Data-Ed: Unlock Business Value through Data Governance

Unlock Business Value through Data Governance
• 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.

1
Copyright 2013 by Data Blueprint

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.
Date: April 9, 2013
Time: 2:00 PM ET
Presented by: Peter Aiken, PhD
2

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

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your comments: #dataed
and event updates.

4

Unlock Business Value through
Data Governance

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

6

Motivation
• #nowthatcherisdead

• #now thatcher is dead

• #now that cher is dead

• #now t hatcher is dead

7

8

•• Context: What is Data Management/
Context: What Management/
DAMA/DM BoK/CDMP?
DAMA/DM BoK/CDMP?
• What is Data Governance and why
is it Important?
is it Important?
–
– Organizational -> IT -> Data
Organiza*onal
-‐>
IT
-‐>
Data
– Requirements for Effective Data
– Requirements
for
Eﬀec*ve
Data
Governance
Governance
• Data Governance
• Data Governance
–
– Frameworks
Frameworks
–
– Checklists

Checklists
–
– Worst
Prac*ces
Worst Practices
–
– Building
Blocks
Building Blocks
• Data Governance in Action:
• Data Governance in Action: Tweeting now:
– Securi*es
eexample
– Securities xample #dataed
– Retail
eexample
– Retail xample
• Take Aways/References/Q&A
9

• Context: What is Data Management/
DAMA/DM BoK/CDMP?
is it Important?
Governance
• Data Governance
– Frameworks
– Checklists
– Worst Practices
– Building Blocks
– Securities example #dataed
– Retail example
10

Data Management is an Integrated System of Five Practice Areas

#dataed
11

Five Integrated DM Practices
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.

#dataed
12

Data Management Practices Hierarchy (after Maslow)
• 5 Data
Management
Practices Areas /
Data Management
Basics
• Are necessary but
insufficient Advanced
prerequisites to Data
organizational data Practices
leveraging • Cloud
• MDM
applications • Mining
(that is Self Actualizing • Analytics
Data or Advanced Data • Warehousing
Practices) • Big

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.pngby Data Blueprint
Copyright 2013

Data
Management
Func-ons

DAMA DM BoK & CDMP
• Published by DAMA International
– The professional association for Data
Managers (40 chapters worldwide)
– DMBoK organized around
– Primary data management functions
focused around data delivery to the
organization (more at dama.org)
– Organized around several environmental
elements
• CDMP
– Certified Data Management Professional
– DAMA International and ICCP
– Membership in a distinct group made up of
your fellow professionals
– Recognition for your specialized knowledge
in a choice of 17 specialty areas
– Series of 3 exams
– For more information, please visit:
• http://www.dama.org/i4a/pages/index.cfm?pageid=3399
• http://iccp.org/certification/designations/cdmp
#dataed
14

DAMA/DM BoK/CDMP?
is it Important?
Governance
• Data Governance
– Frameworks
– Checklists
– Worst Practices
– Building Blocks
– Retail example
15

DAMA/DM BoK/CDMP?
is it Important?
Governance
• Data Governance
– Frameworks
– Checklists
– Worst Practices
– Building Blocks
– Retail example
16

Data Strategy in Context
Organiza)onal

IT
Strategy

Data
Strategy
Only
1
is
10
organiza/ons
has
a
board
approved
data

strategy!

17

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

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

No clear connection exists between to business priorities and IT initiatives
Walmart Strategy Map
CEO Perspective

Leverage Growth Return
Grow expenses Grow operating Grow Produce Deliver greater
Pass on Drive efficiency Leverage scale Leverage Deploy new Attract new Expand into Enter new Make Drive ROI
slower than income faster productivity of significant free shareholder
savings with technology globally expertise formats members new channels markets acquisitions performance
sales than sales existing assets cash flow value
Perspectiv
Customer

Develop new, Integrate Develop new, Remain
See more uniform brand and retail Open new Appeal to new Increase
Attract more customers & have customer purchasing more innovative shopping innovative relevant to all
e

experience stores demographics "Green" Image
formats experience formats customers
Perspectiv

Increase Present
Internal

Create Improve
Improve use of Strengthen Making benefit from consistent Integrate Match staffing Increase sell
competitive Associate
e

information supply chain acquisitions our global view and channels to store needs through
advantages productivity
expertise experience
Perspectiv

Improve
Financial

Human and Increased
Reduce Inventory Manage new Sales and Revenue Return on
Gross Margin Improvement Intell. Capital member-base Cash flow
e

expenses Management facilities margin by growth Capital
investment revenues
facilities

( Alignment Gap )
Strategic Initiatives

Associate Customer
Supply Chain Merchant Tools Multi Channel
Productivity Insights
Transformation Portfolio

Corporate Processes

Supply Chain Human Capital Corp. Reputation Acquisition Strategic Planning

Inventory Mgmt Real estate CRM Sales CRM Accting

Transactional Processing Retail Planning

Analytic and reporting processes

Corporate Reputation - Risk Management, Compliance, Marketing, IT and Data Governance

Corporate Data

Logistics Locations and Codes Associate

Item

Suppliers Customer
Adapted
from
John
Ladley

20

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

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

Data Governance from the DMBOK

23

Data Governance from the DMBOK

Organizational Strategy Formulation/Implementation

Data Security Planning/Implementation

Operational Data Delivery Performance

Data Quality/Inventory Management

Decision Making Needs

24

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

25

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

5 Requirements for Effective DG
Data governance is a set of well-defined policies and
practices designed to ensure that data is: • Integrity
• Accountability
1. Accessible • Transparency
– Can the people who need it access the data they need? • Strategic alignment
– Does the data match the format the user requires? • Standardization
2. Secure • Organizational change
management
– Are authorized people the only ones who can access the data? • Data architecture
– Are non-authorized users prevented from accessing it? • Stewardship/Quality
3. Consistent • Protection
– 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?
Source: “5 Steps to Effective Data Governance” by Angela Guess; http://www.dataversity.net/archives/5160

27

DAMA/DM BoK/CDMP?
is it Important?
Governance
• Data Governance
– Frameworks
– Checklists
– Worst Practices
– Building Blocks
– Retail example
28

DAMA/DM BoK/CDMP?
is it Important?
Governance
• Data Governance
– Frameworks
– Checklists
– Worst Practices
– Building Blocks
– Retail example
29

Getting Started

Assess context Execute plan

Define DG roadmap Evaluate results

Secure executive mandate Revise plan

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

30

Data Governance Frameworks

• A system of ideas
for guiding analyses
• A means of
organizing project
data ™

• Data integration Classification

Audience

Executive
Names

Perspectives

A
l
i
g
What
C o m p o s i t e
Inventory
Identification
Products
I n t e g r a t i o n s
How

Process
Identification
Forecast Sales
Where

Distribution
Identification
Material Supply Ntwk
A l i g n m e n t
Who

Responsibility
Identification
General Mgmt
When

Timing
Identification
Product Cycle
C o m p o s i t e
Why

Motivation
Identification
New Markets
Version
3.0

I n t e g r a t i o n s
A
l
i
g
Classification
Names

Scope
Model
Names

priorities decision
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
e.g.
Repair Products
e.g.
etc., etc.
e.g.
Marketing
e.g.
Economic Cycle
e.g.
Increased Yield
e
(Business
Context n
Accounts Record Transctns Sales Accounting Cycle Increased Quality
n (Scope
Identification

t t Lists)
Planners)
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 Inventory
Definition Process
Definition Distribution
Definition Responsibility
Definition Timing
Definition Motivation
Definition n
s s
Business Mgmt f
o
e.g.: primitive
model:
e.g.: composite model: f
o Business
e.g. e.g. e.g. e.g. e.g. e.g.
Perspective r
m
r
m Concepts
a a
(Business
Concept
t t (Business
Definition

i Business Entity Business Transform Business Location Business Role Business Interval Business End i
Owners) o o Models)
n Business Relationship Business Input/Output Business Connection Business Work Product Business Moment Business Means n

making framework
s s

Inventory
Representation Process
Representation Distribution
Representation Responsibility
Representation Timing
Representation Motivation
Representation
Architect e.g. e.g. e.g. e.g. e.g. e.g.
System
Perspective Logic
(Business
Logic (System
System Entity System Transform System Location System Role System Interval System End Representation
Models)
Designers)
System Relationship System Input /Output System Connection System Work Product System Moment System Means

Inventory
Specification Process
Specification Distribution
Specification Responsibility
Specification Timing
Specification Motivation
Specification
Engineer e.g. e.g. e.g. e.g. e.g. e.g. Technology
Perspective Physics

• A means of
(Business
Physics
(Technology
Technology Entity Technology Transform Technology Location Technology Role Technology Interval Technology End Specification
Models)
Builders)
Technology Relationship Technology Input /Output Technology Connection Technology Work Product Technology Moment Technology Means

A Inventory
Configuration Process
Configuration Distribution
Configuration Responsibility
Configuration Timing
Configuration Motivation
Configuration 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
(Business
Component n
t t (Tool
Configuration

Implementers) Tool Entity Tool Transform Tool Location Tool Role Tool Interval Tool End Models)
T Tool Relationship Tool Input /Output Tool Connection Tool Work Product Tool Moment Tool Means T
r
r
a a
n n

assessing progress
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
(Users) a a (Implementations)
t t
The i
o
Operations
Entities Operations
Transforms Operations
Locations Operations
Roles Operations
Intervals Operations
Ends
i
o The
Enterprise n
s Operations
Relationships Operations
In/Outputs Operations
Connections Operations
Work
Products Operations
Moments Operations
Means
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
Audience *Horizontal integration lines
are shown for example purposes
Perspectives Inventory Process Distribution Responsibility Timing Motivation only and are not a complete set.
Composite, integrative rela-
Enterprise Sets Flows Networks Assignments Cycles Intentions 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

31

Data Governance Institute


8
-‐

datablueprint.com 1/26/2010 http://www.datagovernance.com/
-‐
all
rights
reserved!
http://www.datagovernance.com/
©

Copyright
this
and
previous
years
by
Data
Blueprint

KiK Consulting

http://www.kikconsulting.com/

Copyright 2013 by Data Blueprint 8
8

IBM Data Governance Council

http://www-01.ibm.com/software/data/system-z/data-governance/workshops.html
8
8

Elements of Effective Data Governance

See IBM Data Governance Council, http://www-01.ibm.com/software/tivoli/ governance/servicemanagement/by Data Blueprint
Copyright 2013 data-governance.html. 8
8

American College Personnel Association

36

Data Governance from the DM BoK

Illustration from The DAMA Guide to the Data Management Body of Knowledge p.Copyright 2013 byData Blueprint
37 © 2009 by DAMA International 13
13

NASCIO DG Implementation Process

38

NASCIO Scorecard

39

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

40
Source: “Data Governance Checklist for Educators” by Angela Guess; http://www.dataversity.net/archives/5198

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

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

42

Largely Ineffective DG 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.

43

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

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
45

Data Governance Primary Deliverables
• Data Policies

• Data Standards

• Resolved Issues

• Data Management Projects and Services

• Quality Data and Information

• Recognized Data Value
46

Data Governance Roles and Responsibilities
Participants: Consumers:
• Executive Data Stewards • Data Producers
• Coordinating Data Stewards • Knowledge Workers
• Business Data Stewards • Managers and Executives
• Data Professionals • Data Professionals
• DM Executive • Customers
• CIO

Suppliers:
• Business Executives
• IT Executives
• Data Stewards
• Regulatory Bodies

47

Data Governance Technologies
• Intranet Website

• E-Mail

• Metadata Tools

• Metadata Repository

• Issue Management Tools

• Data Governance KPI Dashboard
48

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

DAMA/DM BoK/CDMP?
is it Important?
Governance
• Data Governance
– Frameworks
– Checklists
– Worst Practices
– Building Blocks
– Retail example
50

DAMA/DM BoK/CDMP?
is it Important?
Governance
• Data Governance
– Frameworks
– Checklists
– Worst Practices
– Building Blocks
– Retail example
51

Data Governance Examples, cont’d

Formalizing the Role of U.S. Army IT Governance/Compliance
52

Suicide Mitigation

53

Suicide Mitigation Mapping
Data
Deploy Work
ments History Abuse

Soldier Legal
Mental
illness Issues Suicide
Analysis

DMSS G1 DMDC FAP CID

MDR

Data objects All sources Best source for How reconcile
complete? identified? each object? differences
between
sources?

12 54

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

Communication Patterns

56
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

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
Christmas bonuses yesterday, after a
• $347 million loss 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
incorrect details into his computer. He
checking 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).

57

Diaper Story

Old New
Shipping Semi Best
Terms 2/10 net 30 ?
Turns 5 50
Risks same JIT
58

DAMA/DM BoK/CDMP?
is it Important?
Governance
• Data Governance
– Frameworks
– Checklists
– Worst Practices
– Building Blocks
– Retail example
59

DAMA/DM BoK/CDMP?
is it Important?
Governance
• Data Governance
– Frameworks
– Checklists
– Worst Practices
– Building Blocks
– Retail example
60

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

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

62
Source: “Data Governance Worst Practices” by Angela Guess; http://www.dataversity.net/archives/4895

10 DG Worst Practices in Detail
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
63
Source: “Data Governance Worst Practices” by Angela Guess; http://www.dataversity.net/archives/4895

Data-Ed: Unlock Business Value through Data Governance

Data-Ed: Unlock Business Value through Data Governance

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