Data-Ed Webinar: Data Governance Strategies

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
Copyright 2016 by Data Blueprint Slide # 1
Peter Aiken, Ph.D.
• 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 (DISA/Army/Marines/DLA)
– 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 BILLINGS
FOREWORD BY JOHN BOTTEGA
MONETIZING
DATA MANAGEMENT
Unlocking the Value in Your Organization’s
Most Important Asset.
The Case for the
Chief Data Officer
Recasting the C-Suite to Leverage
Your MostValuable Asset
Peter Aiken and
Michael Gorman
2Copyright 2016 by Data Blueprint Slide #

We believe ...
Data  
Assets
Financial  
Assets
Real 
Estate Assets
Inventory
Assets
Non-
depletable
Available for
subsequent
use
Can be  
used up
Can be  
used up
Non-
degrading √ √ Can degrade 
over time
Can degrade 
over time
Durable Non-taxed √ √
Strategic
Asset √ √ √ √
• Today, data is the most powerful, yet underutilized and poorly
managed organizational asset
• Data is your
– Sole
– Non-depletable
– Non-degrading
– Durable
– Strategic
• Asset
– Data is the new oil!
– Data is the new (s)oil!
– Data is the new chocolate!
• 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]
Data Assets Win!
Welcome: Data Governance Strategies
• Date: April 12, 2016
• 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 data 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

Managing Data with Guidance?
Lewis in front of the cummins safe

!
Beth Jacobs abruptly  
resigned in March
These decisions have consequences!
Why is Data Governance important?
• Cost organizations
millions each year in
– Productivity
– Redundant and siloed
efforts
– Poorly thought out
hardware and software
purchases
– Delayed decision
making using
inadequate information
– Reactive instead of
proactive initiatives
– 20-40% of IT spending
can be reduced through
better data governance

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
The DAMA Guide to the Data Management Body of Knowledge
• Published by
DAMA
International
– The professional
association for
Data Managers (40
chapters
worldwide)
• DM BoK organized
around
– Primary data
management
functions focused
around data
delivery to the
organization
– Organized around
several
environmental
elements
Data  
Management
Functions

Data Governance from the DMBOK
from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
• 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 Strategy in Action (Storytelling)
• Take Aways/References/Q&A
Tweeting now:
#dataed

Simon Sinek:  
How great leaders  
inspire action
http://www.ted.com/talks/simon_sinek_how_great_leaders_inspire_action.html
 
 
 
 
 
 
 
 
 
What
 
 
 
 
 
 
How
Why
What is a Strategy?
• Current use derived from military
• "a pattern in a stream of decisions" [Henry Mintzberg]

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”
– “a pattern  
in a stream  
of decisions”
Strategy in Action: Napoleon defeats a larger enemy

Wayne Gretzky’s 
Definition of Strategy
He skates to where he  
thinks the puck will be ...
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.

Definition of IT Governance
IT Governance:
• "putting structure around how organizations align IT strategy with business strategy,
ensuring that companies stay on track to achieve their strategies and goals, and
implementing good ways to measure IT’s performance.
• It makes sure that all stakeholders’ interests  
are taken into account and that processes 
provide measurable results.
• An IT governance framework should  
answer some key questions, such  
as how the IT department is functioning  
overall, what key metrics management  
needs and what return IT is giving back  
to the business from the investment it’s  
making." CIO Magazine (May 2007)
IT Governance Institute, five areas of focus:
• Strategic Alignment
• Value Delivery
• Resource Management
• Risk Management
• Performance Measures
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 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Division/Group/Project

Data Strategy in Context
Organizational 
Strategy
IT Strategy
Data Strategy
Organizational 
Strategy
IT Strategy
Data Strategy
This is wrong!
Organizational 
Strategy
IT Strategy
Data Strategy

Organizational 
Strategy
IT Strategy
This is correct …
Data Strategy
No clear connection exists between to business priorities and IT initiatives
Grow expenses
slower than
sales
Grow operating
income faster
than sales
Pass on
savings
Drive efficiency
with technology
Leverage scale
globally
Leverage
expertise
Deploy new
formats
Grow
productivity of
existing assets
Attract new
members
Expand into
new channels
Enter new
markets
Make
acquisitions
Produce
significant free
cash flow
Drive ROI
performance
Deliver greater
shareholder
value
Customer
Perspectiv
e
Open new
stores
Develop new,
innovative
formats
Appeal to new
demographics
Integrate
shopping
experience
Develop new,
innovative
formats
Remain
relevant to all
customers
Increase
"Green" Image
Internal
Perspectiv
e
Create
competitive
advantages
Improve use of
information
Strengthen
supply chain
Improve
Associate
productivity
Making
acquisitions
Increase
benefit from
our global
expertise
Present
consistent
view and
experience
Integrate
channels
Match staffing
to store needs
Increase sell
through
Financial
Perspectiv
e
Reduce
expenses
Inventory
Management
Human and
Intell. Capital
investment
Manage new
facilities
Improve
Sales and
margin by
facilities
Increased
member-base
revenues
Revenue
growth
Cash flow
Return on
Capital
Walmart Strategy Map
See more uniform brand and retail
experience
Leverage Growth Return
Gross Margin Improvement
CEOPerspective
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
TransformationPortfolio
Supply Chain
Multi ChannelMerchant ToolsSupply Chain
Strategic Initiatives
AcctingSales
Transactional Processing
Logistics AssociateLocations and Codes
Item
CustomerSuppliers
Retail Planning
( Alignment Gap )
Adapted from John Ladley

Supplemental: CMMI Data Strategy Elements
The data management strategy defines the overall framework of
the program. A data management strategy typically includes:
• A vision statement, which includes core operating principles;
goals and objectives; priorities, based on a synthesis of factors
important to the organization, such as business value, degree of
support for strategic initiatives, level of effort, and dependencies
• Program scope – including both key business areas (e.g.
Customer Accounts); data management priorities (e.g. Data
Quality); and key data sets (e.g. Customer Master Data)
• Business benefits
– The selected data management framework and how it will be used
– High-level roles and responsibilities
– Governance needs
– Description of the approach used to develop the data management program
– Compliance approach and measures
– High-level sequence plan (roadmap).
• Strategy
• Data Governance
– What is it?
– Frameworks
– Building Blocks
– Checklists
– Worst Practices
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
TheFileNamingConventionCommittee'sOutput

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
Use Their Language ...
• Getting access to data around here is like that Catherine Zeta
Jones scene where she is having to get thru all those lasers …

Practice Articulating How DG Solves Problems
Decision Making Needs
Data Quality/Inventory Management
Organizational Strategy Formulation/Implementation
Operational Data Delivery Performance
Data Security Planning/Implementation
Data Governance for our Organization

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
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.

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
Supplemental: Data Governance Primary Deliverables
• Data Policies
• Data Standards
• Resolved Issues
• Data Management
Projects and
Services
• Quality Data and
Information
• Recognized Data
Value

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
Supplemental: Data Governance Technologies
• Intranet Website
• E-Mail
• Metadata Tools
• Metadata Repository
• Issue Management Tools
• Data Governance KPI
Dashboard

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
• Strategy
• Data Governance
– What is it?
– Frameworks
– Building Blocks
– Checklists
– Worst Practices
Tweeting now:
#dataed

Getting Started
Assess context
Define DG roadmap
Secure executive mandate
Assign Data Stewards
Execute plan
Evaluate results
Revise plan
Apply change management
(Occurs once) (Repeats)
My barn had to pass a foundation inspection
• Before further construction could proceed
• No IT equivalent

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

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
http://www.datagovernance.com/
KiK Consulting
http://www.kikconsulting.com/

IBM Data Governance Council
http://www-01.ibm.com/software/data/system-z/data-governance/workshops.html
Elements of Effective Data Governance
See IBM Data Governance Council, http://www-01.ibm.com/software/tivoli/ governance/servicemanagement/ data-governance.html.

Baseline Consulting (sas.com)
American College Personnel Association

Supplemental: NASCIO DG Implementation Process
Supplemental: Data Governance Checklist
✓ Decision-Making Authority
✓ Standard Policies and
Procedures
✓ Data Inventories
✓ Data Content Management
✓ Data Records Management
✓ Data Quality
✓ Data Access
✓ Data Security and Risk
Management
Source: “Data Governance Checklist for Educators” by Angela Guess; http://www.dataversity.net/archives/5198

• 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
Supplemental: NASCIO Scorecard

Supplemental: 10 DG Worst Practices
1. Buy-in but not Committing:
Business vs. IT
2. Ready, Fire, Aim
3. Trying to Solve World Hunger or
Boil the Ocean
4. The Goldilocks Syndrome
5. Committee Overload
6. Failure to Implement
7. Not Dealing with Change
Management
8. Assuming that Technology Alone
is the Answer
9. Not Building Sustainable and
Ongoing Processes
10. Ignoring “Data Shadow Systems”
• Strategy
• Data Governance
– What is it?
– Frameworks
– Building Blocks
– Checklists
– Worst Practices
Tweeting now:
#dataed

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
 
Q1 
Keeping the doors open 
(little or no proactive  
data management)
Q2 
Increasing organizational
efficiencies/effectiveness
Q3 
Using data to create  
strategic opportunities 
Q4 
Both
Improve Operations
Innovation
Only 1 is 10 organizations has a board
approved data strategy!
Data Governance Strategy Choices

IT Project or Application-Centric Development
Original articulation from Doug Bagley @ Walmart
Data/
Information
IT 
Projects
 
Strategy
• In support of strategy, organizations
implement IT projects
• Data/information are typically
considered within the scope of IT
projects
• 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
Evolving Data is Different than Creating New Systems
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!

The special nature of DCD
• An architectural focus
• Practice extension
• Personality/organizational challenges  
unrecognized
• Technical engineering requires different skills
• Extra attention required to communication
• Scarcity of  
professionals
• Need for a  
specialist  
discipline
MONETIZING
DATA MANAGEMENT
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
Data-Centric Development
Original articulation from Doug Bagley @ Walmart
IT 
Projects
Data/ 
Information
 
Strategy
• In support of strategy, the organization
develops specific, shared data-based
goals/objectives
• These organizational data goals/
objectives drive the development of
specific IT projects with an eye to
organization-wide usage
• 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

• Telemetric data2005-07-17-srm-003.jpg
Why management doesn't need to understand metadata -
Link business objectives to technical capabilities

healthcare.gov
• 55 Contractors!
• 6 weeks from launch and
requirements not finalized
• "Anyone who has written a line of
code or built a system from the
ground-up cannot be surprised or
even mildly concerned that
Healthcare.gov did not work out
of the gate,"  
 
Standish Group International Chairman
Jim Johnson said in a recent podcast.  
• "The real news would have been
if it actually did work. The very
fact that most of it did work at all
is a success in itself."
• "It was pretty obvious from the first look
that the system hadn't been designed to
work right," says Marty Abbott. "Any
single thing that slowed down would slow
everything down."
• Software programmed to  
access data using  
traditional technologies
• Data components incorporated  
"big data technologies" 
http://www.slate.com/articles/technology/bitwise/2013/10/
problems_with_healthcare_gov_cronyism_bad_management_and_too_
many_cooks.html
FormalizingtheRoleofU.S.Army 
ITGovernance/Compliance

Suicide Mitigation
Data Mapping
12
Mental
illness
Deploy
ments
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
Suicide Mitigation

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
Communication Patterns
•
Source: The Challenge and the Promise: Strengthening the Force, Preventing Suicide
and Saving Lives - The Final Report of the Department of Defense Task Force on the
Prevention of Suicide by Members of the Armed Forces - August 2010

Vocabulary is Important-Tank, Tanks, Tankers, Tanked
How one inventory item proliferates data throughout the chain
555 Subassemblies & subcomponents
17,659 Repair parts or Consumables
System 1: 
18,214 Total items 
75 Attributes/ item 
1,366,050 Total attributes
System 2 
47 Total items 
15+ Attributes/item 
720 Total attributes
System 3
16,594 Total items
73 Attributes/item
1,211,362 Total attributes
System 4 
16 Attributes/item 
136,560 Total attributes
System 5 
22 Attributes/item 
351,098 Total attributes
Total for the five systems show above: 
59,350 Items 
179 Unique attributes 
3,065,790 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
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 …
• … and the sale closed …

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).
Possibly the Worst Data Governance Example
Mizuho Securities
Mizuho Securities
• 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
• Strategy
• Data Governance
– What is it?
– Frameworks
– Building Blocks
– Checklists
– Worst Practices
Tweeting now:
#dataed

Maslow's Hierarchy of Needs
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  
greater 
risk (with thanks to Tom DeMarco)
Data Management Practices Hierarchy
Advanced  
Data  
Practices
• MDM
• Mining
• Big Data
• Analytics
• Warehousing
• SOA
Foundational Data Management Practices
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
Data Governance Council Hotel

MONETIZING
DATA MANAGEMENT
• 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

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
Supplemental: 10 DG Worst Practices in Detail
1. Buy-in but not Committing:  
Business vs. IT
– Business needs to do more
– Data governance tasks need  
to recognized as priority
– Without a real business-resource commitment, data governance
takes a backseat and will never be implemented effectively
2. Ready, Fire, Aim
– Good: Create governance steering committee  
(business representatives from across enterprise)  
and separate governance working group (data stewards)
– Problem: Often get the timing wrong: Panels are formed and people
are assigned BEFORE they really understand the scope of the data
governance and participants’ roles and responsibilities
– Prematurely organize management framework and realize you
need a do-over = Guaranteed way to stall DG initiative
Source: “Data Governance Worst Practices” by Angela Guess; http://www.dataversity.net/archives/4895

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
5. Committee Overload
• Good: People of various business units and  
departments get involved in the governance process
• Bad: more people -> more politics -> more watered down
governance responsibilities
• To be successful, limit committee sizes to 6-12 people and ensure
that members have decision-making authority
6. Failure to Implement
• DG efforts won’t produce any business value if  
data definitions, business rules and KPIs are  
created but not used in any processes
• Governance process needs to be a complete feedback loop in which
data is defined, monitored, acted upon, and changed when
appropriate
• Also important: Establish ongoing communication about governance
to prevent business users going back to old habits

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
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

References
Websites
• Data Governance Book
Data Governance Book
Compliance Book
IT Governance Books

Questions?
It’s your turn!
Use the chat feature to submit
your questions to Peter now.
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Upcoming Events
Enterprise Data World • San Diego • April 17-22
Home Made Jam - Monday evening
Establishing the CDO Agenda 
April 19, 2016 @ 3:45-4:30 PM PT
Mapping Roles and Structure to Organizational Needs for Ongoing Success 
April 20, 2016 @ 11:00-12:30 PM PT
Addressing the Data Management Brain Drain 
April 20, 2016 @ 2:00-2:45 PM PT
May Webinar: 
Metadata Strategies 
May 10, 2016 @ 2:00 PM ET
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Data-Ed Webinar: Data Governance Strategies

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