Where Data Architecture and Data Governance Collide

Strategy
Is Where
Data Architecture
&
Data Governance
Collide © Copyright 2022 by Peter Aiken Slide # 1
peter.aiken@anythingawesome.com +1.804.382.5957 Peter Aiken, PhD
Peter Aiken, Ph.D.
• I've been doing this a long time
• My work is recognized as useful
• Associate Professor of IS (vcu.edu)
• Institute for Defense Analyses (ida.org)
• DAMA International (dama.org)
• MIT CDO Society (iscdo.org)
• Anything Awesome (anythingawesome.com)
• Experienced w/ 500+ data
management practices worldwide
• Multi-year immersions
– US DoD (DISA/Army/Marines/DLA)
– Nokia
– Deutsche Bank
– Wells Fargo
– Walmart
– HUD …
• 12 books and
dozens of articles
© Copyright 2022 by Peter Aiken Slide # 2
https://anythingawesome.com
+
• DAMA International President 2009-2013/2018/2020
• DAMA International Achievement Award 2001
(with Dr. E. F. "Ted" Codd
• DAMA International Community Award 2005
$1,500,000,000.00 USD

Where MDM and data
governance collide.

Chris Iloff
Director - Product Marketing

3
Cloud Platforms
Martech
SaaS Apps
Mobility & Devices
Customer / Partners /
Suppliers
Packaged Apps
Databases
Today, data is highly siloed and fragmented
Jane Smith
Opted in for
marketing
Jane Smith
Renewed
policy
John
Smith
Same
household
Jane E
Smith
Called
support
Jane E
Smith
Filed auto
claim

4
Unmanaged data leads to poor business decisions
Low quality data creates
inaccurate insights
Data issue identification is
inconsistent and lacks visibility
Data issue resolution is
irregular, highly manual, and
prone
to errors

5
Customer / Partners / Suppliers
Cloud Platforms Martech
Analytics, ML & AI
3rd Party Data Sources
Packaged Apps
Mobility & Devices
Custom Apps Databases
SaaS Apps
Modern MDM turns policy into action
Automated data
unification
Continuous curation
Flexible stewardship
workflow
Attribute level
permissions
Full audit logging

© Copyright 2022 by Peter Aiken Slide #
Program
• Introduction
– Data's Confounding Characteristics
– Uneven understanding
– DM BoK Foundations
• Defining Data Governance
– Need an elevator pitch
– Increasing costs of organizational data debt
– Requires an adaptive rather than a prescriptive approach
• Defining Data Architecture
– Ubiquitous and not well understood
– Keeping improvements practically focused on strategy
– Cannot use what is not understood
• Strategic Focus Improves Coordination
– Upending the traditional
– Defensive Driving
– Storytelling but don't relate everything
• Take Aways/References/Q&A
3
Strategy Is
Where Data
Architecture &
Data Governance
Collide
Data's Unique Properties
• Today, data is the most powerful, yet underutilized
and poorly managed organizational asset
• Data is
– Nearly unlimited
– Not really visible (absent competent visualization expertise)
– 'Cheap' to keep and transport
– 'Free' to make copies (non rivalrous)
– Impossible to clean-up if you spill it
• 80% of organizational data is (ROT)
– Redundant
– Obsolete
– Trivial
– of unknown quality
• Data specific education is
– Generally non-existent outside of IT
– Inconsistently taught
– Missing 'business' context
– (Re)learned by every workgroup
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]
https://www.inc.com/jim-schleckser/why-need-for-too-much-data-is-a-fatal-leadership-flaw.html
Wally Easton Playing Piano https://www.youtube.com/watch?v=NNbPxSvII-Q

• IT thinks data is a business problem
– "If they can connect to the server, then my job is done!"
• The business thinks IT is managing data adequately
– "Who else would be taking care of it?"
Confusion as to data responsibility

Without Data Structures/models …
It is not as easy to
visualize the cost of
Data Debt
You Must Address Data Debt Proactively
https://www.merkleinc.com/blog/are-you-buried-alive-data-debt
https://johnladley.com/a-bit-more-on-data-debt/
https://uk.nttdataservices.com/en/blog/2020/february/how-to-get-rid-of-your-data-debt
Data debt:
• Slows progress
• Decreases quality
• Increases costs
• Presents greater risks
• Data debt
– The time and effort it will take to return your
shared data to a governed state from its
(likely) current state of ungoverned
• Getting back to zero
– Involves undoing existing stuff
– Likely new skills are required

2020 American Airlines market value ~ $6b
AAdvantage valued between $19.5-$31.5b
2020 United market value ~ $9b
MileagePlus ~ $22b
https://www.forbes.com/sites/advisor/2020/07/15/how-airlines-make-billions-from-monetizing-frequent-flyer-programs/?sh=66da87a614e9
Current approaches are not and have not been working
Driving Innovation with Data
Competing on data and analytics
Managing data as a business asset
Created a data-driven organization
Forged a data culture
25% 50% 75% 100%
24%
24%
39%
41%
49%
Yes No
Source: Big Data and AI Executive Survey 2021 by Randy Bean and Thomas Davenport www.newvantage.com
2021
0%
25%
50%
75%
100%
technology people/process
92.2%
7.8%
80% of data challenges are people/process based!

Metadata
Management
11
Data
Management
Body
of
Knowledge
(DM
BoK
V2)
Practice
Areas
from The DAMA Guide to the Data Management Body of Knowledge 2E © 2017 by DAMA International
Data
Architecture
Metadata
Management
12
Data
Management
Body
of
Knowledge
(DM
BoK
V2)
Practice
Areas
from The DAMA Guide to the Data Management Body of Knowledge 2E © 2017 by DAMA International
Perfecting
operations in 3
data management
practice areas
Data
Strategy
Data
Governance
BI/
Warehouse

Program
• Introduction
13
Strategy Is
Where Data
Architecture &
Data Governance
Collide
Data is not broadly or widely understood
adapted from: http://www.dailymirror.lk/print/opinion/editorial-we-need-to-become-channels-of-peace/172-27164
It is like a fan!
It is like a snake!
It is like a wall!
It is like a rope!
It is like a tree! Blind Persons and the Elephant
It is like a story!
It is like a dashboard!
It is like a pipes!
It is like a warehouse!
It is like statistics!

Hidden Data Factories
https://hbr.org/2016/09/bad-data-costs-the-u-s-3-trillion-per-year
Work products are
delivered to
Customers
Customers
Knowledge Workers
80% looking for stuff
20% doing useful work
Department B
1. Check A's work
2. Make any corrections
3. Complete B's work
4. Deliver to Department C
Department A
https://en.wikipedia.org/wiki/Theory_of_constraints
Department C
1. Check B's work
2. Make any corrections
3. Complete C's work
4. Deliver to Customer
5. Deal with consequences
Poor data manifests as multifaceted organizational challenges
IT
System
Business
Challenge
Business
Process
Business
Challenge
IT
Process
Business
Challenge
Business
System
Business
Challenge
IT
Process
Business
Challenge
IT
System
Business
Challenge
Business
Process
Business
Challenge
Poor results
Root cause
analysis is
part of data
governance

Consistency Encourages Quality Analysis
IT
System
Business
Challenge
Business
Process
Business
Challenge
IT
Process
Business
Challenge
Business
System
Business
Challenge
IT
Process
Business
Challenge
IT
System
Business
Challenge
Business
Process
Business
Challenge
Eliminating data debt/
hidden data factories
requires a team with
specialized skills
deployed to create a
repeatable process
and develop sustained
organizational
skillsets
Definitions
• Corporate governance
– The relationship of an organization to society
• IT Governance
– Align IT strategy with organizational strategy–measure IT’s performance
• 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

Elevator Pitch
An elevator pitch, elevator speech,
or elevator statement is a short
description of an idea, product, or
company that explains the concept in a
way such that any listener can
understand it in a short period of time.
(Wikipedia)
What is Data Governance?
Would
you
want
your
sole,
non-
depletable,
non-
degrading,
durable,
strategic
asset
managed
without
guidance?
Managing
Data
with
Guidance

Data Governance is
Managing
Data Decisions
with
Guidance
Would
you
want
your
sole,
non-
depletable,
non-
degrading,
durable,
strategic
asset
managed
without
guidance?
Why is Data Governance important?
• Cost organizations millions each year in
– Productivity
– Redundant and
siloed efforts
– Poorly thought out
hardware and software
purchases
– Delayed decision making
using inadequate information
– Reactive instead of
proactive initiatives
– 20-40% of IT spending
can be reduced through
better data governance

Bad Data Decisions Spiral
Bad data decisions
Poor organizational outcomes
Technical decision
makers are not data
knowledgable
Business decision
makers are not data
knowledgable
Poor treatment
of organizational
data assets
Poor
quality
data
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.
Illustration from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
24

Primary Deliverables
• Data Policies
• Data Standards
• Resolved Issues
• Data Management Projects and Services
• Quality Data and Information
• Recognized Data Value
25
Roles and Responsibilities
• Suppliers:
– Business Executives
– IT Executives
– Data Stewards
– Regulatory Bodies
• Consumers:
– Data Producers
– Knowledge Workers
– Managers and Executives
– Data Professionals
– Customers
• Participants:
– Executive Data Stewards
– Coordinating Data Stewards
– Business Data Stewards
– Data Professionals
– DM Executive
– CIO
26

Scorecard: Data Governance Practices/Techniques
• Data Value
• Data Management
Cost
• Achievement of
Objectives
• # of Decisions Made
• Steward Representation/Coverage
• Data Professional Headcount
• Data Management Process Maturity
from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
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

DG Components
• Practices and Techniques
– Data Value
– Data Management Cost
– Achievement of Objectives
– # of Decisions Made
– Steward Representation/Coverage
– Data Professional Headcount
– Data Management Process
Maturity
• What do I include in my Data
Governance Program?
– Security and Privacy of Data
– Quality of Data
– Life Cycle Management
– Risk Management
– Content Valuation
– Standards (Data Design, Models
and Tools)
– Governance Tool Kits and Case
Studies
Evolve

https://www.google.com/url?sa=i&url=https%3A%2F%2Fprezi.com%2Fel_1jxok7t-x%2Fevolution-is-not-goal-oriented%2F&psig=AOvVaw3rFrerRanKFatDfESqab4C&ust=1591700086354000&source=images&cd=vfe&ved=0CAIQjRxqFwoTCODBiIyH8ukCFQAAAAAdAAAAABAD
Evolve
Getting Started with Data Governance
Assess context
DG roadmap
Policy/Standards Reqs.
Data Stewards
Execute plan
Evaluate results
Revise plan
Apply change management
(Occurs once)
(Repeats)
Get better at this!
Operational processes
Executive mandate
https://en.wikipedia.org/wiki/PDCA

Doing a poor job with data governance
• Failure to understand the role of data
governance re: proposed and
existing software/services
– Locks in imperfections for the life of the application
– Restricts data investment benefits
– Decreases organizational data leverage
• Accounts for 20-40% of IT budgets
devoted to evolving
– Data migration (Changing the data location)
– Data conversion (Changing data form, state, or product)
– Data improving (Inspecting and manipulating, or re-keying
data to prepare it for subsequent use)
• Lack of data governance causes everything else to
– Take longer
– Cost more
– Deliver less
– Present greater risk (with thanks to Tom DeMarco)
Program
• Introduction
34
Strategy Is
Where Data
Architecture &
Data Governance
Collide

Architecture is about ...
• Things
– (components)
• The functions of the things
– (individually)
• How the things interact
– (as a system,
– towards a goal)
• Business
• Process
• Systems
• Security
• Technical
• Data / Information
Typically Managed Architectures
• Business Architecture
– Goals, strategies, roles, organizational structure, location(s)
• Process Architecture
– Arrangement of inputs -> transformations = value -> outputs
– Typical elements: Functions, activities, workflow, events, cycles, products,
procedures
• Systems Architecture
– Applications, software components, interfaces, projects
• Security Architecture
– Arrangement of security controls relation to IT Architecture
• Technical Architecture/Tarchitecture
– Relation of software capabilities/technology stack
– Structure of the technology infrastructure of an enterprise, solution or system
– Typical elements: Networks, hardware, software platforms, standards/protocols
• Data / Information Architecture
– Arrangement of data assets supporting organizational strategy
– Typical elements: specifications expressed as entities, relationships, attributes,
definitions, values, vocabularies
1 in 10 organizations manage 1
or more of these formally!

Data Architectures: here, whether you like it or not
deviantart.com
• All organizations
have data
architectures
– Some are better
understood and
documented (and
therefore more
useful to the
organization)
Business
Process
Systems
Security
Technical
Data/Information
You cannot architect after implementation!

How are components expressed as architectures?
• Details are
organized into
larger components
• Larger
components are
organized into
models
• Models are
organized into
architectures
(composed of
architectural
components)
A B
C D
A B
C D
A
D
C
B
Intricate
Dependencies
Purposefulness
How are data structures expressed as architectures?
• Attributes are organized into entities/objects
– Attributes are characteristics of "things"
– Entitles/objects are "things" whose
information is managed in support of strategy
– Example(s)
• Entities/objects are organized into models
– Combinations of attributes and entities are
structured to represent information requirements
– Poorly structured data, constrains organizational
information delivery capabilities
– Example(s)
• Models are organized into architectures
– When building new systems, architectures are used to plan development
– More often, data managers do not know what existing architectures are and -
therefore - cannot make use of them in support of strategy implementation
• Why no examples?
Intricate
Dependencies
Purposefulness
THING
Thing.Id #
Thing.Description
Thing.Status
Thing.Sex.To.Be.Assigned
Thing.Reserve.Reason

Data architectures are composed of data models
Data
Data
Data
Information
Fact Meaning
Request
A model defining 3 important concepts composing a data architecture
[Built on definitions from Dan Appleton 1983]
Intelligence
Strategic Use
Data
Data
Data Data
42
“You can have data without information, but
you cannot have information without data”
— Daniel Keys Moran, Science Fiction Writer
1. Each FACT combines with one or more MEANINGS.
2. Each specific FACT and MEANING combination is referred to as a DATUM.
3. An INFORMATION is one or more DATA that are returned in response to a specific REQUEST
4. INFORMATION REUSE is enabled when one FACT is combined with more than one MEANING.
5. INTELLIGENCE is INFORMATION associated with its STRATEGIC USES.
6. DATA/INFORMATION must formally arranged into an ARCHITECTURE.
Wisdom & knowledge are
often used synonymously
Useful Data

Data structures organized into an Architecture
• How do data structures support strategy?
• Consider the opposite question?
– Were your systems explicitly designed to be
integrated or otherwise work together?
– If not then what is the likelihood that they will
work well together?
– They cannot be helpful as long as their
structure is unknown
• Two answers/two separate strategies
– Achieving efficiency and
effectiveness goals
– Providing organizational dexterity
for rapid implementation
Data should not be Sand in
Organizational Functions

Data supply
Data literacy
Standard data
Standard data
Leverage point - high performance automation
Data literacy
45
Data supply
Standard data
Data supply
Data literacy
46

This cannot happen without investments in
engineering and architecture!
47
Data supply
Data literacy
Standard data
Quality engineering/
architecture work products
do not happen accidentally!
Quality data engineering/
architecture work products
do not happen accidentally!
This cannot happen without investments in
data engineering and architecture!
48
Data supply
Data literacy
Standard data

Our barn had to pass a foundation inspection
• Before further construction could proceed
• It makes good business sense
• No IT equivalent
Take Aways
• What is a data architecture?
– A structure of data-based assets supporting
implementation of organizational strategy
– Most organizations have data assets that are not supportive of strategies -
i.e., information architectures that are not helpful
– The really important question is: how can organizations more effectively use
their data architectures to support strategy implementation?
• What is meant by use of an information architecture?
– Application of data assets towards organizational strategic objectives
– Assessed by the maturity of organizational data management practices
– Results in increased capabilities, dexterity, and self awareness
– Accomplished through use of data-centric development practices (including
taxonomies, stewardship, and repository use)
• How does an organization achieve better use of its data
architecture?
– Continuous re-development; the starting point isn't the beginning
– Information architecture components must typically be reengineered
– Using an iterative, incremental approach, typically focusing on one component
at a time and applying formal transformations

Program
• Introduction
51
Strategy Is
Where Data
Architecture &
Data Governance
Collide
What is Strategy?
• Current use derived from military
- a pattern in a stream of decisions
[Henry Mintzberg]
A thing

Your Data Strategy
• Highest level data guidance
available ...
• Focusing data activities on
business-goal achievement ...
• Providing guidance when
faced with a stream of
decisions or uncertainties
• Data strategy most usefully
articulates how data can be
best used to support
organizational strategy
• This usually involves a
balance of remediation and
proactive measures
This is the wrong way to think about data strategy
Organizational Strategy
IT Strategy
Data Strategy
x54

IT Strategy
This is correct …
Data Strategy
55
Data Strategy and Governance in Strategic Context
Data asset support for
organizational strategy
Organizational
Strategy
Data Strategy
Data
Governance
What is the most
effective use of steward
investments?
What the data assets do to
better support strategy
How well the data strategy is working
Data
Stewards
Progress,
plans, problems
(Business Goals)
(Metadata)
(Metadata/
Business Goals)
Trusted
Catalog

3-Dimensional Model Evolution Framework
Validated !
Not Validated "
As
Is
#
To
Be
☁
Physical %
Logical !
Conceptual &
Every modeling change can be mapped
to a transformation in this framework!
Forward Engineering
Physical %
Logical !
Conceptual &
As Is Requirements
Assets WHAT?
As Is Design Assets
HOW?
As Is Implementation
Assets AS BUILT
Validated !
Not Validated "
'
To
Be
☁
• Building new stuff (20% of effort and funding)
• Enhancing existing stuff (80% Reverse Engineering)

80% Reverse Engineering
As
Is
#
Physical %
Logical !
Conceptual &
As Is Requirements
Assets WHAT?
As Is Design Assets
HOW?
Assets AS BUILT
Evolve existing systems using a structured technique aimed
at recovering rigorous knowledge of the existing system to
leverage enhancement efforts [Chikofsky & Cross 1990]
Reengineering
As Is Requirements
Assets WHAT?
As
Is
#
Physical %
Logical !
Conceptual &
As Is Design Assets
HOW?
Assets AS BUILT
Reimplement
To Be
Implementation
Assets
To Be Design
Assets
To
Be
☁
To Be Requirements
Assets
• First, reverse
engineering the
existing system
to understand
its strengths/
weaknesses
• Next, use this
information to
inform the
design of the
new system

≈
≈
≈
≈
≈
≈
≈
≈
≈
≈
≈
≈
≈
≈
≈
≈
≈
≈
≈
≈
≈
≈
Data Governance Role: Produce systemic organizational changes that impact
data and work practices over time
Data
Leadership
Feedback
Data
Governance
Data
Improvement
Data
Stewards
Data
Community
Participants
Data
Generators/Data
Users
Data
Things
Happen
Organizational
Things
Happen
DIPs
Data
Improves
Over
Time
Data
Improves As
A Result of
Focus
Feedback
X
$
X
$
X
$
X
$
X
$
X
$
X
$
X
$
X $
Data architecture
components make better
'targets' of reengineering/
digitization efforts
(Things that further)
Lighthouse Metaphor Provides Focus
(
O
c
c
a
s
i
o
n
s
t
o
P
r
a
c
t
i
c
e
)
N
e
e
d
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d
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a
t
a
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k
i
l
l
s
(
O
p
p
o
r
t
u
n
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t
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e
s
t
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p
r
o
v
e
)
D
a
t
a
u
s
e
b
y
t
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b
u
s
i
n
e
s
s
62

IT/Systems Development
Leadership
(data decision makers)
Stewards
(data trustees)
Participants/Experts
(data subject matter experts)
Other Sources/Uses
(data makers & consumers)
IT/Systems
Development
Guidance
Decisions
Data/feedback
Changes
Action
R
e
s
o
u
r
c
e
s
Ideas
Data/Feedback
Components comprising the data community
Data architecture
grounds/constrains all
of these conversations

The MacGyver approach to DG uses paperclips and duct tape
• First, reverse
engineering the
existing system
to understand
its strengths/
weaknesses
• Next, use this
information to
inform the
design of the
new system

Keep the proper focus
• Wrong question:
– How should we govern all this data?
• Right question:
– Should we include this
data item within the
scope of our current
data govenance
practices?
• Regardless of the decision, document why!
• When taught as part of
basic driving, behavior
becomes part of a
lifelong practice
• Difficult to practice when
learned after driving has
been taught
• The Convincer
• General principles:
◦ Controlling your speed.
◦ Looking ahead and being prepared for unexpected events.
◦ Being alert and distraction free.
• Regarding other participants in traffic:
◦ Preparedness for all sorts of actions and reactions of other drivers
and pedestrians.
◦ Not expecting the other drivers to do what you would ordinarily do.
◦ Watching and respecting other drivers.
• Regarding your own vehicle:
◦ Maintaining a safe following distance.
◦ Driving safely considering (adjusting for) weather and/or road
conditions.
◦ Adjusting your speed before entering a bend, in order to avoid
applying the brakes in the middle of a bend.

Differences between Programs and Projects
• Programs are Ongoing, Projects End
– Managing a program involves long term strategic planning and
continuous process improvement is not required of a project
• Programs are Tied to the Financial Calendar
– Program managers are often responsible for delivering
results tied to the organization's financial calendar
• Program Management is Governance Intensive
– Programs are governed by a senior board that provides direction,
oversight, and control while projects tend to be less governance-intensive
• Programs Have Greater Scope of Financial Management
– Projects typically have a straight-forward budget and project financial
management is focused on spending to budget while program planning,
management and control is significantly more complex
• Program Change Management is an Executive Leadership
Capability
– Projects employ a formal change management process while at the program
level, change management requires executive leadership skills and program
change is driven more by an organization's strategy and is subject to market
conditions and changing business goals
Adapted from http://top.idownloadnew.com/program_vs_project/ and http://management.simplicable.com/management/new/program-management-vs-project-management
69
Your data program
must last at least as
long as your HR
program!
Level 3–KW–Citizen Data Knowledge Areas
• Elevator story
• Data stewardship
• Demonstrating value
• Currency
• Fiduciary responsibilities
• Shared fate
– https://www.techtarget.com/searchenterpriseai/definition/data-scientist

Data Architecture Data Governance Program
External Comprehension
Sample from: https://artist.com/kathy-linden/on-outside-looking-in/?artid=4385
Everything Else Data
Data (blah blah blah)
Data Program
Most do not appreciate the
difference between Data
Governance and the other data
stuff that needs to be done
• Introduction
– DM BoK
Program
72
Strategy Is
Where Data
Architecture &
Data Governance
Collide

Take Aways
• Need for DA/DG is increasing
– Increase in data volume
– DG is a new discipline
– Must conform to constraints
– No single best way
• DG/DA must become strategy driven
– Reengineer opportunistically
– Quantify strategic improvements
– Programmatic implementation
• Shared focus produces reusable,
shared results
– Implement data as a program not a project
– Gradually add ingredients
– Learn the value of stories/storytelling
• The goal is to improve DG/DA effectiveness and efficiencies (and
the data itself) over time
• The more data literate the organization, the easier the
transformations
Upcoming Events
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13 December 2022
Data Strategy Best Practices
10 January 2023
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Time: 19:00 UTC (2:00 PM NYC) | Presented by: Peter Aiken, PhD
Note: In this .pdf, clicking any webinar title opens the registration link

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Literacy Training?
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Where Data Architecture and Data Governance Collide

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