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Key Elements of a Successful Data
Governance Program
At its core, data governance (DG) is: managing data with
guidance. This immediately provokes the question: would
you tolerate any of your assets to be managed without
guidance? (In all likelihood, your organization has been
managing data without adequate guidance and this accounts
for its current, less-than-optimal state.) This program
provides a practical guide to implementing DG or recharging
your existing program. It provides 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/stewardship programs
that manage data in support of organizational strategy.
Delegates will understand why data governance can be tricky
for organizations due to data’s confounding characteristics.
Focusing on four key DG elements
•#1 Keeping DG practically focused on strategy
•#2 DG must exist at the same level as HR
•#3 Gradually add ingredients (practicing and getting better)
•#4 Data governance in action: value-focused storytelling
© Copyright 2022 by Peter Aiken Slide # 1
https://anythingawesome.com
Date: 10 May 2022 | Time: 19:00 UTC (2:00 PM NYC) | Presented by: Peter Aiken, PhD
Wonderful musical contributions from
https://www.bensound.com/royalty-free-music
Chief Digital Manager at Dataversity.net
© Copyright 2022 by Peter Aiken Slide # 2
https://anythingawesome.com
Shannon Kempe
1 | Alation Confidential & Proprietary, Internal Use Only
Data Intelligence + Human Brilliance
Key Elements of a Successful Data
Governance Program
Opening Remarks
John Wills, Field CTO, Alation
2 | Proprietary & Confidential
We focus on ‘Catalog Led Governance’
● Unlocking the value of data for a broad array of roles across the enterprise
● Creating a culture of empowerment, democratization, and self-perpetuating value
3 | Proprietary & Confidential
We focus on guided stewardship and curation at scale
● The old, failed approach to governance was to put stewards in a no win position of
being responsible for the population, curation, and maintenance of all data assets
● Our approach pivots the role of stewards to enabling the crowd and guiding them on
the application of governance standards
Guided Community Driven
Social Collaboration
Centralized
Command & Control
4 | Proprietary & Confidential
We focus on driving community adoption and participation
● Traditional governance programs struggle with being relevant as rank and file
information workers go about their daily work
● Alation provides an immersive environment where information worker can find,
understand, trust, collaborate, publish, and reuse data assets all while being guided
by governance policies and standards that are co-located with information about
these assets.
Example 1 - User Self-Service Example 2 - Community
Collaboration & Sharing
Example 3 - Stewards as Guides
5 | Proprietary & Confidential
Active Data Governance: Prescriptive Approach
Monitor & Measure
• Determine policy conformance
• Create curation analysis
• Measure usage & asset creation
• Establish data quality
Establish Governance Framework
• Set mission & vision
• Create policies, standards & glossaries
Populate Data Catalog
• Ingest metadata (technical, business,
lineage)
• Analyze metadata (top users, popular
data)
Empower Data Stewards
• Recognize and assign stewards
• Automate stewardship processes
• Identify reviewers & workflow approvers
Curate Assets
• Describe data, apply quality flags
• Surface descriptions, quality, etc. to
users at point of data use
Apply Policies & Controls
• Implement policies & controls
• Enforce policies & controls
Drive Community Collaboration
• Promote trusted data use
• Leverage community knowledge
• Determine whether data is fit for
purpose / intended use
Supported by Alation Active Data Governance
Service Offering
Two Most Commonly
Asked Questions
© Copyright 2022 by Peter Aiken Slide # 3
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1) Will I get copies of the
slides after the event?
2) Is this being recorded?
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 # 4
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+
• DAMA International President 2009-2013/2018/2020
• DAMA International Achievement Award 2001
(with Dr. E. F. "Ted" Codd
• DAMA International Community Award 2005
Event Pricing
© Copyright 2022 by Peter Aiken Slide # 5
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• 20% off
directly from the
publisher
on select titles
• My Book Store @
http://anythingawesome.com
• Enter the code
"anythingawesome" at
the Technics bookstore
checkout where it says to
"Apply Coupon"
anythingawesome
Successful
Data
Governance
Program© Copyright 2022 by Peter Aiken Slide # 6
peter.aiken@anythingawesome.com +1.804.382.5957 Peter Aiken, PhD
Key Elements of a
• Data's Confounding Characteristics
– Uneven understanding
– Has lead fractured views of data and to
– Increasing organizational data debt
1. Keeping DG practically focused on strategy
– This is a young profession and must
– Directly support organizational strategy by
– Improving data and its use in the short and long term
2. DG must exist at the same level as HR
– In order to achieve effectiveness,
– DG is central to DM (and central to digitization efforts)
– Must be de-coupled from IT strategy
3. Gradually add ingredients (practicing and getting better)
– Digital and data are dependent on high speed automation/data processing
– Employ a DG Frameworks to refine focus
– Plan to evolve (PDCA)
4. Data governance in action: Storytelling
• Take Aways/References/Q&A
© Copyright 2022 by Peter Aiken Slide #
Program
7
https://anythingawesome.com
Key Elements of a
Successful Data
Governance
Program
© Copyright 2022 by Peter Aiken Slide #
https://anythingawesome.com
Program
• Data's Confounding Characteristics
– Uneven understanding
– Has lead fractured views of data and to
– Increasing organizational data debt
1. Keeping DG practically focused on strategy
– This is a young profession and must
– Directly support organizational strategy by
– Improving data and its use in the short and long term
2. DG must exist at the same level as HR
– In order to achieve effectiveness,
– DG is central to DM (and central to digitization efforts)
– Must be de-coupled from IT strategy
3. Gradually add ingredients (practicing and getting better)
– Digital and data are dependent on high speed automation/data processing
– Employ a DG Frameworks to refine focus
– Plan to evolve (PDCA)
4. Data governance in action: Storytelling
• Take Aways/References/Q&A
Key Elements of a
Successful Data
Governance
Program
Data is not broadly or widely understood
© Copyright 2022 by Peter Aiken Slide # 9
https://anythingawesome.com
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!
• 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
© Copyright 2022 by Peter Aiken Slide # 10
https://anythingawesome.com
© Copyright 2022 by Peter Aiken Slide # 11
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The
Princess
on the
Pea



by 

Hans Christian
Andersen
on
Doing a poor job with data governance
© Copyright 2022 by Peter Aiken Slide # 12
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• 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)
© Copyright 2022 by Peter Aiken Slide # 13
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Separating the Wheat from the Chaff
Organizing the Wheat Separated from the Chaff
© Copyright 2022 by Peter Aiken Slide # 14
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Is well organized data worth more?
• Examples of information architecture achievements that happened
well before the information age:
– Page numbering
– Alphabetical order
– Table of contents
– Indexes
– Lexicons
– Maps
– Diagrams
Pre-Information Age Metadata
© Copyright 2022 by Peter Aiken Slide # 15
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Example from: How to make sense of any mess
by Abby Covert (2014) ISBN: 1500615994
"While we can arrange things
with the intent to communicate
certain information, we can't
actually make information. Our
users do that for us."
https://www.youtube.com/watch?v=60oD1TDzAXQ&feature=emb_logo
https://www.youtube.com/watch?v=r10Sod44rME&t=1s
https://www.youtube.com/watch?v=XD2OkDPAl6s
Remove the structure and things fall apart rapidly
• Better organized data increases in value
© Copyright 2022 by Peter Aiken Slide # 16
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Organizing the Wheat Separated from the Chaff
• Better organized data increases in value
• Poor data management practices are costing
organizations money/time/effort
• 80% of organizational data is ROT
– Redundant
– Obsolete
– Trivial
• The question is which data to eliminate?
– Most enterprise data is never analyzed
© Copyright 2022 by Peter Aiken Slide # 17
https://anythingawesome.com
© Copyright 2022 by Peter Aiken Slide # 18
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Who is qualified to accomplish this?
You must address data debt proactively
© Copyright 2022 by Peter Aiken Slide # 19
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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
© Copyright 2022 by Peter Aiken Slide # 20
https://anythingawesome.com
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
© Copyright 2022 by Peter Aiken Slide #
https://anythingawesome.com
Program
• Data's Confounding Characteristics
– Uneven understanding
– Has lead fractured views of data and to
– Increasing organizational data debt
1. Keeping DG practically focused on strategy
– This is a young profession and must
– Directly support organizational strategy by
– Improving data and its use in the short and long term
2. DG must exist at the same level as HR
– In order to achieve effectiveness,
– DG is central to DM (and central to digitization efforts)
– Must be de-coupled from IT strategy
3. Gradually add ingredients (practicing and getting better)
– Digital and data are dependent on high speed automation/data processing
– Employ a DG Frameworks to refine focus
– Plan to evolve (PDCA)
4. Data governance in action: Storytelling
• Take Aways/References/Q&A
Key Elements of a
Successful Data
Governance
Program
© Copyright 2022 by Peter Aiken Slide #
https://anythingawesome.com
Program
• Data's Confounding Characteristics
– Uneven understanding
– Has lead fractured views of data and to
– Increasing organizational data debt
1. Keeping DG practically focused on strategy
– This is a young profession and must
– Directly support organizational strategy by
– Improving data and its use in the short and long term
2. DG must exist at the same level as HR
– In order to achieve effectiveness,
– DG is central to DM (and central to digitization efforts)
– Must be de-coupled from IT strategy
3. Gradually add ingredients (practicing and getting better)
– Digital and data are dependent on high speed automation/data processing
– Employ a DG Frameworks to refine focus
– Plan to evolve (PDCA)
4. Data governance in action: Storytelling
• Take Aways/References/Q&A
Key Elements of a
Successful Data
Governance
Program
How old is your profession?
© Copyright 2022 by Peter Aiken Slide # 23
https://anythingawesome.com
Augusta Ada King
Countess of Lovelace
(1815-52)
• 8,000+ years
• formalize practices
• GAAP
Current approaches are not and have not been working
© Copyright 2022 by Peter Aiken Slide # 24
https://anythingawesome.com
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!
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.
© Copyright 2022 by Peter Aiken Slide # 25
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IT Governance
© Copyright 2022 by Peter Aiken Slide # 26
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• "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.
• 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, 5 foci:
– Strategic Alignment
– Value Delivery
– Resource Management
– Risk Management
– Performance Measures
• "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.
• 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, 5 foci:
– Strategic Alignment
– Value Delivery
– Resource Management
– Risk Management
– Performance Measures
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
© Copyright 2022 by Peter Aiken Slide # 27
https://anythingawesome.com
What is Data Governance?
© Copyright 2022 by Peter Aiken Slide # 28
https://anythingawesome.com
Would
you
want
your
sole,
non-
depletable,
non-
degrading,
durable,
strategic
asset
managed
without
guidance?
Managing
Data
with
Guidance
Data Governance is
© Copyright 2022 by Peter Aiken Slide # 29
Managing
Data Decisions
with
Guidance
https://anythingawesome.com
Would
you
want
your
sole,
non-
depletable,
non-
degrading,
durable,
strategic
asset
managed
without
guidance?
Bad Data Decisions Spiral
© Copyright 2022 by Peter Aiken Slide # 30
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Bad Data Decisions Spiral
© Copyright 2022 by Peter Aiken Slide # 31
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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
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!
© Copyright 2022 by Peter Aiken Slide # 32
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What is Strategy?
• Current use derived from military
- a pattern in a stream of decisions
[Henry Mintzberg]
© Copyright 2022 by Peter Aiken Slide # 33
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A thing
Every Day
Low Price
Former Walmart Business Strategy
© Copyright 2022 by Peter Aiken Slide # 34
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Wayne
Gretzky’s
Strategy
© Copyright 2022 by Peter Aiken Slide #
He skates to where he
thinks the puck will be ...
35
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Strategy Example 3
© Copyright 2022 by Peter Aiken Slide #
Good Guys
(Us)
Bad Guys
(Them)
36
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Strategy Example 3
© Copyright 2022 by Peter Aiken Slide #
Good Guys
(Us)
Bad Guys
(Them)
37
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Strategy Example 3
© Copyright 2022 by Peter Aiken Slide #
Good Guys
(Us)
Bad Guys
(Them)
38
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Strategy Guides Workgroup Activities
© Copyright 2022 by Peter Aiken Slide #
A pattern
in a stream
of decisions
39
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Executive data board
Oversee council
• Translate commonwealth goals to agency performance targets
• provide resources
• Remove organizational obstacles
• appoint data governance council members
• Oversee the data governance council
• Oversee data sharing & analytics projects
© Copyright 2022 by Peter Aiken Slide #
Complex Data Governance Environment
Complex
Data Governance Council
• Liaise between agency operations & CDO
• Advise CDO on technology, policy, and governance strategies
• Administer data governance policies set by the board
• implement data sharing & analytics projects
• Review open data assets
• Report progress & compliance to the Board
Advise CDO
40
https://anythingawesome.com
Data sharing and analytics
• Identify goals and objectives
• Prioritize initiatives
• Study & report
• Recommend changes to budget and code
DATA COMMISSION
LEGISLATIVE
Region representatives
EXECUTIVE
JUDICIAL
Advise Governor
TRUSTEE
execute
• Define, approve, and communicate data strategies, policies,
standards, rules, guidelines, & best practices
• Provide a governance, policy, and technology framework
• Define agency data governance responsibilities
• Encourage & facilitate data sharing
• Facilitate coordination to prevent duplication
• Coordinate policy and technology proposals and recommendations
• Administer and manage the commonwealth data trust
• Track and enforce compliance and conformance
• Oversee dissemination of open data
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
© Copyright 2022 by Peter Aiken Slide # 41
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Data Strategy and Governance in Strategic Context
© Copyright 2022 by Peter Aiken Slide # 42
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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)
≈
≈
≈
≈
≈
≈
≈
≈
≈
≈
≈
≈
≈
≈
≈
≈
≈
≈
≈
≈
≈
≈
Data Governance Role: Produce systemic organizational changes that impact
data and work practices over time
© Copyright 2022 by Peter Aiken Slide # 43
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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 $
Support for
Organizational
Strategy
Better
IT
Outcomes
© Copyright 2022 by Peter Aiken Slide # 44
https://anythingawesome.com
Better Managed/
Higher Quality Data
Assets
Improved
Knowledge Worker
Productivity
Multiple Dependencies
© Copyright 2022 by Peter Aiken Slide # 45
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Better Managed/
Higher Quality Data
Assets
Better
IT
Outcomes
Improved
Knowledge Worker
Productivity
Support for
Organizational
Strategy
Poor data manifests as multifaceted organizational challenges
© Copyright 2022 by Peter Aiken Slide # 46
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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
© Copyright 2022 by Peter Aiken Slide # 47
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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
requires a team with
specialized skills
deployed to create a
repeatable process
and develop sustained
organizational
skillsets
© Copyright 2022 by Peter Aiken Slide # 48
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The MacGyver approach to DG uses paperclips and duct tape
© Copyright 2022 by Peter Aiken Slide # 49
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© Copyright 2022 by Peter Aiken Slide # 50
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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
– Delayed decision making
using inadequate information
– Reactive instead of
proactive initiatives
– 20-40% of IT spending
can be reduced through
better data governance
© Copyright 2022 by Peter Aiken Slide # 51
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© Copyright 2022 by Peter Aiken Slide #
https://anythingawesome.com
Program
• Data's Confounding Characteristics
– Uneven understanding
– Has lead fractured views of data and to
– Increasing organizational data debt
1. Keeping DG practically focused on strategy
– This is a young profession and must
– Directly support organizational strategy by
– Improving data and its use in the short and long term
2. DG must exist at the same level as HR
– In order to achieve effectiveness,
– DG is central to DM (and central to digitization efforts)
– Must be de-coupled from IT strategy
3. Gradually add ingredients (practicing and getting better)
– Digital and data are dependent on high speed automation/data processing
– Employ a DG Frameworks to refine focus
– Plan to evolve (PDCA)
4. Data governance in action: Storytelling
• Take Aways/References/Q&A
Key Elements of a
Successful Data
Governance
Program
© Copyright 2022 by Peter Aiken Slide #
https://anythingawesome.com
Program
• Data's Confounding Characteristics
– Uneven understanding
– Has lead fractured views of data and to
– Increasing organizational data debt
1. Keeping DG practically focused on strategy
– This is a young profession and must
– Directly support organizational strategy by
– Improving data and its use in the short and long term
2. DG must exist at the same level as HR
– In order to achieve effectiveness,
– DG is central to DM (and central to digitization efforts)
– Must be de-coupled from IT strategy
3. Gradually add ingredients (practicing and getting better)
– Digital and data are dependent on high speed automation/data processing
– Employ a DG Frameworks to refine focus
– Plan to evolve (PDCA)
4. Data governance in action: Storytelling
• Take Aways/References/Q&A
Key Elements of a
Successful Data
Governance
Program
Mismatched railroad tracks non aligned
© Copyright 2022 by Peter Aiken Slide # 54
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Data programmes driving IT programs
Data is not a Project
© Copyright 2022 by Peter Aiken Slide # 55
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Common Organizational Data
(and corresponding data needs requirements)
New Organizational
Capabilities
Systems
Development
Activities
Create
Evolve
Future State
(Version +1)
Data programmes drive IT programs
Data evolution is separate from,
external to, and precedes system
development life cycle activities!
What is the Difference Between DG and DM?
Data Governance
• Policy level guidance
• Setting general guidelines/direction
• Example: All information not marked
public should be considered confidential
• Keep in mind "firehouse" metaphore
Data Management
• The business function of
– planning for,
– controlling and
– delivering
– data/information assets
• Example: Delivering data to solve business challenges
© Copyright 2022 by Peter Aiken Slide # 56
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Data Management Data Governance Program
External Comprehension
© Copyright 2022 by Peter Aiken Slide # 57
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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
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
© Copyright 2022 by Peter Aiken Slide #
Adapted from http://top.idownloadnew.com/program_vs_project/ and http://management.simplicable.com/management/new/program-management-vs-project-management
58
https://anythingawesome.com
Your data program
must last at least as
long as your HR
program!
© Copyright 2022 by Peter Aiken Slide #
Metadata
Management
59
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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
Iteration 1
© Copyright 2022 by Peter Aiken Slide # 60
https://anythingawesome.com
Data
Strategy
Data
Governance
BI/
Warehouse
Perfecting
operations in 3
data management
practice areas
1X
1X
1X
Metadata
Data
Quality
Iteration 2
© Copyright 2022 by Peter Aiken Slide # 61
https://anythingawesome.com
Data
Strategy
Data
Governance
BI/
Warehouse Metadata
2X
2X
1X
Perfecting
operations in 3
data management
practice areas
Iteration 3
© Copyright 2022 by Peter Aiken Slide # 62
https://anythingawesome.com
Data
Strategy
Data
Governance
BI/
Warehouse
Reference &
Master Data
1X
3X
3X
Perfecting
operations in 3
data management
practice areas
(Things that further)
Organizational Strategy
DG Lighthouse Metaphors Provides Focus
© Copyright 2022 by Peter Aiken Slide #
(
O
c
c
a
s
i
o
n
s
t
o
P
r
a
c
t
i
c
e
)
N
e
e
d
e
d
D
a
t
a
S
k
i
l
l
s
(
O
p
p
o
r
t
u
n
i
t
i
e
s
t
o
i
m
p
r
o
v
e
)
D
a
t
a
u
s
e
b
y
t
h
e
b
u
s
i
n
e
s
s
63
https://anythingawesome.com
IT Project or Application-Centric Development
© Copyright 2022 by Peter Aiken Slide #
Original articulation from Doug Bagley @ Walmart 64
https://anythingawesome.com
Data/
Information
IT
Projects
• In support of strategy, organizations
implement IT projects
• Data/information are typically considered
within the scope of IT projects
• Problems with this approach:
– Ensures data is formed to the applications and
not around the organizational-wide information
requirements
– Process are narrowly formed around
applications
– Very little data reuse is possible
Strategy
This is the wrong way to think about data strategy
© Copyright 2022 by Peter Aiken Slide #
Organizational Strategy
IT Strategy
Data Strategy
x65
https://anythingawesome.com
Organizational Strategy
IT Strategy
This is correct …
© Copyright 2022 by Peter Aiken Slide #
Data Strategy
66
https://anythingawesome.com
Data-Centric Development
© Copyright 2022 by Peter Aiken Slide #
Original articulation from Doug Bagley @ Walmart 67
https://anythingawesome.com
Data/
Information
IT
Projects
• In support of strategy, the organization
develops specific, shared data-based
goals/objectives
• These organizational data goals/
objectives drive the development of
specific IT projects with an eye to
organization-wide usage
• Advantages of this approach:
- Data/information assets are developed from an
organization-wide perspective
- Systems support organizational data
needs and compliment organizational
process flows
- Maximum data/information reuse
Strategy
© Copyright 2022 by Peter Aiken Slide #
https://anythingawesome.com
Program
• Data's Confounding Characteristics
– Uneven understanding
– Has lead fractured views of data and to
– Increasing organizational data debt
1. Keeping DG practically focused on strategy
– This is a young profession and must
– Directly support organizational strategy by
– Improving data and its use in the short and long term
2. DG must exist at the same level as HR
– In order to achieve effectiveness,
– DG is central to DM (and central to digitization efforts)
– Must be de-coupled from IT strategy
3. Gradually add ingredients (practicing and getting better)
– Digital and data are dependent on high speed automation/data processing
– Employ a DG Frameworks to refine focus
– Plan to evolve (PDCA)
4. Data governance in action: Storytelling
• Take Aways/References/Q&A
Key Elements of a
Successful Data
Governance
Program
© Copyright 2022 by Peter Aiken Slide #
https://anythingawesome.com
Program
• Data's Confounding Characteristics
– Uneven understanding
– Has lead fractured views of data and to
– Increasing organizational data debt
1. Keeping DG practically focused on strategy
– This is a young profession and must
– Directly support organizational strategy by
– Improving data and its use in the short and long term
2. DG must exist at the same level as HR
– In order to achieve effectiveness,
– DG is central to DM (and central to digitization efforts)
– Must be de-coupled from IT strategy
3. Gradually add ingredients (practicing and getting better)
– Digital and data are dependent on high speed automation/data processing
– Employ a DG Frameworks to refine focus
– Plan to evolve (PDCA)
4. Data governance in action: Storytelling
• Take Aways/References/Q&A
Key Elements of a
Successful Data
Governance
Program
Digital Insight
• Subtract data from digital and what do you have?
• Subtract digital from data and you still have data
© Copyright 2022 by Peter Aiken Slide # 70
https://anythingawesome.com
https://www.linkedin.com/in/mark-johnson-518a752/
DIGITAL
DATA
?
DIGITAL
DATA
DATA
It isn't possible to go digital
Digital
© Copyright 2022 by Peter Aiken Slide # 71
https://anythingawesome.com
a
By just spelling 'data'
Dat
© Copyright 2022 by Peter Aiken Slide # 72
https://anythingawesome.com
It requires more work
Data
© Copyright 2022 by Peter Aiken Slide #
a
73
https://anythingawesome.com
© Copyright 2022 by Peter Aiken Slide #
Garbage
Digital
Results
74
https://anythingawesome.com
Perfect
Model
Data
Warehouse
Machine
Learning
Business
Intelligence
Block Chain
AI
MDM
Data
Governance
Analytics
Technology
GI➜GO!
Garbage
Data
Garbage In ➜ Garbage Out!
© Copyright 2022 by Peter Aiken Slide #
Garbage
Digital
Results
75
https://anythingawesome.com
Perfect
Model
Quality
Data
Data
Warehouse
AI
MDM
Analytics
Technology
Data
Governance
GI➜GO!
Machine
Learning
Block Chain
Business
Intelligence
© Copyright 2022 by Peter Aiken Slide #
Garbage
Digital
Results
76
https://anythingawesome.com
Perfect
Model
Quality
Data
Data
Warehouse
AI
MDM
Analytics
Technology
Data
Governance
GI➜GO!
Machine
Learning
Block Chain
Business
Intelligence
© Copyright 2022 by Peter Aiken Slide #
Garbage
Digital
Results
77
https://anythingawesome.com
Perfect
Model
Quality
Data
Data
Warehouse
AI
MDM
Analytics
Technology
Data
Governance
GI➜GO!
Machine
Learning
Block Chain
Business
Intelligence
© Copyright 2022 by Peter Aiken Slide #
Quality
Digital
Results
78
https://anythingawesome.com
Perfect
Model
Quality
Data
Data
Warehouse
AI
MDM
Analytics
Technology
Data
Governance
QI➜QO!
Machine
Learning
Block Chain
Business
Intelligence
© Copyright 2022 by Peter Aiken Slide #
Quality
Digital
Results
79
https://anythingawesome.com
Quality
Data
Today
Machine
Learning
© Copyright 2022 by Peter Aiken Slide # 80
https://anythingawesome.com
Data
Sandwich
Data supply
Data literacy
Standard data
Standard data
Leverage point - high performance automation
© Copyright 2022 by Peter Aiken Slide #
Data literacy
81
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Data supply
Leverage point - high performance automation
© Copyright 2022 by Peter Aiken Slide #
Standard data
Data supply
Data literacy
82
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Leverage point - high performance automation
© Copyright 2022 by Peter Aiken Slide #
This cannot happen without investments in
engineering and architecture!
83
https://anythingawesome.com
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!
Leverage point - high performance automation
© Copyright 2022 by Peter Aiken Slide #
This cannot happen without investments in
data engineering and architecture!
84
https://anythingawesome.com
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
© Copyright 2022 by Peter Aiken Slide # 85
https://anythingawesome.com
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
© Copyright 2022 by Peter Aiken Slide # 86
https://anythingawesome.com
Data Governance from the DMBOK
© Copyright 2022 by Peter Aiken Slide # 87
https://anythingawesome.com
from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
Data Governance Institute
© Copyright 2022 by Peter Aiken Slide # 88
https://anythingawesome.com
http://www.datagovernance.com/
KiK Consulting
© Copyright 2022 by Peter Aiken Slide # 89
https://anythingawesome.com
http://www.kikconsulting.com/
IBM Data Governance Council
© Copyright 2022 by Peter Aiken Slide # 90
https://anythingawesome.com
http://www-01.ibm.com/software/data/system-z/data-governance/workshops.html
Elements of Effective Data Governance
© Copyright 2022 by Peter Aiken Slide # 91
https://anythingawesome.com
See IBM Data Governance Council, http://www-01.ibm.com/software/tivoli/ governance/servicemanagement/ data-governance.html.
Baseline Consulting (sas.com)
© Copyright 2022 by Peter Aiken Slide # 92
https://anythingawesome.com
American College Personnel Association
© Copyright 2022 by Peter Aiken Slide # 93
https://anythingawesome.com
Domain expertise is less ← | → Domain expertise is greater
Roles more formally defined ← | → Roles less formally defined
Encounter
governed
data
more
directly
←
|
→
Encounter
governed
data
less
directly
More
time
is
dedicated
←
|
→
Less
time
is
dedicated
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
© Copyright 2022 by Peter Aiken Slide # 94
https://anythingawesome.com
R
e
s
o
u
r
c
e
s
Leadership
(data decision makers)
Stewards
(data trustees)
Guidance
Decisions
Participants/Experts
(data subject matter experts)
Other Sources/Uses
(data makers & consumers)
Data/feedback
Changes
Action
Ideas
Data/Feedback
Components comprising the data community
© Copyright 2022 by Peter Aiken Slide # 95
https://anythingawesome.com
Getting Started with Data Governance
© Copyright 2022 by Peter Aiken Slide # 96
https://anythingawesome.com
Assess context
DG roadmap
Policy/Standards Reqs.
Data Stewards
Operational processes
Executive mandate
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.
© Copyright 2022 by Peter Aiken Slide #
Illustration from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
97
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Primary Deliverables
• Data Policies
• Data Standards
• Resolved Issues
• Data Management Projects and Services
• Quality Data and Information
• Recognized Data Value
© Copyright 2022 by Peter Aiken Slide #
Illustration from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
98
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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
© Copyright 2022 by Peter Aiken Slide #
Illustration from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
99
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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
© Copyright 2022 by Peter Aiken Slide # 100
https://anythingawesome.com
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
© Copyright 2022 by Peter Aiken Slide # 101
https://anythingawesome.com
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
© Copyright 2022 by Peter Aiken Slide # 102
https://anythingawesome.com
Illustration from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
© Copyright 2022 by Peter Aiken Slide # 103
https://anythingawesome.com
Evolve
© Copyright 2022 by Peter Aiken Slide # 104
https://anythingawesome.com
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
© Copyright 2022 by Peter Aiken Slide # 105
https://anythingawesome.com
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
© Copyright 2022 by Peter Aiken Slide #
https://anythingawesome.com
Program
• Data's Confounding Characteristics
– Uneven understanding
– Has lead fractured views of data and to
– Increasing organizational data debt
1. Keeping DG practically focused on strategy
– This is a young profession and must
– Directly support organizational strategy by
– Improving data and its use in the short and long term
2. DG must exist at the same level as HR
– In order to achieve effectiveness,
– DG is central to DM (and central to digitization efforts)
– Must be de-coupled from IT strategy
3. Gradually add ingredients (practicing and getting better)
– Digital and data are dependent on high speed automation/data processing
– Employ a DG Frameworks to refine focus
– Plan to evolve (PDCA)
4. Data governance in action: Storytelling
• Take Aways/References/Q&A
Key Elements of a
Successful Data
Governance
Program
© Copyright 2022 by Peter Aiken Slide #
https://anythingawesome.com
Program
• Data's Confounding Characteristics
– Uneven understanding
– Has lead fractured views of data and to
– Increasing organizational data debt
1. Keeping DG practically focused on strategy
– This is a young profession and must
– Directly support organizational strategy by
– Improving data and its use in the short and long term
2. DG must exist at the same level as HR
– In order to achieve effectiveness,
– DG is central to DM (and central to digitization efforts)
– Must be de-coupled from IT strategy
3. Gradually add ingredients (practicing and getting better)
– Digital and data are dependent on high speed automation/data processing
– Employ a DG Frameworks to refine focus
– Plan to evolve (PDCA)
4. Data governance in action: Storytelling
• Take Aways/References/Q&A
Key Elements of a
Successful Data
Governance
Program
Non-Fungible Tokens
© Copyright 2022 by Peter Aiken Slide # 108
https://anythingawesome.com
$48M. $280
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 …
© Copyright 2022 by Peter Aiken Slide # 109
https://anythingawesome.com
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, 2008
• the 179 contracts were marked as
“hidden” in Excel, and those entries
became “un-hidden” when when
globally reformatting the document …
• … and the sale closed …
https://www.businessinsider.com/2008/10/barclays-excel-error-results-in-lehman-chaos
© Copyright 2022 by Peter Aiken Slide # 110
https://anythingawesome.com
Excel spreadsheet error blamed for UK’s 16,000 missing coronavirus cases
© Copyright 2022 by Peter Aiken Slide # 111
https://anythingawesome.com
The UK failed to add
nearly 16,000 confirmed
cases of coronavirus to its
national track and trace
system due to an Excel
error. A number of reports,
including from The
Guardian, Sky News, and
The Daily Mail, say the
mistake was caused when
an Excel spreadsheet
used to track confirmed
cases of the virus reached
its maximum file size and
failed to update.
"Failure to upload these cases to the national database meant anyone who came into contact with these individuals was not informed. It’s
an error that may have helped spread the virus further through the country as individuals exposed to the virus continued to act as normal."
Fan Blade Sensor
• 1 Sensor
– Probabilistic (generalist) maintenance forecasts
• 100 Sensors
– Establish optimal monitoring targets
– Finer tuned and safer maintenance
– Mission Readiness ???
– Storage $$$
– Handling $$$
– Opportunity $$$
– Systemic $$$
– Maintenance $$$
– Total > $1.5 Billion
© Copyright 2022 by Peter Aiken Slide # 112
https://anythingawesome.com
• Data's Confounding Characteristics
– Uneven understanding
– Has lead fractured views of data and to
– Increasing organizational data debt
1. Keeping DG practically focused on strategy
– This is a young profession and must
– Directly support organizational strategy by
– Improving data and its use in the short and long term
2. DG must exist at the same level as HR
– In order to achieve effectiveness,
– DG is central to DM (and central to digitization efforts)
– Must be de-coupled from IT strategy
3. Gradually add ingredients (practicing and getting better)
– Digital and data are dependent on high speed automation/data processing
– Employ a DG Frameworks to refine focus
– Plan to evolve (PDCA)
4. Data governance in action: Storytelling
• Take Aways/References/Q&A
© Copyright 2022 by Peter Aiken Slide #
Program
113
https://anythingawesome.com
Key Elements of a
Successful Data
Governance
Programs
Take Aways
• Need for DG is increasing
– Increase in data volume
– Lack of rigorous practice improvement
• DG is a new discipline
– Must conform to constraints
– No one best way
• DG must be driven by 4 key elements
1. Keep DG practically focused on strategy
2. Implement DG (and data) as a program not a project
3. Gradually add ingredients
4. Learn the value of stories/storytelling
• The goal is to improve DG effectiveness and efficiencies (and the
data itself) over time
• The more data literate the organization, the easier the
transformation
© Copyright 2022 by Peter Aiken Slide # 114
https://anythingawesome.com
Image adapted from: https://www.everythingneon.com/proddetail.php?prod=n105-0826-take-away-neon-sign
By the book
• Data Governance: How to
Design, Deploy, and
Sustain an Effective Data
Governance Program
• John Ladley
• Amazon Best Sellers
Rank: #641,937 in Books
(See Top 100 in Books)
– #242 in Management Information
Systems
– #209 in Library Management
– #380 in Database Storage &
Design
© Copyright 2022 by Peter Aiken Slide # 115
https://anythingawesome.com
References
Websites
• Data Governance Book
Data Governance Book
Compliance Book
© Copyright 2022 by Peter Aiken Slide # 116
https://anythingawesome.com
IT Governance Books
© Copyright 2022 by Peter Aiken Slide # 117
https://anythingawesome.com
Event Pricing
© Copyright 2022 by Peter Aiken Slide # 118
https://anythingawesome.com
• 20% off
directly from the
publisher
on select titles
• My Book Store @
http://anythingawesome.com
• Enter the code
"anythingawesome" at
the Technics bookstore
checkout where it says to
"Apply Coupon"
anythingawesome
[ Clicking any webinar title will link directly to the registration page ]
Upcoming Events
Data Preparation Fundamentals
14 June 2022
Conceptual vs. Logical vs. Physical Data
12 July 2022
The Importance of Metadata
9 August 2022
© Copyright 2022 by Peter Aiken Slide # 119
https://anythingawesome.com
Brought to you by:
Time: 19:00 UTC (2:00 PM NYC) | Presented by: Peter Aiken, PhD
Peter.Aiken@AnythingAwesome.com +1.804.382.5957
Thank You!
© Copyright 2022 by Peter Aiken Slide # 120
Book a call with Peter to discuss anything - https://anythingawesome.com/OfficeHours.html
Critical Design Review?
Hiring Assistance?
Reverse Engineering Expertise?
Executive Data
Literacy Training?
Mentoring?
Tool/automation evaluation?
Use your data more strategically?
© Copyright 2022 by Peter Aiken Slide # 121
https://anythingawesome.com
As a topic, data is ...
Complex &
detailed
• Outsiders do not
want to hear about
or discuss any
aspects of
challenges/solutions
• Most are unqualified
re: architecture/
engineering
Taught
inconsistently
• Focus is on
technology
• Business impact is
not addressed
Not well
understood
• (Re)learned by
every
workgroup
• Lack of standards/
poor literacy/
unknown
dependencies
© Copyright 2022 by Peter Aiken Slide #
Wally Easton Playing Piano
https://www.youtube.com/watch?v=NNbPxSvII-Q
122
https://anythingawesome.com
© Copyright 2022 by Peter Aiken Slide # 123
https://anythingawesome.com
What is the most
effective use of steward
investments?
Data Strategy and Governance in Strategic Context
© Copyright 2022 by Peter Aiken Slide # 124
https://anythingawesome.com
(Business Goals)
(Metadata)
Data asset support for
organizational strategy
What the data assets do to
better support strategy
How well the data strategy is working
Organizational
Strategy
Data
Governance
Data Strategy
Data
Stewards
(Metadata/
Business Goals)
Progress,
plans, problems
Toyota versus Detroit Engine Mounting (Circa 1994)
• Detroit
– 3 different
bolts
– 3 different
wrenches
– 3 different
bolt
inventories
• Toyota
– 1 bolt used
for all three
assemblies
– 1 bolt
inventory
– 1 type of
wrench
© Copyright 2022 by Peter Aiken Slide # 125
https://anythingawesome.com
Toyota versus Detroit Engine Mounting (Circa 1994)
• Detroit
– many
different
bolts
– many
different
wrenches
– many
different bolt
inventories
• Toyota
– same bolts
used for all
three
assemblies
– same 1 bolt
inventory
– same 1 type
of wrench
© Copyright 2022 by Peter Aiken Slide # 126
https://anythingawesome.com
© Copyright 2022 by Peter Aiken Slide # 127
https://anythingawesome.com

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Key Elements of a Successful Data Governance Program

  • 1. Key Elements of a Successful Data Governance Program At its core, data governance (DG) is: managing data with guidance. This immediately provokes the question: would you tolerate any of your assets to be managed without guidance? (In all likelihood, your organization has been managing data without adequate guidance and this accounts for its current, less-than-optimal state.) This program provides a practical guide to implementing DG or recharging your existing program. It provides 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/stewardship programs that manage data in support of organizational strategy. Delegates will understand why data governance can be tricky for organizations due to data’s confounding characteristics. Focusing on four key DG elements •#1 Keeping DG practically focused on strategy •#2 DG must exist at the same level as HR •#3 Gradually add ingredients (practicing and getting better) •#4 Data governance in action: value-focused storytelling © Copyright 2022 by Peter Aiken Slide # 1 https://anythingawesome.com Date: 10 May 2022 | Time: 19:00 UTC (2:00 PM NYC) | Presented by: Peter Aiken, PhD Wonderful musical contributions from https://www.bensound.com/royalty-free-music Chief Digital Manager at Dataversity.net © Copyright 2022 by Peter Aiken Slide # 2 https://anythingawesome.com Shannon Kempe
  • 2. 1 | Alation Confidential & Proprietary, Internal Use Only Data Intelligence + Human Brilliance Key Elements of a Successful Data Governance Program Opening Remarks John Wills, Field CTO, Alation
  • 3. 2 | Proprietary & Confidential We focus on ‘Catalog Led Governance’ ● Unlocking the value of data for a broad array of roles across the enterprise ● Creating a culture of empowerment, democratization, and self-perpetuating value
  • 4. 3 | Proprietary & Confidential We focus on guided stewardship and curation at scale ● The old, failed approach to governance was to put stewards in a no win position of being responsible for the population, curation, and maintenance of all data assets ● Our approach pivots the role of stewards to enabling the crowd and guiding them on the application of governance standards Guided Community Driven Social Collaboration Centralized Command & Control
  • 5. 4 | Proprietary & Confidential We focus on driving community adoption and participation ● Traditional governance programs struggle with being relevant as rank and file information workers go about their daily work ● Alation provides an immersive environment where information worker can find, understand, trust, collaborate, publish, and reuse data assets all while being guided by governance policies and standards that are co-located with information about these assets. Example 1 - User Self-Service Example 2 - Community Collaboration & Sharing Example 3 - Stewards as Guides
  • 6. 5 | Proprietary & Confidential Active Data Governance: Prescriptive Approach Monitor & Measure • Determine policy conformance • Create curation analysis • Measure usage & asset creation • Establish data quality Establish Governance Framework • Set mission & vision • Create policies, standards & glossaries Populate Data Catalog • Ingest metadata (technical, business, lineage) • Analyze metadata (top users, popular data) Empower Data Stewards • Recognize and assign stewards • Automate stewardship processes • Identify reviewers & workflow approvers Curate Assets • Describe data, apply quality flags • Surface descriptions, quality, etc. to users at point of data use Apply Policies & Controls • Implement policies & controls • Enforce policies & controls Drive Community Collaboration • Promote trusted data use • Leverage community knowledge • Determine whether data is fit for purpose / intended use Supported by Alation Active Data Governance Service Offering
  • 7. Two Most Commonly Asked Questions © Copyright 2022 by Peter Aiken Slide # 3 https://anythingawesome.com 1) Will I get copies of the slides after the event? 2) Is this being recorded? 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 # 4 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
  • 8. Event Pricing © Copyright 2022 by Peter Aiken Slide # 5 https://anythingawesome.com • 20% off directly from the publisher on select titles • My Book Store @ http://anythingawesome.com • Enter the code "anythingawesome" at the Technics bookstore checkout where it says to "Apply Coupon" anythingawesome Successful Data Governance Program© Copyright 2022 by Peter Aiken Slide # 6 peter.aiken@anythingawesome.com +1.804.382.5957 Peter Aiken, PhD Key Elements of a
  • 9. • Data's Confounding Characteristics – Uneven understanding – Has lead fractured views of data and to – Increasing organizational data debt 1. Keeping DG practically focused on strategy – This is a young profession and must – Directly support organizational strategy by – Improving data and its use in the short and long term 2. DG must exist at the same level as HR – In order to achieve effectiveness, – DG is central to DM (and central to digitization efforts) – Must be de-coupled from IT strategy 3. Gradually add ingredients (practicing and getting better) – Digital and data are dependent on high speed automation/data processing – Employ a DG Frameworks to refine focus – Plan to evolve (PDCA) 4. Data governance in action: Storytelling • Take Aways/References/Q&A © Copyright 2022 by Peter Aiken Slide # Program 7 https://anythingawesome.com Key Elements of a Successful Data Governance Program © Copyright 2022 by Peter Aiken Slide # https://anythingawesome.com Program • Data's Confounding Characteristics – Uneven understanding – Has lead fractured views of data and to – Increasing organizational data debt 1. Keeping DG practically focused on strategy – This is a young profession and must – Directly support organizational strategy by – Improving data and its use in the short and long term 2. DG must exist at the same level as HR – In order to achieve effectiveness, – DG is central to DM (and central to digitization efforts) – Must be de-coupled from IT strategy 3. Gradually add ingredients (practicing and getting better) – Digital and data are dependent on high speed automation/data processing – Employ a DG Frameworks to refine focus – Plan to evolve (PDCA) 4. Data governance in action: Storytelling • Take Aways/References/Q&A Key Elements of a Successful Data Governance Program
  • 10. Data is not broadly or widely understood © Copyright 2022 by Peter Aiken Slide # 9 https://anythingawesome.com 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! • 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 © Copyright 2022 by Peter Aiken Slide # 10 https://anythingawesome.com
  • 11. © Copyright 2022 by Peter Aiken Slide # 11 https://anythingawesome.com The Princess on the Pea
 
 by 
 Hans Christian Andersen on Doing a poor job with data governance © Copyright 2022 by Peter Aiken Slide # 12 https://anythingawesome.com • 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)
  • 12. © Copyright 2022 by Peter Aiken Slide # 13 https://anythingawesome.com Separating the Wheat from the Chaff Organizing the Wheat Separated from the Chaff © Copyright 2022 by Peter Aiken Slide # 14 https://anythingawesome.com Is well organized data worth more?
  • 13. • Examples of information architecture achievements that happened well before the information age: – Page numbering – Alphabetical order – Table of contents – Indexes – Lexicons – Maps – Diagrams Pre-Information Age Metadata © Copyright 2022 by Peter Aiken Slide # 15 https://anythingawesome.com Example from: How to make sense of any mess by Abby Covert (2014) ISBN: 1500615994 "While we can arrange things with the intent to communicate certain information, we can't actually make information. Our users do that for us." https://www.youtube.com/watch?v=60oD1TDzAXQ&feature=emb_logo https://www.youtube.com/watch?v=r10Sod44rME&t=1s https://www.youtube.com/watch?v=XD2OkDPAl6s Remove the structure and things fall apart rapidly • Better organized data increases in value © Copyright 2022 by Peter Aiken Slide # 16 https://anythingawesome.com
  • 14. Organizing the Wheat Separated from the Chaff • Better organized data increases in value • Poor data management practices are costing organizations money/time/effort • 80% of organizational data is ROT – Redundant – Obsolete – Trivial • The question is which data to eliminate? – Most enterprise data is never analyzed © Copyright 2022 by Peter Aiken Slide # 17 https://anythingawesome.com © Copyright 2022 by Peter Aiken Slide # 18 https://anythingawesome.com Who is qualified to accomplish this?
  • 15. You must address data debt proactively © Copyright 2022 by Peter Aiken Slide # 19 https://anythingawesome.com 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 © Copyright 2022 by Peter Aiken Slide # 20 https://anythingawesome.com 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
  • 16. © Copyright 2022 by Peter Aiken Slide # https://anythingawesome.com Program • Data's Confounding Characteristics – Uneven understanding – Has lead fractured views of data and to – Increasing organizational data debt 1. Keeping DG practically focused on strategy – This is a young profession and must – Directly support organizational strategy by – Improving data and its use in the short and long term 2. DG must exist at the same level as HR – In order to achieve effectiveness, – DG is central to DM (and central to digitization efforts) – Must be de-coupled from IT strategy 3. Gradually add ingredients (practicing and getting better) – Digital and data are dependent on high speed automation/data processing – Employ a DG Frameworks to refine focus – Plan to evolve (PDCA) 4. Data governance in action: Storytelling • Take Aways/References/Q&A Key Elements of a Successful Data Governance Program © Copyright 2022 by Peter Aiken Slide # https://anythingawesome.com Program • Data's Confounding Characteristics – Uneven understanding – Has lead fractured views of data and to – Increasing organizational data debt 1. Keeping DG practically focused on strategy – This is a young profession and must – Directly support organizational strategy by – Improving data and its use in the short and long term 2. DG must exist at the same level as HR – In order to achieve effectiveness, – DG is central to DM (and central to digitization efforts) – Must be de-coupled from IT strategy 3. Gradually add ingredients (practicing and getting better) – Digital and data are dependent on high speed automation/data processing – Employ a DG Frameworks to refine focus – Plan to evolve (PDCA) 4. Data governance in action: Storytelling • Take Aways/References/Q&A Key Elements of a Successful Data Governance Program
  • 17. How old is your profession? © Copyright 2022 by Peter Aiken Slide # 23 https://anythingawesome.com Augusta Ada King Countess of Lovelace (1815-52) • 8,000+ years • formalize practices • GAAP Current approaches are not and have not been working © Copyright 2022 by Peter Aiken Slide # 24 https://anythingawesome.com 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!
  • 18. 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. © Copyright 2022 by Peter Aiken Slide # 25 https://anythingawesome.com IT Governance © Copyright 2022 by Peter Aiken Slide # 26 https://anythingawesome.com • "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. • 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, 5 foci: – Strategic Alignment – Value Delivery – Resource Management – Risk Management – Performance Measures • "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. • 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, 5 foci: – Strategic Alignment – Value Delivery – Resource Management – Risk Management – Performance Measures
  • 19. 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 © Copyright 2022 by Peter Aiken Slide # 27 https://anythingawesome.com What is Data Governance? © Copyright 2022 by Peter Aiken Slide # 28 https://anythingawesome.com Would you want your sole, non- depletable, non- degrading, durable, strategic asset managed without guidance? Managing Data with Guidance
  • 20. Data Governance is © Copyright 2022 by Peter Aiken Slide # 29 Managing Data Decisions with Guidance https://anythingawesome.com Would you want your sole, non- depletable, non- degrading, durable, strategic asset managed without guidance? Bad Data Decisions Spiral © Copyright 2022 by Peter Aiken Slide # 30 https://anythingawesome.com
  • 21. Bad Data Decisions Spiral © Copyright 2022 by Peter Aiken Slide # 31 https://anythingawesome.com 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 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! © Copyright 2022 by Peter Aiken Slide # 32 https://anythingawesome.com
  • 22. What is Strategy? • Current use derived from military - a pattern in a stream of decisions [Henry Mintzberg] © Copyright 2022 by Peter Aiken Slide # 33 https://anythingawesome.com A thing Every Day Low Price Former Walmart Business Strategy © Copyright 2022 by Peter Aiken Slide # 34 https://anythingawesome.com
  • 23. Wayne Gretzky’s Strategy © Copyright 2022 by Peter Aiken Slide # He skates to where he thinks the puck will be ... 35 https://anythingawesome.com Strategy Example 3 © Copyright 2022 by Peter Aiken Slide # Good Guys (Us) Bad Guys (Them) 36 https://anythingawesome.com
  • 24. Strategy Example 3 © Copyright 2022 by Peter Aiken Slide # Good Guys (Us) Bad Guys (Them) 37 https://anythingawesome.com Strategy Example 3 © Copyright 2022 by Peter Aiken Slide # Good Guys (Us) Bad Guys (Them) 38 https://anythingawesome.com
  • 25. Strategy Guides Workgroup Activities © Copyright 2022 by Peter Aiken Slide # A pattern in a stream of decisions 39 https://anythingawesome.com Executive data board Oversee council • Translate commonwealth goals to agency performance targets • provide resources • Remove organizational obstacles • appoint data governance council members • Oversee the data governance council • Oversee data sharing & analytics projects © Copyright 2022 by Peter Aiken Slide # Complex Data Governance Environment Complex Data Governance Council • Liaise between agency operations & CDO • Advise CDO on technology, policy, and governance strategies • Administer data governance policies set by the board • implement data sharing & analytics projects • Review open data assets • Report progress & compliance to the Board Advise CDO 40 https://anythingawesome.com Data sharing and analytics • Identify goals and objectives • Prioritize initiatives • Study & report • Recommend changes to budget and code DATA COMMISSION LEGISLATIVE Region representatives EXECUTIVE JUDICIAL Advise Governor TRUSTEE execute • Define, approve, and communicate data strategies, policies, standards, rules, guidelines, & best practices • Provide a governance, policy, and technology framework • Define agency data governance responsibilities • Encourage & facilitate data sharing • Facilitate coordination to prevent duplication • Coordinate policy and technology proposals and recommendations • Administer and manage the commonwealth data trust • Track and enforce compliance and conformance • Oversee dissemination of open data
  • 26. 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 © Copyright 2022 by Peter Aiken Slide # 41 https://anythingawesome.com Data Strategy and Governance in Strategic Context © Copyright 2022 by Peter Aiken Slide # 42 https://anythingawesome.com 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)
  • 27. ≈ ≈ ≈ ≈ ≈ ≈ ≈ ≈ ≈ ≈ ≈ ≈ ≈ ≈ ≈ ≈ ≈ ≈ ≈ ≈ ≈ ≈ Data Governance Role: Produce systemic organizational changes that impact data and work practices over time © Copyright 2022 by Peter Aiken Slide # 43 https://anythingawesome.com 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 $ Support for Organizational Strategy Better IT Outcomes © Copyright 2022 by Peter Aiken Slide # 44 https://anythingawesome.com Better Managed/ Higher Quality Data Assets Improved Knowledge Worker Productivity
  • 28. Multiple Dependencies © Copyright 2022 by Peter Aiken Slide # 45 https://anythingawesome.com Better Managed/ Higher Quality Data Assets Better IT Outcomes Improved Knowledge Worker Productivity Support for Organizational Strategy Poor data manifests as multifaceted organizational challenges © Copyright 2022 by Peter Aiken Slide # 46 https://anythingawesome.com 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
  • 29. Consistency Encourages Quality Analysis © Copyright 2022 by Peter Aiken Slide # 47 https://anythingawesome.com 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 requires a team with specialized skills deployed to create a repeatable process and develop sustained organizational skillsets © Copyright 2022 by Peter Aiken Slide # 48 https://anythingawesome.com
  • 30. The MacGyver approach to DG uses paperclips and duct tape © Copyright 2022 by Peter Aiken Slide # 49 https://anythingawesome.com © Copyright 2022 by Peter Aiken Slide # 50 https://anythingawesome.com
  • 31. 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 © Copyright 2022 by Peter Aiken Slide # 51 https://anythingawesome.com © Copyright 2022 by Peter Aiken Slide # https://anythingawesome.com Program • Data's Confounding Characteristics – Uneven understanding – Has lead fractured views of data and to – Increasing organizational data debt 1. Keeping DG practically focused on strategy – This is a young profession and must – Directly support organizational strategy by – Improving data and its use in the short and long term 2. DG must exist at the same level as HR – In order to achieve effectiveness, – DG is central to DM (and central to digitization efforts) – Must be de-coupled from IT strategy 3. Gradually add ingredients (practicing and getting better) – Digital and data are dependent on high speed automation/data processing – Employ a DG Frameworks to refine focus – Plan to evolve (PDCA) 4. Data governance in action: Storytelling • Take Aways/References/Q&A Key Elements of a Successful Data Governance Program
  • 32. © Copyright 2022 by Peter Aiken Slide # https://anythingawesome.com Program • Data's Confounding Characteristics – Uneven understanding – Has lead fractured views of data and to – Increasing organizational data debt 1. Keeping DG practically focused on strategy – This is a young profession and must – Directly support organizational strategy by – Improving data and its use in the short and long term 2. DG must exist at the same level as HR – In order to achieve effectiveness, – DG is central to DM (and central to digitization efforts) – Must be de-coupled from IT strategy 3. Gradually add ingredients (practicing and getting better) – Digital and data are dependent on high speed automation/data processing – Employ a DG Frameworks to refine focus – Plan to evolve (PDCA) 4. Data governance in action: Storytelling • Take Aways/References/Q&A Key Elements of a Successful Data Governance Program Mismatched railroad tracks non aligned © Copyright 2022 by Peter Aiken Slide # 54 https://anythingawesome.com Data programmes driving IT programs
  • 33. Data is not a Project © Copyright 2022 by Peter Aiken Slide # 55 https://anythingawesome.com Common Organizational Data (and corresponding data needs requirements) New Organizational Capabilities Systems Development Activities Create Evolve Future State (Version +1) Data programmes drive IT programs Data evolution is separate from, external to, and precedes system development life cycle activities! What is the Difference Between DG and DM? Data Governance • Policy level guidance • Setting general guidelines/direction • Example: All information not marked public should be considered confidential • Keep in mind "firehouse" metaphore Data Management • The business function of – planning for, – controlling and – delivering – data/information assets • Example: Delivering data to solve business challenges © Copyright 2022 by Peter Aiken Slide # 56 https://anythingawesome.com
  • 34. Data Management Data Governance Program External Comprehension © Copyright 2022 by Peter Aiken Slide # 57 https://anythingawesome.com 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 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 © Copyright 2022 by Peter Aiken Slide # Adapted from http://top.idownloadnew.com/program_vs_project/ and http://management.simplicable.com/management/new/program-management-vs-project-management 58 https://anythingawesome.com Your data program must last at least as long as your HR program!
  • 35. © Copyright 2022 by Peter Aiken Slide # Metadata Management 59 https://anythingawesome.com 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 Iteration 1 © Copyright 2022 by Peter Aiken Slide # 60 https://anythingawesome.com Data Strategy Data Governance BI/ Warehouse Perfecting operations in 3 data management practice areas 1X 1X 1X Metadata Data Quality
  • 36. Iteration 2 © Copyright 2022 by Peter Aiken Slide # 61 https://anythingawesome.com Data Strategy Data Governance BI/ Warehouse Metadata 2X 2X 1X Perfecting operations in 3 data management practice areas Iteration 3 © Copyright 2022 by Peter Aiken Slide # 62 https://anythingawesome.com Data Strategy Data Governance BI/ Warehouse Reference & Master Data 1X 3X 3X Perfecting operations in 3 data management practice areas
  • 37. (Things that further) Organizational Strategy DG Lighthouse Metaphors Provides Focus © Copyright 2022 by Peter Aiken Slide # ( O c c a s i o n s t o P r a c t i c e ) N e e d e d D a t a S k i l l s ( O p p o r t u n i t i e s t o i m p r o v e ) D a t a u s e b y t h e b u s i n e s s 63 https://anythingawesome.com IT Project or Application-Centric Development © Copyright 2022 by Peter Aiken Slide # Original articulation from Doug Bagley @ Walmart 64 https://anythingawesome.com Data/ Information IT Projects • In support of strategy, organizations implement IT projects • Data/information are typically considered within the scope of IT projects • Problems with this approach: – Ensures data is formed to the applications and not around the organizational-wide information requirements – Process are narrowly formed around applications – Very little data reuse is possible Strategy
  • 38. This is the wrong way to think about data strategy © Copyright 2022 by Peter Aiken Slide # Organizational Strategy IT Strategy Data Strategy x65 https://anythingawesome.com Organizational Strategy IT Strategy This is correct … © Copyright 2022 by Peter Aiken Slide # Data Strategy 66 https://anythingawesome.com
  • 39. Data-Centric Development © Copyright 2022 by Peter Aiken Slide # Original articulation from Doug Bagley @ Walmart 67 https://anythingawesome.com Data/ Information IT Projects • In support of strategy, the organization develops specific, shared data-based goals/objectives • These organizational data goals/ objectives drive the development of specific IT projects with an eye to organization-wide usage • Advantages of this approach: - Data/information assets are developed from an organization-wide perspective - Systems support organizational data needs and compliment organizational process flows - Maximum data/information reuse Strategy © Copyright 2022 by Peter Aiken Slide # https://anythingawesome.com Program • Data's Confounding Characteristics – Uneven understanding – Has lead fractured views of data and to – Increasing organizational data debt 1. Keeping DG practically focused on strategy – This is a young profession and must – Directly support organizational strategy by – Improving data and its use in the short and long term 2. DG must exist at the same level as HR – In order to achieve effectiveness, – DG is central to DM (and central to digitization efforts) – Must be de-coupled from IT strategy 3. Gradually add ingredients (practicing and getting better) – Digital and data are dependent on high speed automation/data processing – Employ a DG Frameworks to refine focus – Plan to evolve (PDCA) 4. Data governance in action: Storytelling • Take Aways/References/Q&A Key Elements of a Successful Data Governance Program
  • 40. © Copyright 2022 by Peter Aiken Slide # https://anythingawesome.com Program • Data's Confounding Characteristics – Uneven understanding – Has lead fractured views of data and to – Increasing organizational data debt 1. Keeping DG practically focused on strategy – This is a young profession and must – Directly support organizational strategy by – Improving data and its use in the short and long term 2. DG must exist at the same level as HR – In order to achieve effectiveness, – DG is central to DM (and central to digitization efforts) – Must be de-coupled from IT strategy 3. Gradually add ingredients (practicing and getting better) – Digital and data are dependent on high speed automation/data processing – Employ a DG Frameworks to refine focus – Plan to evolve (PDCA) 4. Data governance in action: Storytelling • Take Aways/References/Q&A Key Elements of a Successful Data Governance Program Digital Insight • Subtract data from digital and what do you have? • Subtract digital from data and you still have data © Copyright 2022 by Peter Aiken Slide # 70 https://anythingawesome.com https://www.linkedin.com/in/mark-johnson-518a752/ DIGITAL DATA ? DIGITAL DATA DATA
  • 41. It isn't possible to go digital Digital © Copyright 2022 by Peter Aiken Slide # 71 https://anythingawesome.com a By just spelling 'data' Dat © Copyright 2022 by Peter Aiken Slide # 72 https://anythingawesome.com
  • 42. It requires more work Data © Copyright 2022 by Peter Aiken Slide # a 73 https://anythingawesome.com © Copyright 2022 by Peter Aiken Slide # Garbage Digital Results 74 https://anythingawesome.com Perfect Model Data Warehouse Machine Learning Business Intelligence Block Chain AI MDM Data Governance Analytics Technology GI➜GO! Garbage Data Garbage In ➜ Garbage Out!
  • 43. © Copyright 2022 by Peter Aiken Slide # Garbage Digital Results 75 https://anythingawesome.com Perfect Model Quality Data Data Warehouse AI MDM Analytics Technology Data Governance GI➜GO! Machine Learning Block Chain Business Intelligence © Copyright 2022 by Peter Aiken Slide # Garbage Digital Results 76 https://anythingawesome.com Perfect Model Quality Data Data Warehouse AI MDM Analytics Technology Data Governance GI➜GO! Machine Learning Block Chain Business Intelligence
  • 44. © Copyright 2022 by Peter Aiken Slide # Garbage Digital Results 77 https://anythingawesome.com Perfect Model Quality Data Data Warehouse AI MDM Analytics Technology Data Governance GI➜GO! Machine Learning Block Chain Business Intelligence © Copyright 2022 by Peter Aiken Slide # Quality Digital Results 78 https://anythingawesome.com Perfect Model Quality Data Data Warehouse AI MDM Analytics Technology Data Governance QI➜QO! Machine Learning Block Chain Business Intelligence
  • 45. © Copyright 2022 by Peter Aiken Slide # Quality Digital Results 79 https://anythingawesome.com Quality Data Today Machine Learning © Copyright 2022 by Peter Aiken Slide # 80 https://anythingawesome.com Data Sandwich
  • 46. Data supply Data literacy Standard data Standard data Leverage point - high performance automation © Copyright 2022 by Peter Aiken Slide # Data literacy 81 https://anythingawesome.com Data supply Leverage point - high performance automation © Copyright 2022 by Peter Aiken Slide # Standard data Data supply Data literacy 82 https://anythingawesome.com
  • 47. Leverage point - high performance automation © Copyright 2022 by Peter Aiken Slide # This cannot happen without investments in engineering and architecture! 83 https://anythingawesome.com 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! Leverage point - high performance automation © Copyright 2022 by Peter Aiken Slide # This cannot happen without investments in data engineering and architecture! 84 https://anythingawesome.com Data supply Data literacy Standard data
  • 48. Our barn had to pass a foundation inspection • Before further construction could proceed • It makes good business sense • No IT equivalent © Copyright 2022 by Peter Aiken Slide # 85 https://anythingawesome.com 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 © Copyright 2022 by Peter Aiken Slide # 86 https://anythingawesome.com
  • 49. Data Governance from the DMBOK © Copyright 2022 by Peter Aiken Slide # 87 https://anythingawesome.com from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International Data Governance Institute © Copyright 2022 by Peter Aiken Slide # 88 https://anythingawesome.com http://www.datagovernance.com/
  • 50. KiK Consulting © Copyright 2022 by Peter Aiken Slide # 89 https://anythingawesome.com http://www.kikconsulting.com/ IBM Data Governance Council © Copyright 2022 by Peter Aiken Slide # 90 https://anythingawesome.com http://www-01.ibm.com/software/data/system-z/data-governance/workshops.html
  • 51. Elements of Effective Data Governance © Copyright 2022 by Peter Aiken Slide # 91 https://anythingawesome.com See IBM Data Governance Council, http://www-01.ibm.com/software/tivoli/ governance/servicemanagement/ data-governance.html. Baseline Consulting (sas.com) © Copyright 2022 by Peter Aiken Slide # 92 https://anythingawesome.com
  • 52. American College Personnel Association © Copyright 2022 by Peter Aiken Slide # 93 https://anythingawesome.com Domain expertise is less ← | → Domain expertise is greater Roles more formally defined ← | → Roles less formally defined Encounter governed data more directly ← | → Encounter governed data less directly More time is dedicated ← | → Less time is dedicated 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 © Copyright 2022 by Peter Aiken Slide # 94 https://anythingawesome.com
  • 53. R e s o u r c e s Leadership (data decision makers) Stewards (data trustees) Guidance Decisions Participants/Experts (data subject matter experts) Other Sources/Uses (data makers & consumers) Data/feedback Changes Action Ideas Data/Feedback Components comprising the data community © Copyright 2022 by Peter Aiken Slide # 95 https://anythingawesome.com Getting Started with Data Governance © Copyright 2022 by Peter Aiken Slide # 96 https://anythingawesome.com Assess context DG roadmap Policy/Standards Reqs. Data Stewards Operational processes Executive mandate
  • 54. 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. © Copyright 2022 by Peter Aiken Slide # Illustration from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International 97 https://anythingawesome.com Primary Deliverables • Data Policies • Data Standards • Resolved Issues • Data Management Projects and Services • Quality Data and Information • Recognized Data Value © Copyright 2022 by Peter Aiken Slide # Illustration from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International 98 https://anythingawesome.com
  • 55. 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 © Copyright 2022 by Peter Aiken Slide # Illustration from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International 99 https://anythingawesome.com 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 © Copyright 2022 by Peter Aiken Slide # 100 https://anythingawesome.com from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
  • 56. 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 © Copyright 2022 by Peter Aiken Slide # 101 https://anythingawesome.com 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 © Copyright 2022 by Peter Aiken Slide # 102 https://anythingawesome.com Illustration from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
  • 57. © Copyright 2022 by Peter Aiken Slide # 103 https://anythingawesome.com Evolve © Copyright 2022 by Peter Aiken Slide # 104 https://anythingawesome.com 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
  • 58. Getting Started with Data Governance © Copyright 2022 by Peter Aiken Slide # 105 https://anythingawesome.com 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 © Copyright 2022 by Peter Aiken Slide # https://anythingawesome.com Program • Data's Confounding Characteristics – Uneven understanding – Has lead fractured views of data and to – Increasing organizational data debt 1. Keeping DG practically focused on strategy – This is a young profession and must – Directly support organizational strategy by – Improving data and its use in the short and long term 2. DG must exist at the same level as HR – In order to achieve effectiveness, – DG is central to DM (and central to digitization efforts) – Must be de-coupled from IT strategy 3. Gradually add ingredients (practicing and getting better) – Digital and data are dependent on high speed automation/data processing – Employ a DG Frameworks to refine focus – Plan to evolve (PDCA) 4. Data governance in action: Storytelling • Take Aways/References/Q&A Key Elements of a Successful Data Governance Program
  • 59. © Copyright 2022 by Peter Aiken Slide # https://anythingawesome.com Program • Data's Confounding Characteristics – Uneven understanding – Has lead fractured views of data and to – Increasing organizational data debt 1. Keeping DG practically focused on strategy – This is a young profession and must – Directly support organizational strategy by – Improving data and its use in the short and long term 2. DG must exist at the same level as HR – In order to achieve effectiveness, – DG is central to DM (and central to digitization efforts) – Must be de-coupled from IT strategy 3. Gradually add ingredients (practicing and getting better) – Digital and data are dependent on high speed automation/data processing – Employ a DG Frameworks to refine focus – Plan to evolve (PDCA) 4. Data governance in action: Storytelling • Take Aways/References/Q&A Key Elements of a Successful Data Governance Program Non-Fungible Tokens © Copyright 2022 by Peter Aiken Slide # 108 https://anythingawesome.com $48M. $280
  • 60. 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 … © Copyright 2022 by Peter Aiken Slide # 109 https://anythingawesome.com 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, 2008 • the 179 contracts were marked as “hidden” in Excel, and those entries became “un-hidden” when when globally reformatting the document … • … and the sale closed … https://www.businessinsider.com/2008/10/barclays-excel-error-results-in-lehman-chaos © Copyright 2022 by Peter Aiken Slide # 110 https://anythingawesome.com
  • 61. Excel spreadsheet error blamed for UK’s 16,000 missing coronavirus cases © Copyright 2022 by Peter Aiken Slide # 111 https://anythingawesome.com The UK failed to add nearly 16,000 confirmed cases of coronavirus to its national track and trace system due to an Excel error. A number of reports, including from The Guardian, Sky News, and The Daily Mail, say the mistake was caused when an Excel spreadsheet used to track confirmed cases of the virus reached its maximum file size and failed to update. "Failure to upload these cases to the national database meant anyone who came into contact with these individuals was not informed. It’s an error that may have helped spread the virus further through the country as individuals exposed to the virus continued to act as normal." Fan Blade Sensor • 1 Sensor – Probabilistic (generalist) maintenance forecasts • 100 Sensors – Establish optimal monitoring targets – Finer tuned and safer maintenance – Mission Readiness ??? – Storage $$$ – Handling $$$ – Opportunity $$$ – Systemic $$$ – Maintenance $$$ – Total > $1.5 Billion © Copyright 2022 by Peter Aiken Slide # 112 https://anythingawesome.com
  • 62. • Data's Confounding Characteristics – Uneven understanding – Has lead fractured views of data and to – Increasing organizational data debt 1. Keeping DG practically focused on strategy – This is a young profession and must – Directly support organizational strategy by – Improving data and its use in the short and long term 2. DG must exist at the same level as HR – In order to achieve effectiveness, – DG is central to DM (and central to digitization efforts) – Must be de-coupled from IT strategy 3. Gradually add ingredients (practicing and getting better) – Digital and data are dependent on high speed automation/data processing – Employ a DG Frameworks to refine focus – Plan to evolve (PDCA) 4. Data governance in action: Storytelling • Take Aways/References/Q&A © Copyright 2022 by Peter Aiken Slide # Program 113 https://anythingawesome.com Key Elements of a Successful Data Governance Programs Take Aways • Need for DG is increasing – Increase in data volume – Lack of rigorous practice improvement • DG is a new discipline – Must conform to constraints – No one best way • DG must be driven by 4 key elements 1. Keep DG practically focused on strategy 2. Implement DG (and data) as a program not a project 3. Gradually add ingredients 4. Learn the value of stories/storytelling • The goal is to improve DG effectiveness and efficiencies (and the data itself) over time • The more data literate the organization, the easier the transformation © Copyright 2022 by Peter Aiken Slide # 114 https://anythingawesome.com Image adapted from: https://www.everythingneon.com/proddetail.php?prod=n105-0826-take-away-neon-sign
  • 63. By the book • Data Governance: How to Design, Deploy, and Sustain an Effective Data Governance Program • John Ladley • Amazon Best Sellers Rank: #641,937 in Books (See Top 100 in Books) – #242 in Management Information Systems – #209 in Library Management – #380 in Database Storage & Design © Copyright 2022 by Peter Aiken Slide # 115 https://anythingawesome.com References Websites • Data Governance Book Data Governance Book Compliance Book © Copyright 2022 by Peter Aiken Slide # 116 https://anythingawesome.com
  • 64. IT Governance Books © Copyright 2022 by Peter Aiken Slide # 117 https://anythingawesome.com Event Pricing © Copyright 2022 by Peter Aiken Slide # 118 https://anythingawesome.com • 20% off directly from the publisher on select titles • My Book Store @ http://anythingawesome.com • Enter the code "anythingawesome" at the Technics bookstore checkout where it says to "Apply Coupon" anythingawesome
  • 65. [ Clicking any webinar title will link directly to the registration page ] Upcoming Events Data Preparation Fundamentals 14 June 2022 Conceptual vs. Logical vs. Physical Data 12 July 2022 The Importance of Metadata 9 August 2022 © Copyright 2022 by Peter Aiken Slide # 119 https://anythingawesome.com Brought to you by: Time: 19:00 UTC (2:00 PM NYC) | Presented by: Peter Aiken, PhD Peter.Aiken@AnythingAwesome.com +1.804.382.5957 Thank You! © Copyright 2022 by Peter Aiken Slide # 120 Book a call with Peter to discuss anything - https://anythingawesome.com/OfficeHours.html Critical Design Review? Hiring Assistance? Reverse Engineering Expertise? Executive Data Literacy Training? Mentoring? Tool/automation evaluation? Use your data more strategically?
  • 66. © Copyright 2022 by Peter Aiken Slide # 121 https://anythingawesome.com As a topic, data is ... Complex & detailed • Outsiders do not want to hear about or discuss any aspects of challenges/solutions • Most are unqualified re: architecture/ engineering Taught inconsistently • Focus is on technology • Business impact is not addressed Not well understood • (Re)learned by every workgroup • Lack of standards/ poor literacy/ unknown dependencies © Copyright 2022 by Peter Aiken Slide # Wally Easton Playing Piano https://www.youtube.com/watch?v=NNbPxSvII-Q 122 https://anythingawesome.com
  • 67. © Copyright 2022 by Peter Aiken Slide # 123 https://anythingawesome.com What is the most effective use of steward investments? Data Strategy and Governance in Strategic Context © Copyright 2022 by Peter Aiken Slide # 124 https://anythingawesome.com (Business Goals) (Metadata) Data asset support for organizational strategy What the data assets do to better support strategy How well the data strategy is working Organizational Strategy Data Governance Data Strategy Data Stewards (Metadata/ Business Goals) Progress, plans, problems
  • 68. Toyota versus Detroit Engine Mounting (Circa 1994) • Detroit – 3 different bolts – 3 different wrenches – 3 different bolt inventories • Toyota – 1 bolt used for all three assemblies – 1 bolt inventory – 1 type of wrench © Copyright 2022 by Peter Aiken Slide # 125 https://anythingawesome.com Toyota versus Detroit Engine Mounting (Circa 1994) • Detroit – many different bolts – many different wrenches – many different bolt inventories • Toyota – same bolts used for all three assemblies – same 1 bolt inventory – same 1 type of wrench © Copyright 2022 by Peter Aiken Slide # 126 https://anythingawesome.com
  • 69. © Copyright 2022 by Peter Aiken Slide # 127 https://anythingawesome.com