Approaching Data Quality

© Copyright 2021 by Peter Aiken Slide # 1
peter.aiken@anythingawesome.com +1.804.382.5957 Peter Aiken, PhD
Approaching
Data Quality
Engineering Business Success Stories
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 (plusanythingawesome.com)
• Experienced w/ 500+ data
management practices worldwide
• Multi-year immersions
– US DoD (DISA/Army/Marines/DLA)
– Nokia
– Deutsche Bank
– Wells Fargo
– Walmart …
• 12 books and
dozens of articles
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

3
© Copyright 2021 by Peter Aiken Slide #
Approaching Data Quality
Engineering Success Stories
Program
• Approaching Data Quality
– Definitions
– Causes can be difficult to discern
– Data quality challenges are the
root cause of most IT and
business failures
– Must be built on leverage
– Requires a programmatic
approach to be most effective
– Early business cases
often have a dual purpose
– High quality data requires
architecture and engineering
• What do we need to get
better at?
– Systems thinking
– Not looking at data
quality in isolation
– Understanding data ROT
– Not underestimating
the role of culture
– Developing repeatable
capabilities/core data
quality expertise
• How do we get better?
– Refocus the request around
business outcomes
– Leadership
– Program focus
– Math (cost or investment?)
– Storytelling/Practice
• Takeaways and Q&A
https://anythingawesome.com https://www.youtube.com/watch?v=uL2PsmlGn9g

Definitions
• Quality Data
– Fit for purpose meets the requirements of its authors,
users, and administrators (from Martin Eppler)
– Synonymous with information quality, since poor data
quality results in inaccurate information and poor performance
• Data Quality Management
– "Planning, implementation and control activities that apply quality
management techniques to measure, assess, improve, and
ensure data quality"
– Encompasses life cycle activities
– Include supporting processes from change management, etc.
– Continuous improvement process requiring core capabilities
• Data Quality Engineering
– Recognition that data quality solutions cannot not managed but
must be engineered
– Data quality engineering concepts are generally not known and understood
within IT or business!
https://anythingawesome.com Spinach/Popeye story from http://it.toolbox.com/blogs/infosphere/spinach-how-a-data-quality-mistake-created-a-myth-and-a-cartoon-character-10166
DQ Effort Pattern
from The DAMA Guide to the Data Management Body of Knowledge © 2017 by DAMA International
80% time spent 20%

Hidden Data Factories
https://hbr.org/2016/09/bad-data-costs-the-u-s-3-trillion-per-year
Work products are
delivered to
Customers
Customers
Knowledge Workers
80% looking for stuff
20% doing useful work
Department B
1. Check A's work
2. Make any corrections
3. Complete B's work
4. Deliver to Department C
Department A
https://en.wikipedia.org/wiki/Theory_of_constraints
Department C
1. Check B's work
2. Make any corrections
3. Complete C's work
4. Deliver to Customer
5. Deal with consequences
Blind Persons and the Elephant
https://anythingawesome.com 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!

Events are not always recognized as data quality challenges?
• Letters from a bank
• A very expensive, very
small data rounding error
• Health data story
• The chocolate story
• Covid-19
A congratulations
letter from another
bank
Problems
• Bank did not know it
made an error
• Tools alone could not
have prevented this
error
• Lost confidence in the
ability of the bank to
manage customer
funds

• Needed trench for electrical
cable 2.52" - delivered 2.5"
• $1M required to rent other
facilities while new cable is obtained
• Either rounding or truncation could explain
– We need to get a summary on all of this," he said.
"How did the mistake occur? Who's at fault? What
are the damages? And how is money going to be recovered?"
Port of Seattle
2.52" ➜ 2.5"
This research, published as a letter
this week in the British Medical
Journal, was meant to draw attention
to how much data gets entered
incorrectly in the country’s medical
system. These guys weren’t turning
up at the doctor for pregnancy-
related services. Instead, they were
at their doctor for procedures that
had medical codes similar to those of
midwifery and obstetric services.
With a misplaced keystroke here or
there, an annual physical could
become a consultation with a
midwife.
Why Britain has 17,000 pregnant men
here
keystroke
misplaced
there

Areyougonnatellusthechocolatestory-again?
Why using Microsoft's tool caused Covid-19 results to be lost
https://www.bbc.com/news/technology-54423988?es_p=12801491

https://www.bbc.com/news/technology-54423988?es_p=12801491
• Since 2007 should have
been forced to use .xlsx
(1,000,000+ rows)
• Used .xls (65,000 rows)
• Additional data was
dropped without
notification
Practice-Oriented
• Failure in rigor when capturing/
manipulating data
• Allowing imprecise or incorrect data to
be collected when requirements specify
otherwise
• Presenting data out of sequence
Structure-Oriented
• Data and metadata arranged
imperfectly
• Data is captured but inaccessible
• When a incorrect data is provided as
the correct response
Practice-oriented
activities focus on the
capture and manipulation
of data
Data quality best
practices depend
on both
Structure-oriented
activities focus on the
data implementation
Quality
"Fit for
purpose"
Data

Poor data manifests as multifaceted organizational challenges
Root cause analysis is required to diagnose
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

Many DQ challenges are unique and/or context specific!
Burning Bridge
• Something bad happened
– Imperfect data was to blame
• Someone needs to fix
– Poor quality data
• You currently have
management's attention
– It is wise to ensure you
also have their understanding
• "Do something" often
leads to "Buy something"
– Mostly technology-based
• Get data quality-ing!
– A fool with a tool is still a fool
• Something is accomplished
– Most often all the funding is used up
• Early cases have a dual purpose
– Make the case that this will fix the
immediate challenge
– Illustrate why a programmatic approach
is preferable

Leverage is an Engineering Concept
• Using proper engineering techniques, a human can lift a bulk that
is weighs much more than the human
1 kg
10 kg
11 kg
A wholistic approach to obtaining data leverage
Organizational
Data
Knowledge workers
supplemented by
data professionals
Process
Guided by strategy
https://www.computerhope.com/jargon/f/framework.htm
People
Technology
Reducing ROT increases data leverage

Data Leverage is a multi-use concept
• Permits organizations to better manage their data
– Within the organization, and
– With organizational data exchange partners
– In support of the organizational mission
• Leverage
– Obtained by implementation of data-centric
technologies, processes, and human skill sets
– Focus on the non-ROT data
• The bigger the organization, the greater potential leverage exists
• Treating data more asset-like simultaneously
– Lowers organizational IT costs and
– Increases organizational knowledge worker productivity
Concrete example of data leverage
• Reference
– Controls accessible data values
• Master
– Controls access to system capabilities
• Transaction
– Instances of values
Countries where we do business?
Types of accounts available?
Controlled vocabulary items
Are you a member of our premium club?
Authorizing uses/users?
Common/standard data structures
$5
Authorized
Like !
Example based on: Dr. Christopher Bradley of DMAdvisors–he has more, ping him at chris.bradley@dmadvisors.co.uk
Cannot do business overseas?
Cannot determine product origin?
Cannot add a foreign language to the website?
Cannot select a valid menu item?

Simple Math
• At the beginning of a project,
• Where the parties know the least about each other
• All are expected to agree on the meaning of price, timing, and
functionalities
• Define X (some resources)
• Define Y (cleaning 1 set of data)
• Define Z (that data will be clean)
If X is invested in Y then outcome Z will result (Z > X)
Simple Math
• Define X ($100)
• Define Y (cleaning 1 set of data)
• Define Z ($1000)
If $100 is invested in cleaning 1 set of data then outcome $1000 will result

Data is not a Project
• Durable asset
– An asset that has a usable
life more than one year
• Reasonable project
deliverables
– 90 day increments
– Data evolution is measured in years
• Data
– Evolves - it is not created
– Significantly more stable
• Readymade data architectural components
– Prerequisite to agile development
• Only alternative is to create additional data siloes!
https://anythingawesome.com 27
Differences between Programs and Projects
• Programs are Ongoing, Projects End
– Managing a program involves long term strategic planning and
continuous process improvement is not required of a project
• Programs are Tied to the Financial Calendar
– Program managers are often responsible for delivering
results tied to the organization's financial calendar
• Program Management is Governance Intensive
– Programs are governed by a senior board that provides direction,
oversight, and control while projects tend to be less governance-intensive
• Programs Have Greater Scope of Financial Management
– Projects typically have a straight-forward budget and project financial
management is focused on spending to budget while program planning,
management and control is significantly more complex
• Program Change Management is an Executive Leadership
Capability
– Projects employ a formal change management process while at the program
level, change management requires executive leadership skills and program
change is driven more by an organization's strategy and is subject to market
conditions and changing business goals
Adapted from http://top.idownloadnew.com/program_vs_project/ and http://management.simplicable.com/management/new/program-management-vs-project-management
28
Your data quality
program must last at
least as long as your
HR program!

Making a Better Quality Data Sandwich
Data supply
Data literacy
Standard data
Standard data
Leverage point - high performance automation
Data literacy
30
Data supply

Standard data
Data supply
Data literacy
31
This cannot happen without engineering and architecture!
Quality engineering/
architecture work products
do not happen accidentally!
32
Data supply
Data literacy
Standard data

This cannot happen without data engineering and architecture!
33
Quality data engineering/
architecture work products
do not happen accidentally!
Data supply
Data literacy
Standard data
USS Midway
& Pancakes
Why is this an excellent
example of engineering?
• It is tall
• It has a clutch
• It was built in 1942
• It is cemented to the floor
• It is still in regular use!

Our barn had to pass a foundation inspection
• Before further construction could proceed
• No IT equivalent
https://plusanythingawesome.com
What does is mean "data quality program"
• Ongoing commitment
– Permits evolutionary improvement of the approach
• Governance
– Senior level coordination, direction, and control
• Executive leadership capabilities
– Change and risk management
• Data quality approach inherits (above)
– Budget, strategic priorities
– Senior level attention and improving topical facility
– Reasonable timelines/expectations
https://blog.ducenit.com/data-quality-management

37
Program
– Definitions
business failures
better at?
the role of culture
quality expertise
business outcomes
– Leadership
– Program focus
Systems Thinking
http://victorianscandal.wordpress.com/picturesque/rachel-olshausen/
• A framework that is based on
the belief that the component
parts of a system can best
be understood in the
context of relationships
with other systems, rather than
in isolation.
• The only way to fully
understand why a problem or
element occurs and persists is
to understand the part in
relation to the whole.
Capra, F. (1996) The web of life: a new scientific understanding of living
systems (1st Anchor Books ed). New York: Anchor Books. p. 30

Process
Input ➜ Process ➜ Output Diagram
Inputs Outputs
Pizza
Make Pizza
Dough
Water
Pizza
Crust
Make Crust
Make Pizza
Data Steward Quality
Responsibilities
• Inputs
– From where, do
each of these my
responsible data
items come?
– Why are they
produced?
– What level of quality
is required by 'my
processes?'
• Process
– What business
processes use the
data within my
fiduciary
responsibility?
– For what business
purpose do they use
each data item?
– What role does
quality play for my
processes to
contribute?
• Output
– What downstream
business processes
consume data that
was under my
fiduciary care?
– For what purpose
are each data items
consumed?
– What quality
attribute are
required by each
downstream
consumer?

Interdependencies
Data Governance
ERP
Data Quality
Data
Management
Body
of
Knowledge
(DM
BoK
V2)
Practice
Areas
from The DAMA Guide to the Data Management Body of Knowledge 2E © 2017 by DAMA International

Data
Strategy
Data
Governance
BI/
Warehouse
Perfecting
operations in 3
data management
practice areas
1X
1X
1X
Metadata
Data
Quality
from The DAMA Guide to the Data Management Body of Knowledge 2E © 2017 by DAMA International
Separating the Wheat from the Chaff

https://plusanythingawesome.com 45
Is well organized data worth more?
Pre-Information Age Metadata
• Examples of information architecture achievements that
happened well before the information age:
– Page numbering
– Alphabetical order
– Table of contents
– Indexes
– Lexicons
– Maps
– Diagrams
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
• Data that is better organized 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

Multiple Sources of Master/Reference Data
Payroll Application
(3rd GL)
Payroll Data
(database)
R& D Applications
(researcher supported, no documentation)
R & D
Data
(raw) Mfg. Data
(home grown
database)
Mfg. Applications
(contractor supported)
Marketing Application
(4rd GL, query facilities,
no reporting, very large)
Marketing Data
(external database)
Finance
Data
(indexed)
Finance Application
(3rd GL, batch
system, no source)
Personnel App.
(20 years old,
un-normalized data)
Personnel Data
(database)
49
https://www.forbes.com/sites/ciocentral/2019/01/02/what-we-learned-from-top-execs-about-their-big-data-and-ai-initiatives/
2020
0%
25%
50%
75%
100%
% of challenges: technology % of challenges: people/process
90%
10%
Culture's impact
• 2019 challenges
– 5% technology
– 95% people/process
• 2020 challenges
– 10% technology
– 95% people/process

Change Management & Leadership
Diagnosing Organizational Readiness
adapted from the Managing Complex Change model by Lippitt, 1987
Culture is the biggest impediment to a
shift in organizational thinking about data!
52

Consistency Encourages Quality Analysis
IT
System
Business
Challenge
Business
Process
Business
Challenge
IT
Process
Business
Challenge
Business
System
Business
Challenge
IT
Process
Business
Challenge
IT
System
Business
Challenge
Business
Process
Business
Challenge
Eliminating data debt
requires a team with
specialized skills
deployed to create a
repeatable process
and develop sustained
organizational
skillsets
1. Allow the form of the
Problem to guide the
form of the solution
2. Provide a means of
decomposing the problem
3. Feature a variety of tools
simplifying system understanding
4. Offer a set of strategies for evolving a design solution
5. Provide criteria for evaluating the quality of the various solutions
6. Facilitate development of a framework for developing
organizational knowledge.
Programmatic
Data Quality
Engineering

Structured Approaches to Data Quality
• Use organizational challenges to
guide the form of quality remediation
• Decompose implementation in a
manner that will be seen by all as
helping to address specific
challenges
• Aid the implementation using a
variety of techniques (not just tools)
• Develop a series of progressively
stronger strategies for addressing
the challenges
• Provide meaningful feedback on
progress
• Facilitate development of a data-
centric framework for
institutionalizing organizational data
quality knowledge
https://en.wikipedia.org/wiki/Theory_of_constraints
(TOC)
• A management paradigm that views any
manageable system as being limited in
achieving more of its goals by a small
number of constraints(Eliyahu M. Goldratt)
• There is always at least one constraint, and
TOC uses a focusing process to identify the
constraint and restructure the rest of the
organization to address it
• TOC adopts the common idiom "a chain
is no stronger than its weakest link,"
processes, organizations, etc., are
vulnerable because the weakest
component can damage or break them or
at least adversely affect the outcome

The DQE Cycle
• Deming cycle
• "Plan-do-study-act" or
"plan-do-check-act"
– Identifying data issues that are
critical to the achievement of
business objectives
– Defining business requirements
for data quality
– Identifying key data quality
dimensions
– Defining business rules critical
to ensuring high quality data
The DQE Cycle: (1) Plan
• Plan for the assessment of
the current state and
identification of key metrics
for measuring quality
• The data quality engineering
team assesses the scope of
known issues
– Determining cost and impact
– Evaluating alternatives for
addressing them

The DQE Cycle: (2) Deploy
• Deploy processes for
measuring and improving
the quality of data:
• Data profiling
– Institute inspections and
monitors to identify data issues
when they occur
– Fix flawed processes that are
the root cause of data errors or
correct errors downstream
– When it is not possible to
correct errors at their source,
correct them at their earliest
point in the data flow
The DQE Cycle: (3) Monitor
• Monitor the quality of data
as measured against the
defined business rules
• If data quality meets defined
thresholds for acceptability,
the processes are in control
and the level of data quality
meets the business
requirements
• If data quality falls below
acceptability thresholds,
notify data stewards so they
can take action during the
next stage

The DQE Cycle: (4) Act
• Act to resolve any identified
issues to improve data
quality and better meet
business expectations
• New cycles begin as new
data sets come under
investigation or as new data
quality requirements are
identified for existing data
sets
Starting
point
for new
system
development
data performance metadata
data architecture
data
architecture and
data models
shared data updated data
corrected
data
architecture
refinements
facts &
meanings
Metadata &
Data Storage
Starting point
for existing
systems
Metadata Refinement
• Correct Structural Defects
• Update Implementation
Metadata Creation
• Define Data Architecture
• Define Data Model Structures
Metadata Structuring
• Implement Data Model Views
• Populate Data Model Views
Data Refinement
• Correct Data Value Defects
• Re-store Data Values
Data Manipulation
• Manipulate Data
• Updata Data
Data Utilization
• Inspect Data
• Present Data
Data Creation
• Create Data
• Verify Data Values
Data Assessment
• Assess Data Values
• Assess Metadata
Extended data life cycle model with metadata sources and uses

Data Quality Attributes
64
Program
– Definitions
business failures
better at?
the role of culture
quality expertise
business outcomes
– Leadership
– Program focus

Engineers say: Business wants to hear:
Clean some data
Decrease the number of
undeliverable targeted marketing
ads
Reorganize the database
Increase the ability of the
salesforce to
perform their own analyses
Develop a taxonomy
Create a common vocabulary for
the organization
Optimize a query
Shaved 1 second off a task that
runs a billion times a day
Reverse engineer the legacy
system
Understand: what was good
about the old system so it can be
formally preserved and, what
was bad so it can be improved
Compare the utility of data quality conversation topics
CDO Agenda
Inventory Data -> uncovering
assets & decreasing ROT
Develop the first version of an
organizational data strategy
Monetize your organization's data
The CDOs goal is to better manage data as an organizational
asset in support of the organizational mission!

Data Asset Inventory (Implementation)
1. Purpose is the goal of understanding, not definitions
– Definitions are passive, purpose statements incorporate strategic elements,
the rationale and justification based on the need for data
2. The sharing of inventoried data assets are categorized as:
A. Data items that are shared with external organizations
B. Data items that are shared within the organization
C. Data items that are not shared but are used to derive shared data items
D. Data items not shared outside but used to support workgroup activities
E. Organizational data ROT
3. Assign each data asset inventoried, an existing subject area from which
that data item best supports the organizational mission (ex. PAY is part of
BACK OFFICE OPERATIONS) – based on (refine-able) purpose statements,
primary subject-area allegiance is posited
4. Identify, de-dupe and harmonize data assets participating in synonyms/
homonym/other challenges - ensure only one item is designated as a
(current) golden source
5. Identify which data items are deemed to be sensitive or personal data items
and what specific controls need to be in place
6. Document all mapping rules for data items in categories 2A and 2B above
Note: this exercise cannot be comprehensively performed in a single cycle so equally as important as the exercise itself, a
processing system needs to be established so that as other data items are inevitably discovered, this inventory can be easily updated
$
What is Strategy?
• Current use derived from military
- a pattern in a stream of decisions
[Henry Mintzberg]
A thing

Q1
Organizations
without
a formalized
data quality focus
Q4
Data Quality
Focus: both,
simultaneously
Q2
Data Quality
Focus: Increase
organizational
efficiencies/effectiveness
Improve Operations
Innovation
Initially pick one or the other but not both
x
x
Q3
Data Quality
Focus: Use data
to create |
strategic |
opportunities |
Math
• VCU
– $5m 35 year faculty member
– +$20 million in grants/funded
research projects/student
supplemental salaries
• Collaborations
– Range
– $0 ← (range) →+$1.5 billion
documented savings
–
• My introduction is often:
–Peter is a professor
with a positive
cash flow!

A musical analogy that works for both practice and storytelling
+ =
https://www.youtube.com/watch?v=4n1GT-VjjVs&frags=pl%2Cwn
12.5
25
37.5
50
Monday Tuesday Wednesday Thursday Friday
48
24
12
6
3
Pandemic Math Question? (a very bad week)
• If demand at a 48-bed hospital facility is doubling-daily …
• … at what point does anyone notice that the hospital beds are
becoming scarce?
– Monday 3 beds occupied
– Tuesday 6
– Wednesday – ¾ of all beds were available
– Yesterday – ½ of all beds were available
– Today – zero beds available
– Tomorrow …???

– Definitions
business failures
better at?
the role of culture
quality expertise
business outcomes
– Leadership
– Program focus
Program
Famous 1990's Words?
• Question:
– Why haven't organizations taken a
more proactive approach to data quality?
• Answer:
– Fixing data quality problems is not easy
– It is dangerous -- they'll come after you
– Your efforts are likely to be misunderstood
– You could make things worse
– Now you get to fix it
• A single data quality
issue can grow
into a significant,
unexpected
investment

• Information transparency
• Analytics
• Business Intelligence
• Increasing efficiencies
• Decreasing costs
• Driving holistic decision-making
across the organization
• Information transparency
• Analytics
• Business Intelligence
• Increasing efficiencies
• Decreasing costs
across the organization
High
Quality
Data is
Critical
N
o
t
H
e
l
p
f
u
l
• Information transparency $
• Analytics $
• Business Intelligence $
• Increasing efficiencies $
• Decreasing costs $
across the organization $
Data Quality Dimensions

Data Value Quality
Data Representation Quality

Data Model Quality
Data Architecture Quality

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Data
Things
Happen
Organizational
Things
Happen
This approach only works if
• We know where the data that needs to be fixed–resides
• We can communicate precisely and correctly amongst team
members
• We are adept with the correct technological support
• …
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1 The project needs to be small Projects should not be allowed to
begin unless the data
requirements for the entire
project are verified
2 The product Owner or sponsor
must be highly skilled
Few in IT have the requisite data
skills and knowledge
3 The process must be agile Agile is a construction technique/
data requires more planning
before construction
4 The agile team must be highly
skilled in both the agile process
and the technology
Few agile teams have requisite
levels of data skills
5 The organization must be highly
skilled at emotional maturity
Few organizations understand
data stuff
Winning Cards for data quality program success

Approaching Data Quality

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Approaching Data Quality