Increased Yield
Increased Quality e
n n
t t
T
List: Inventory Types List: Process Types List: Distribution Types List: Responsibility Types List: Timing Types List: Motivation Types T
r r
a a
n n
s s
Business Mgmt f f Business

making framework
e.g.: primitive e.g.: composite model:
o model: o
e.g. e.g. e.g. e.g. e.g. e.g.
Perspective r
m
r
m Concepts
a a
t t
i Business Entity Business Transform Business Location Business Role Business Interval Business End i
o o
n Business Relationship Business Input/Output Business Connection Business Work Product Business Moment Business Means n
s s

Architect e.g. e.g. e.g. e.g. e.g. e.g.
System
Perspective Logic
System Entity System Transform System Location System Role System Interval System End
System Relationship System Input /Output System Connection System Work Product System Moment System Means

• A means of
Engineer e.g. e.g. e.g. e.g. e.g. e.g. Technology
Perspective Physics
Technology Entity Technology Transform Technology Location Technology Role Technology Interval Technology End
Technology Relationship Technology Input /Output Technology Connection Technology Work Product Technology Moment Technology Means

A A
l l
Technician i
g
e.g. e.g. e.g. e.g. e.g. e.g. i
g Tool
Perspective n
m
n
m
e
Components
e
n n
t t
Tool Entity Tool Transform Tool Location Tool Role Tool Interval Tool End
Tool Relationship Tool Input /Output Tool Connection Tool Work Product Tool Moment Tool Means

assessing progress
T T
r r
a a
n n
Enterprise s
f Inventory Process Distribution Responsibility Timing Motivation s
f Operations
Perspective o
r
Instantiations Instantiations Instantiations Instantiations Instantiations Instantiations o
r Instances
m m
a a
t t
The i
o
i
o The
Enterprise n
s
n
s Enterprise
C o m p o s i t e I n t e g r a t i o n s A l i g n m e n t C o m p o s i t e I n t e g r a t i o n s
*Horizontal integration lines
are shown for example purposes
only and are not a complete set.
Composite, integrative rela-
tionships connecting every cell
Names horizontally potentially exist.

© 1987-2011 John A. Zachman, all rights reserved. Zachman® and Zachman International® are registered trademarks of John A. Zachman



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Data Governance Institute


DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 01/24/12
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-‐
all
rights
reserved!
http://www.datagovernance.com/
©

Copyright
this
and
previous
years
by
Data
Blueprint

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KiK Consulting

http://www.kikconsulting.com/

8

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


8

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


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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.”
• The five page paper offers a number of suggestions for
implementing a successful data governance program that can
be applied to a variety of business models beyond education.
• For more information, please visit the Privacy Technical
Assistance Center: http://ed.gov/ptac

Source: “Data Governance Checklist for Educators” by Angela Guess; http://www.dataversity.net/archives/5198


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Data Governance Checklist, cont’d
1. Decision-Making Authority
2. Standard Policies and
Procedures
3. Data Inventories
4. Data Content Management
5. Data Records Management
6. Data Quality
7. Data Access
8. Data Security and Risk
Management



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1. 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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4. 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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7. 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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Outline
Governance
Checklists
Examples
• Q&A


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Example of Poor Data Governance
Mizuho Securities Example
• Wanted to sell 1 share for
600,000 yen
• Sold 600,000 shares for 1 CLUMSY typing cost a Japanese bank
yen at least £128 million and staff their
• $347 million loss Christmas bonuses yesterday, after a
trader mistakenly sold 600,000 more
• In-house system did not have shares than he should have. The
limit checking trader at Mizuho Securities, who has
not been named, fell foul of what is
• Tokyo stock exchange known in financial circles as “fat finger
system did not have limit syndrome” where a dealer types
checking incorrect details into his computer. He
wanted to sell one share in a new
• And doesn't allow order
telecoms company called J Com, for
cancellations 600,000 yen (about £3,000).



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Largely Ineffective DM Investments
• 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 Practices
1. Buy-in but not committing
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”
Source: “Data Governance Worst Practices” by Angela Guess; http://www.dataversity.net/archives/4895


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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


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10 DG Worst Practices in Detail, cont’d
3. Trying to Solve World Hunger or Boil the Ocean
• Trap 1: Trying to solve all organizational data problems in initial
project phase
• Trap 2: Starting with biggest data problems (highly political
issues)
• Almost impossible to establish a DG program while tacking
data problems that have taken years to build up
• Instead: “Think globally and act locally”: break data problems
down into incremental deliverables
• “Too big too fast” = Recipe for disaster
4. The Goldilocks Syndrome
• Encountering things that are either one extreme or another
• Either the program is too high-level and substantive issues are
never dealt with or it attempts to create definitions and rules for
every field and table
• Need to find happy compromise that enables DG initiatives to
create real business value


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5. Committee Overload
• Good: People of various business units and departments get
involved in the governance process
• Bad: more people -> more politics -> more watered down
governance responsibilities
• To be successful, limit committee sizes to 6-12 people and
ensure that members have decision-making authority

6. Failure to Implement
• DG efforts won’t produce any business value if data definitions,
business rules and KPIs are created but not used in any
processes
• Governance process needs to be a complete feedback loop in
which data is defined, monitored, acted upon, and changed
when appropriate
• Also important: Establish ongoing communication about
governance to prevent business users going back to their old
habits


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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



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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



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Outline
Governance
Checklists
Examples
• Q&A


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Data Governance Building Blocks

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


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