Data-Ed Online Webinar: Data Governance Strategies

Data Governance Strategies
• Date: April 14, 2015
• 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 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
1
Copyright 2014 by Data Blueprint

Shannon Kempe
Executive Editor at DATAVERSITY.net
2

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Peter Aiken, Ph.D.
4
• 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 
- 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

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 √ √ √ √
5
• Today, data is the most powerful, yet underutilized and poorly managed
organizational asset
• Data is your
– Sole
– Non-depleteable
– Non-degrading
– Durable
– Strategic
• Asset
– Data is the new oil!
– Data is the new (s)oil!
– Data is the new bacon!
• 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]

Presented By Peter Aiken, Ph.D.
“If you don't know where you are going, any road will get you there.”  
- Lewis Carroll

Motivation
Beth Jacobs abruptly  
resigned in March
7

Reported Home Depot data breach could exceed Target hack
8

9
• 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 (Storytelling) in Action
• Take Aways/References/Q&A
Tweeting now:
#dataed
• Strategy
• Data Governance
– What is it?
– Frameworks
– Building Blocks
– Checklists
– Worst Practices

10
• Strategy
• Data Governance
– What is it?
– Frameworks
– Building Blocks
– Checklists
– Worst Practices
Tweeting now:
#dataed

What is Strategy?
• Current use derived from military
• "a pattern in a stream of decisions" [Henry Mintzberg]
• "a system of finding, formulating, and developing a
doctrine that will ensure long-term success if followed
faithfully [Vladimir Kvint]
11

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”
12
– “a pattern  
in a stream  
of decisions”

Strategy in Action: Napoleon defeats a larger enemy
13

Wayne Gretzky’s Strategy
He skates to where he  
thinks the puck will be ...
14

Data Strategy in Context
• Organizational Strategy
• IT Strategy
• Data  
Governance  
Strategy
15

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.
16

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
17

No clear connection exists between to business priorities and IT initiatives
18
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

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 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
19
Division/Group/Project

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
20

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).
21

22
• Strategy
• Data Governance
– What is it?
– Frameworks
– Building Blocks
– Checklists
– Worst Practices
Tweeting now:
#dataed

23
• 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
24

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
25
Data Management Functions

5 Requirements for Effective DG
Data governance is a set of well-defined policies and practices
designed to ensure that data is:
1. Accessible
– Can the people who need it access the data they need?
– Does the data match the format the user requires?
2. Secure
– Are authorized people the only ones who can access the data?
– Are non-authorized users prevented from accessing it?
3. Consistent
– When two users seek the "same" piece of data, is it actually
the same data?
– Have multiple versions been rationalized?
4. High Quality
– Is the data accurate?
– Has it been conformed to meet agreed standards
5. Auditable
– Where did the data come from?
– Is the lineage clear?
– Does IT know who is using it and for what purpose?
26
Source: “5 Steps to Effective Data Governance” by Angela Guess; http://www.dataversity.net/archives/5160
• Integrity
• Accountability
• Transparency
• Strategic alignment
• Standardization
• Organizational change
management
• Data architecture
• Stewardship/Quality
• Protection

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
27

• Getting access to data around here is like that Catherine Zeta
Jones scene where she is having to get thru all those lasers …
Use Their Language ...
28

Practice Articulating How DG Solves Problems
29
Decision Making Needs
Data Quality/Inventory Management
Organizational Strategy Formulation/Implementation
Operational Data Delivery Performance
Data Security Planning/Implementation

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
30

DMM℠ Structure
31

One concept for process
improvement, others include:
• Norton Stage Theory
• TQM
• TQdM
• TDQM
• ISO 9000 
and focus on understanding
current processes and
determining where to make
improvements.
DMM℠ Capability Maturity Model Levels
Our DM practices are informal and ad hoc,
dependent upon "heroes" and heroic efforts
Performed
(1)
Managed
(2)
Our DM practices are defined and
documented processes performed at
the business unit level
Our DM efforts remain aligned with
business strategy using
standardized and consistently
implemented practices
Defined
(3)
Measured
(4)
We manage our data as a asset using
advantageous data governance practices/structures
 
Optimized
(5) 
DM is strategic organizational capability,
most importantly we have a process for
improving our DM capabilities
32

Assessment Components•
Data Management Practice Areas
Data Management
Strategy
DM is practiced as a
coherent and
coordinated set of
activities
Data Quality
Delivery of data is
support of
organizational
objectives – the
currency of DM
Data  
Governance
Designating specific
individuals caretakers
for certain data
Data Platform/
Architecture
Efficient delivery of
data via appropriate
channels
Data Operations
Ensuring reliable
access to data
Capability
Maturity Model
Levels
Examples of practice
maturity
1 – Performed
Our DM practices are ad hoc and
dependent upon "heroes" and
heroic efforts
2 – Managed
We have DM experience and have
the ability to implement disciplined
processes
3 – Defined
We have standardized DM
practices so that all in the
organization can perform it with
uniform quality
4 – Measured
We manage our DM processes so
that the whole organization can
follow our standard DM guidance
5 – Optimized
We have a process for improving
our DM capabilities
33

Industry Focused Results
34
Data Management Strategy
Data Governance
Platform & Architecture
Data Quality
Data Operations
Optimized(V) 
Measured(IV) 
Defined(III) 
Managed(II) 
Initial(I)
• CMU's Software  
Engineering Institute (SEI) Collaboration
• Results from hundreds organizations in various industries
including:
✓ Public Companies
✓ State Government Agencies
✓ Federal Government
✓ International Organizations
• Defined industry standard
• Steps toward defining data management "state of the practice"

Data Governance
Data Platform & Architecture
Data Quality
Data Operations
0 1 2 3 4 5
Client Industry Competition All Respondents
Comparative Assessment Results
Challenge
Challenge
Challenge
35

1
2
3
4
5
DataProgramCoordination
OrganizationalDataIntegration
DataStewardship
DataDevelopment
DataSupportOperations
2007 Maturity Levels 2012 Maturity Levels
Comparison of DM Maturity 2007-2012
36

2012 London Summer Games
• 60 GB of data/second
• 200,000 hours of big
data will be generated
testing systems
• 2,000 hours media
coverage/daily
• 845 million Facebook
users averaging 15 TB/
day
• 13,000 tweets/second
• 4 billion watching
• 8.5 billion devices
connected
37

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.
38
from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International

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
39

Supplemental: Data Governance Primary Deliverables
• Data Policies
• Data Standards
• Resolved Issues
• Data Management
Projects and
Services
• Quality Data and
Information
• Recognized Data
Value
40

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
41

Supplemental: Data Governance Technologies
• Intranet Website
• E-Mail
• Metadata Tools
• Metadata Repository
• Issue Management Tools
• Data Governance KPI
Dashboard
42

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
43

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
44

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
45

Application-Centric Development
Original articulation from Doug Bagley @ Walmart
• In support of strategy, organizations
develop specific goals/objectives
• The goals/objectives drive the development
of specific systems/applications
• Development of systems/applications leads
to network/infrastructure requirements
• Data/information are typically considered
after the systems/applications and network/
infrastructure have been articulated
• 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
Data/
Information
Network/
Infrastructure
Systems/
Applications
Goals/
Objectives
Strategy
46

What does it mean to treat data
as an organizational asset?
• An asset is a resource controlled
by the organization as a result of
past events or transactions and
from which future economic
benefits are expected to flow to  
the organization [Wikipedia]
• Assets are economic resources
– Must own or control
– Must use to produce value
– Value can be converted into cash
• As assets:
– Formalize the care and feeding of
data
– Put data to work in unique and
significant ways
47

Evolving Data is Different than Creating New Systems
48
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!

Data-Centric Development
Original articulation from Doug Bagley @ Walmart
• In support of strategy, the organization
develops specific goals/objectives
• The goals/objectives drive the development
of specific data/information assets with an
eye to organization-wide usage
• Network/infrastructure components are
developed to support organization-wide use
of data
• Development of systems/applications is
derived from the data/network architecture
• 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
Systems/
Applications
Network/
Infrastructure
Data/
Information
Goals/
Objectives
Strategy
49

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
50
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

51
• Strategy
• Data Governance
– What is it?
– Frameworks
– Building Blocks
– Checklists
– Worst Practices
Tweeting now:
#dataed

52
• Strategy
• Data Governance
– What is it?
– Frameworks
– Building Blocks
– Checklists
– Worst Practices
Tweeting now:
#dataed

Getting Started
53
Assess context
Define DG roadmap
Secure executive mandate
Assign Data Stewards
Execute plan
Evaluate results
Revise plan
Apply change management
(Occurs once) (Repeats)

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
54

Data Governance from the DMBOK
55

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
56
http://www.datagovernance.com/

KiK Consulting
57
http://www.kikconsulting.com/

IBM Data Governance Council
58
http://www-01.ibm.com/software/data/system-z/data-governance/workshops.html

Elements of Effective Data Governance
59
See IBM Data Governance Council, http://www-01.ibm.com/software/tivoli/ governance/servicemanagement/ data-governance.html.

Baseline Consulting (sas.com)
60

American College Personnel Association
61

Supplemental: NASCIO DG Implementation Process
62

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
63
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
64

Supplemental: NASCIO Scorecard
65

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”
66

67
• Strategy
• Data Governance
– What is it?
– Frameworks
– Building Blocks
– Checklists
– Worst Practices
Tweeting now:
#dataed

68
• Strategy
• Data Governance
– What is it?
– Frameworks
– Building Blocks
– Checklists
– Worst Practices
Tweeting now:
#dataed

Simon Sinek:  
How great leaders
inspire action
69
http://www.ted.com/talks/simon_sinek_how_great_leaders_inspire_action.html
 
 
 
 
 
 
 
 
 
What
 
 
 
 
 
 
How
Why

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
70

71

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."
72
• "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

Formalizing the
Role of U.S. Army
IT Governance/
Compliance
73

Suicide Mitigation
74

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
75

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
76

Communication Patterns
77
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
78

How one inventory item proliferates data throughout the chain
79
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
80

Spreadsheet Interpretation
81

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.
82

Example of Poor Data Governance
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
83
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).

84

Seven Sisters (from British Telecom)
http://www.datablueprint.com/thought-leaders/peter-aiken/book-monetizing-data-management/ [Thanks to Dave Evans]
85

86
• Strategy
• Data Governance
– What is it?
– Frameworks
– Building Blocks
– Checklists
– Worst Practices
Tweeting now:
#dataed

87
• Strategy
• Data Governance
– What is it?
– Frameworks
– Building Blocks
– Checklists
– Worst Practices
Tweeting now:
#dataed

Maslow's Hierarchy of Needs
88

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
89
Copy
right
2015by Data
Blueprint
Data Platform/Architecture
Data Governance Data Quality
Data Operations
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
90

The File
Naming
Convention
Committee's
Output
91

Data Governance Council Hotel
92

93
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
94

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
95

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
96
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
97

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
98
Source: “Data Governance Worst Practices” by Angela Guess; http://www.dataversity.net/archives/4895Source: “Data Governance Worst Practices” by Angela Guess; http://www.dataversity.net/archives/4895

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
99

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
100

References
Websites
• Data Governance Book
Data Governance Book
Compliance Book
101

IT Governance Books
102

Interdependencies
103
Data Governance
Master DataData Quality
makes the
case and is
responsible for
is a necessary but
insufficient prerequisite
to success
MD capabilities
constrain governance
effectiveness

Upcoming Events
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Sign up here:
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104

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