Data-Ed Webinar: Data Governance Strategies

Data Governance Strategies
• Date: September 9, 2014
• 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.
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Copyright 2014 by Data Blueprint
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“If you don't know where you are going, any road will get you there.”
Presented By Peter Aiken, Ph.D.
- Lewis Carroll
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MONETIZING
DATA MANAGEMENT
Unlocking the Value in Your Organization’s
Most Important Asset.
PETER AIKEN WITH JUANITA BILLINGS
FOREWORD BY JOHN BOTTEGA
Peter Aiken, Ph.D.
• 30+ years of experience in data
management
• Multiple international awards &
recognition
• Founder, Data Blueprint (datablueprint.com)
• Associate Professor of IS, VCU (vcu.edu)
• (Past) President, DAMA Int. (dama.org)
• 9 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, Nokia, Deutsche Bank,
Wells Fargo, Walmart, and the
Commonwealth of Virginia
5
The Case for the
Chief Data Officer
Recasting the C-Suite to Leverage
Your Most Valuable Asset
Peter Aiken and
Michael Gorman
5

Motivation
Beth Jacobs abruptly
resigned in March
6
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Reported Home Depot data breach could exceed Target hack
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8
• 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
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9
• Strategy
• Data Governance
– What is it?
– Frameworks
– Building Blocks
– Checklists
– Worst Practices
Tweeting now:
#dataed
9

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]
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10

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!
– of decisions”
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– “a pattern
in a stream
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Strategy in Action: Napoleon defeats a larger enemy
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Wayne Gretzky’s Strategy
He skates to where he
thinks the puck will be ...
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Data Strategy in Context
• Organizational Strategy
• IT Strategy
• Data
Governance
Strategy
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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.
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15

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
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No clear connection exists between to business priorities and IT initiatives
17
Leverage Growth Return
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
Gross Margin Improvement
CEO Perspective
Attract more customers & have customer purchasing more
( Alignment Gap )
Associate
Productivity
Customer
Insights
Supply Chain Merchant Tools Multi Channel
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
Retail Planning
Corporate Data
Inventory Mgmt
Transformation Portfolio
Supply Chain
Strategic Initiatives
Sales Accting
Transactional Processing
Logistics Locations and Codes Associate
Item
Suppliers Customer
Adapted from John Ladley
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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
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Division/Group/Project
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Data Governance Strategy Choices
!
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!
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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).
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21
• Strategy
• Data Governance
– What is it?
– Frameworks
– Building Blocks
– Checklists
– Worst Practices
Tweeting now:
#dataed
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22
• Strategy
• Data Governance
– What is it?
– Frameworks
– Building Blocks
– Checklists
– Worst Practices
Tweeting now:
#dataed
22

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
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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
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Data Management Functions
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5 Requirements for Effective DG
Data governance is a set of well-defined policies and practices
designed to ensure that data is:
1. Accessible
– 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?
• Integrity
• Accountability
• Transparency
• Strategic alignment
• Standardization
• Organizational change
management
• Data architecture
• Stewardship/Quality
• Protection
Source: “5 Steps to Effective Data Governance” by Angela Guess; http://www.dataversity.net/archives/5160
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Organizational Data Governance Purpose Statement
• What does data
governance mean to my
organization?
– Getting some individuals
(whose opinions matter)
– To form a body (needs a
formal purpose/authority)
– Who will advocate/evangelize
for (not dictate, enforce, rule)
– Increasing scope and rigor of
– Data-centric development
practices
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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 …
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Practice Articulating How DG Solves Problems
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Organizational Strategy Formulation/Implementation
Data Security Planning/Implementation
Operational Data Delivery Performance
Data Quality/Inventory Management
Decision Making Needs
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What is the Difference Between DG and DM?
• Data Governance
– Policy level guidance
– Setting general guidelines
and direction
– Example: All information not
marked public should be
considered confidential
• Data Management
– The business function of
planning
for, controlling and delivering
data/information assets
– Example: Delivering data
to solve business challenges
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DMM℠ Structure
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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
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• 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
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32

Industry Focused Results
33
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"
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Comparative Assessment Results
Data Management Strategy
Data Governance
Data Platform & Architecture
Data Quality
Data Operations
Challenge
Challenge
Challenge
0 1 2 3 4 5
Client Industry Competition All Respondents
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34

5
4
3
2
1
Comparison of DM Maturity 2007-2012
Data Program Coordination
Organizational Data Integration
Data Stewardship
Data Development
Data Support Operations
2007 Maturity Levels 2012 Maturity Levels
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35

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
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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.
from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
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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
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Supplemental: Data Governance Primary Deliverables
• Data Policies
• Data Standards
• Resolved Issues
• Data Management
Projects and
Services
• Quality Data and
Information
• Recognized Data
Value
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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
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Supplemental: Data Governance Technologies
• Intranet Website
• E-Mail
• Metadata Tools
• Metadata Repository
• Issue Management Tools
• Data Governance KPI
Dashboard
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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
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Why is Data Governance Important?
Cost organizations millions each year in
• Productivity
• Redundant and siloed efforts
• Poorly thought out hardware
and software purchases
• Reactive instead of
proactive initiatives
• Delayed decision making
using inadequate information
• 20-40% of IT spending can
be reduced through better
data governance
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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
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44

Application-Centric Development
Strategy
Goals/
Objectives
Systems/
Applications
Network/
Infrastructure
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
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45

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
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46

Evolving Data is Different than Creating New Systems
47
Common Organizational Data
(and corresponding data needs requirements)
Evolve
New Organizational
Capabilities
Systems
Development
Activities
Create
Future State
(Version +1)
Data evolution is separate from,
external to, and precedes system
development life cycle activities!
47

Data-Centric Development
Strategy
Goals/
Objectives
Data/
Information
Network/
Infrastructure
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
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48

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
49
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
49

50
• Strategy
• Data Governance
– What is it?
– Frameworks
– Building Blocks
– Checklists
– Worst Practices
Tweeting now:
#dataed
50

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

Getting Started
52
Assess context
Define DG roadmap
Secure executive mandate
Assign Data Stewards
Execute plan
Evaluate results
Revise plan
Apply change management
(Occurs once) (Repeats)
52

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
53
53

Data Governance from the DMBOK
54
54

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
55
http://www.datagovernance.com/
55

KiK Consulting
http://www.kikconsulting.com/
56
56

http://www-01.ibm.com/software/data/system-z/data-governance/workshops.html
IBM Data Governance Council
57
57

Elements of Effective Data Governance
See IBM Data Governance Council, http://www-01.ibm.com/software/tivoli/ governance/servicemanagement/ data-governance.html.
58
58

Baseline Consulting (sas.com)
59
59

American College Personnel Association
60
60

Supplemental: NASCIO DG Implementation Process
61
61

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
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• 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
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Supplemental: NASCIO Scorecard
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64

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”
65
65

66
• Strategy
• Data Governance
– What is it?
– Frameworks
– Building Blocks
– Checklists
– Worst Practices
Tweeting now:
#dataed
66

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

Simon Sinek:
How great leaders
inspire action
68
http://www.ted.com/talks/simon_sinek_how_great_leaders_inspire_action.html
Why
How
What
68

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
69
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70
70

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
71
71

Formalizing the
Role of U.S. Army
IT Governance/
Compliance
72
72

Suicide Mitigation
73
73

Data Mapping
12
Mental
illness
Deploy
ments
Work
History
Soldier Legal
Issues
Abuse
Suicide
Analysis
DMSS G1 DMDC FAP CID
Data objects
complete?
All sources
identified?
Best source for
each object?
How reconcile
differences
between
sources?
MDR
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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
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75

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
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76

Vocabulary is Important-Tank, Tanks, Tankers, Tanked
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How one inventory item proliferates data throughout the chain
78
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
8,535 Total items
16 Attributes/item
136,560 Total attributes
System 5
15,959 Total items
22 Attributes/item
351,098 Total attributes
Total for the five systems show above:
59,350 Items
179 Unique attributes
3,065,790 values
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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
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Spreadsheet Interpretation
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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.
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Example of Poor Data Governance
Mizuho Securities
Example
• Wanted to sell 1 share for
600,000 yen
• Sold 600,000 shares for 1
yen
• $347 million loss
• In-house system did not
have limit checking
• Tokyo stock exchange
system did not have limit
checking
• And doesn't allow order
cancellations
CLUMSY typing cost a Japanese bank at
least £128 million and staff their
Christmas bonuses yesterday, after a
trader mistakenly sold 600,000 more
shares than he should have. The trader
at Mizuho Securities, who has not been
named, fell foul of what is known in
financial circles as “fat finger syndrome”
where a dealer types incorrect details
into his computer. He wanted to sell one
share in a new telecoms company called
J Com, for 600,000 yen (about £3,000).
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Seven Sisters from British Telecom
84
Thanks to Dave Evans
84

85
• Strategy
• Data Governance
– What is it?
– Frameworks
– Building Blocks
– Checklists
– Worst Practices
Tweeting now:
#dataed
85

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

Maslow's Hierarchy of Needs
87
87

Build a Solid Foundation for Advanced Solutions
You can accomplish
Advanced Data Practices
without becoming proficient
in the Basic Data
Advanced
Management Practices
Data
however this will:
Practices
• MDM
• Take longer
• Mining
• Cost more
• Big Data
• Analytics
• Deliver less
• Warehousing
• Present
• SOA
greater
risk Basic Data Management Practices
88
Data Management Strategy Data Governance
Data Management Function
Metadata Management
Data Quality Program
88

Data Management Practices Hierarchy
Outcomes
(tooth)
Capabilities
(tail)
89
89

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
90

The File
Naming
Convention
Committee's
Output
91
91

Data Governance Council Hotel
92
92

93
MONETIZING
DATA MANAGEMENT
93

• 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
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
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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
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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
Copyright 2014 by Data Blueprint Source: “Data Governance Worst Practices” by Angela Guess; http://www.dataversity.net/archives/4895
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
Source: “Data Governance Worst Practices” by Angela Guess; http://www.dataversity.Source: “Data Governance Worst Practices” by Angela Guess; http://www.dnaetat/vaercrshiitvye.nse/4t/8a9rc5hives/4895
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7.Not Dealing with Change Management
• Business and IT processes need to be
changed for enterprise DG to be successful
• Need for change management is seldom addressed
• Challenges: people/process issues and internal politics
8.Assuming that Technology Alone is the Answer
• Purchasing MDM, data integration or data quality
software to support DG programs is not the solution
• Combination of vendor hype and high
price tags set high expectations
• Internal interactions are what make
or break data governance efforts
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9.Not Building Sustainable and Ongoing
Processes
• Initial investment in time, money
and people may be accurate
• Many organizations don’t establish a budget, resource
commitments or design DG processes with an eye toward
sustaining the governance effort for the long term
10.Ignoring “Data Shadow Systems”
• Common mistake: focus on “systems
of record” and BI systems, assuming
that all important data can be found there
• Often, key information is located in “data shadow systems”
scattered through organization
• Don’t ignore such additional deposits of information
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References
Websites
!
!
• Data Governance Book
!
Data Governance Book
!
Compliance Book
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101

IT Governance Books
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Upcoming Events
October Webinar:
Trends in Data Modeling
October 14, 2014 @ 2:00 PM ET
!
November Webinar:
Metadata Strategies
November 11, 2014 @ 2:00 PM ET
!
Sign up here:
• www.datablueprint.com/webinar-schedule
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!
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