DataEd Slides: Getting Data Quality Right – Success Stories

© Copyright 2020 by Peter Aiken Slide # 1paiken@plusanythingawesome.com+1.804.382.5957 Peter Aiken, PhD
Getting Data Quality Right
Engineering 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)
• Founder, Data Blueprint (datablueprint.com)
• DAMA International (dama.org)
• MIT CDO Society (iscdo.org)
• Anything Awesome (plusanythingawesome.com)
• 11 books and dozens of articles
• Experienced w/ 500+ data
management practices worldwide
• Multi-year immersions
– US DoD (DISA/Army/Marines/DLA)
– Nokia
– Deutsche Bank
– Wells Fargo
– Walmart …
© Copyright 2020 by Peter Aiken Slide # 2https://plusanythingawesome.com
PETER AIKEN WITH JUANITA BILLINGS
FOREWORD BY JOHN BOTTEGA
MONETIZING
DATA MANAGEMENT
Unlocking the Value in Your Organization’s
Most Important Asset.

3
Program
© Copyright 2020 by Peter Aiken Slide #https://plusanythingawesome.com
Getting Data Quality Right -
Success Stories
• Adopt a broad definition to data quality
• Understand data quality in the
broader context of organizational data use
• Approach data quality as an engineering challenge
• Putting a price on data quality
• Savings based stories
• Innovation based stories
• Non-monetary stories
• Takeaways and Q&A

How to solve this data quality problem using just tools?
© Copyright 2020 by Peter Aiken Slide #
Microwave Oven retail price was $40
4https://plusanythingawesome.com

4/6/12
Why Britain has 17,000
pregnant men
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.
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.

© Copyright 2020 by Peter Aiken Slide #https://www.washingtonpost.com/us-policy/2020/06/25/irs-stimulus-checks-dead-people-gao/
https://slate.com/business/2020/06/irs-stimulus-check-dead-people-yawn.html
Payments sent 160,000,000
to dead taxpayers 1,100,000
error rate 0.4%
(substantive?) $1.4 billion
Note: IRS lawyers determined "did not have legal authority to
deny payments to those who filed a return for 2019, even if
they were deceased at the time of payment” and checking
with SSA might have slowed the distribution process down.
From SLATE:
• The real headline here should really
be that the government did its job
pretty well!
• Speed was the priority
• Within two weeks, the IRS delivered
80 million payments electronically
• "Speed is part of the reason that
poorer households were able to start
spending normally again by May
despite an unemployment rate
rivaling the Great Depression’s."

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
• Likely did not have the
ability to respond

Year 2000 (or Y2K) Bug
• Before the internet
- Computing resources were expensive
- It was worth the tradeoff to represent the year
field using two digits
- 1959 was represented to the computer as 59
- Subtracting 59 from 99 yields the correct answer
40 (for dates prior to 2000/01/01!)
- No one expected those programs to still be in use
- Documentation was poorly created/maintained
• If all these fields were not expanded to
four digits before 2000/01/01 then date
calculations will not give correct results
- Subtracting 59 from 00 yields the incorrect
answer -41
- No one knew how long this would take or
cost–only when it must be completed!
On the OFFICIAL Clock of the United States at 1
second BEFORE Midnight showed:
December 31, 1999 11:59:59
One SECOND later the OFFICIAL Clock of the United
States showed:
January 1, 19100 00:00:01

US DoD Reverse Engineering Program Manager
• "Your first project is to keep me from
having to testify to a Congressional Hearing!"
(Belkis Leon-Hong former ASD-C3I)
• Problem:
– 37 systems paid personnel within DoD
– How many were needed?
– How many potential losers?
– What do you mean by employee?
• Process modeling
– Inconclusive results
• Data reverse engineering - definitive
– One legged engineer,
working in waist deep waters,
underneath rotating helicopter blades,
on overtime

Why using Microsoft's tool caused Covid-19 results to be lost
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

Reasonable individuals disagree on basic definitions
"Data quality
is part of
data management"
"Data management
is part of
data quality"

Poor data manifests as multifaceted organizational challenges

Poor data quality manifests as multifaceted organizational challenges
IT
System
Business
Challenge
Business
Process
Business
Challenge
IT
Process
Business
Challenge
Business
System
Business
Challenge
IT
Process
Business
Challenge
IT
System
Business
Challenge
Business
Process
Business
Challenge
Poor results

The Blind Men and the Elephant
(Source: John Godfrey Saxe's ( 1816-1887) version of the famous Indian legend )
• It was six men of Indostan, To learning much inclined,
Who went to see the Elephant
(Though all of them were blind),
That each by observation
Might satisfy his mind.
• The First approached the Elephant,
And happening to fall
Against his broad and sturdy side,
At once began to bawl:
"God bless me! but the Elephant
Is very like a wall!"
• The Second, feeling of the tusk
Cried, "Ho! what have we here,
So very round and smooth and sharp? To me `tis mighty clear
This wonder of an Elephant
Is very like a spear!"
• The Third approached the animal,
And happening to take
The squirming trunk within his hands, Thus boldly up he spake:
"I see," quoth he, "the Elephant
Is very like a snake!"
• The Fourth reached out an eager hand, And felt about the knee:
"What most this wondrous beast is like Is mighty plain," quoth he;
"'Tis clear enough the Elephant
Is very like a tree!"
• The Fifth, who chanced to touch the ear, Said: "E'en
the blindest man
Can tell what this resembles most;
Deny the fact who can,
This marvel of an Elephant
Is very like a fan!"
• The Sixth no sooner had begun
About the beast to grope,
Than, seizing on the swinging tail
That fell within his scope.
"I see," quoth he, "the Elephant
Is very like a rope!"
• And so these men of Indostan
Disputed loud and long,
Each in his own opinion
Exceeding stiff and strong,
Though each was partly in the right,
And all were in the wrong!

• Problem:
– Most organizations approach
data quality problems in the same way
that the blind men approached the elephant - people tend to see only the data
that is in front of them
– Little cooperation across boundaries, just as the blind men were unable to
convey their impressions about the elephant to recognize the entire entity.
– Leads to confusion, disputes and narrow views
• Solution:
– Data quality engineering can help achieve a more complete picture and facilitate
cross boundary communications
No universal conception of data
quality exists, instead many
differing perspective compete

Definitions
• Quality Data
– Fit for purpose meets the requirements of its authors, users,
and administrators (adapted from Martin Eppler)
– Synonymous with information quality, since poor data quality
results in inaccurate information and poor business performance
• Data Quality Management
– Planning, implementation and control activities that apply quality
management techniques to measure, assess, improve, and
ensure data quality
– Entails the "establishment and deployment of roles, responsibilities
concerning the acquisition, maintenance, dissemination, and
disposition of data" http://www2.sas.com/proceedings/sugi29/098-29.pdf
✓ Critical supporting process from change management
✓ Continuous process for defining acceptable levels of data quality to meet
business needs and for ensuring that data quality meets these levels
• Data Quality Engineering
– Recognition that data quality solutions cannot not managed but must be engineered
– Engineering is the application of scientific, economic, social, and practical knowledge
in order to design, build, and maintain solutions to data quality challenges
– Engineering concepts are generally not known and understood within IT or business!
© Copyright 2020 by Peter Aiken Slide # 16https://plusanythingawesome.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

Quality Data is ...
Fit
For
Purpose

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Success Stories

Separating the Wheat from the Chaff
• 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
– 54% of data is unidentified (plus)
32% ROT =
14% business critical?
© Copyright 2020 by Peter Aiken Slide #https://plusanythingawesome.com 19https://plusanythingawesome.comhttps://plusanythingawesome.com

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

My most profound lesson! (so far)
+

Perfect
Model
Poor
Quality
Data
Poor
Results
Data
Warehouse
Machine
Learning
Business
Intelligence
Block ChainAIMDM
Data
Governance
AnalyticsTechnology

Perfect
Model
Poor
Quality
Data
Poor
Results
Data
Warehouse
Machine
Learning
Block Chain
AI
MDM
Analytics
Technology
Data
Governance
Business
Intelligence

Perfect
Model
Quality
Data
Good
Results
Data
Warehouse
Machine
Learning
Business
Intelligence
Block Chain
AI
MDM
Analytics
Technology
Data
Governance
Quality In ➜ Quality Out!

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Success Stories

Data Footprints
• SQL Server
– 47,000,000,000,000 bytes
– Largest table 34 billion records 3.5 TBs
• Informix
– 1,800,000,000 queries/day
– 65,000,000 tables / 517,000 databases
• Teradata
– 117 billion records
– 23 TBs for one table
• DB2
– 29,838,518,078 daily queries

Repeat 100s, thousands, millions of times ...

Death by 1000 Cuts
W o r k i n g
W h i l e
B l e e d i n g
P r o f u s e l y
D E A T H
B Y A
T H O U S A N D
C U T S

bleeding
unnecessarily
from a lots of
cuts

Working While Bleeding

Making a Better
Data Sandwich

Standard data
Data supply
Data literacy
Making a Better Data Sandwich
Data literacy
Standard data
Data supply

Standard data
Data supply
Data literacy

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

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

Engineering
Architecture
Engineering/Architecting
Relationship
• Architecting is used to create
and build systems too complex
to be treated by engineering
analysis alone
– Require technical details as the
exception
• Engineers develop the
technical designs
– Engineering/Crafts-persons deliver
components supervised by:
• Manufacturers
• Building Contractors
© Copyright 2020 by Peter Aiken Slide #https://plusanythingawesome.com 36

USS Midway
& Pancakes
What is this?
• It is tall
• It has a clutch
• It was built in 1942
• It is cemented to the floor
• It is still in regular use!

Data should not act as chaff

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Program
Success Stories

DQ challenges are context specific!

Hidden Data Factories
• Make explicit the extra steps required to correct costly and time-
consuming data errors
https://hbr.org/2016/09/bad-data-costs-the-u-s-3-trillion-per-year
Department A
Department B
1. Check A's work
2. Make any corrections
3. Complete B's work
4. Deliver to customers
5. Deal with consequences
Work products
are delivered to
CustomersCustomers
Knowledge Workers
80% looking for stuff
20% doing useful work

Hidden Data Factories

Hidden Data Factories are expensive https://hbr.org/2016/09/bad-data-costs-the-u-s-3-trillion-per-year
• Consider these two questions:
– Were your systems explicitly designed to
be integrated or otherwise work together?
– If not then what is the likelihood that they
will just happen to work well together?
• Data must function at the most granular
interaction or it results in things that:
– Take longer (end-of-day job runs 45 hours)
– Cost more (the wrong assets are transferred)
– Deliver less (features are not delivered)
– Present greater risk (billing delayed 30 days, monthly)
• 20-40% of IT budgets are spent evolving data:
– Data migration (changing the location from one place to another)
– Data conversion (changing it into another form, state, or product)
– Data improvement (inspecting, manipulating it, preparing for subsequent use)
"The choice of data structure and algorithm
can make the difference between software
running in a few seconds or many days."
http://slideplayer.com/slide/7664141/

Great inspiration ...
• How to Measure Anything: Finding the Value of
Intangibles in Business by Douglas Hubbard (ISBN: 0470539399)
• If something can be observed then it can be measured
• Measurement is a reduction in uncertainty
• Formalizing stuff forces clarity
• Specific challenge characteristics
– Whatever your measurement problem is, it's been done before
– You have more data than you think
– You need less data than you think
– You probably need different data than you think
– Getting data is more economical than you think

Enrico Fermi (Nobel Prize Physics 1938)
• How many piano tuners in the city of Chicago?
– Without using existing lists such as yellow pages, google ...
– Current population of Chicago (3 million at the time)
– Average number of people per household (2 or 3)
– Share of households with regularly tuned pianos (1 in 3)
– Required frequency of tuning (1/year)
– How many pianos can a tuner tune daily? (4 or 5)
– How many days/year are worked (250)
• Tuners in Chicago ≈ Population/people per household
times % households with tuned pianos
times tunings per year
divided by (tunings per tuner per day
times workdays/year)

Technical Business
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 Story Types

Monetization: Time & Leave Tracking
At Least 300 employees are
spending 15 minutes/week
tracking leave/time

Capture Cost of Labor/Category

District-L (as an example) Leave Tracking Time Accounting
Employees 73 50
Number of documents 1000 2040
Timesheet/employee 13.7 40.8
Time spent 0.08 0.25
Hourly Cost $6.92 $6.92
Additive Rate $11.23 $11.23
Cost per timekeeper $12.31 $114.56
Total timekeeper cost $898.49 $5,727.89
Monthly cost $21,563.83 $137,469.40
Compute Labor Costs

Annual Organizational Totals
• Range $192,000 - $159,000/month
• $100,000 Salem
• $159,000 Lynchburg
• $100,000 Richmond
• $100,000 Suffolk
• $150,000 Fredericksburg
• $100,000 Staunton
• $100,000 NOVA
• $800,000/month or $9,600,000/annually
• Awareness of the cost of things considered overhead

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Success Stories

Improving Data Quality during System Migration
• Challenge
– Millions of NSN/SKUs
maintained in a catalog
– Key and other data stored in
clear text/comment fields
– Original suggestion was manual
approach to text extraction
– Left the data structuring problem unsolved
• Solution
– Proprietary, improvable text extraction process
– Converted non-tabular data into tabular data
– Saved a minimum of $5 million
– Literally person centuries of work

Unmatched
Items
Ignorable
Items
Items
Matched
Week # (% Total) (% Total) (% Total)
1 31.47% 1.34% N/A
2 21.22% 6.97% N/A
3 20.66% 7.49% N/A
4 32.48% 11.99% 55.53%
… … … …
14 9.02% 22.62% 68.36%
15 9.06% 22.62% 68.33%
16 9.53% 22.62% 67.85%
17 9.5% 22.62% 67.88%
18 7.46% 22.62% 69.92%
Determining Diminishing Returns
Before
After

Time needed to review all NSNs once over the life of the project:
NSNs 2,000,000
Average time to review & cleanse (in minutes) 5
Total Time (in minutes) 10,000,000
Time available per resource over a one year period of time:
Work weeks in a year 48
Work days in a week 5
Work hours in a day 7.5
Work minutes in a day 450
Total Work minutes/year 108,000
Person years required to cleanse each NSN once prior to migration:
Minutes needed 10,000,000
Minutes available person/year 108,000
Total Person-Years 92.6
Resource Cost to cleanse NSN's prior to migration:
Avg Salary for SME year (not including overhead) $60,000.00
Projected Years Required to Cleanse/Total DLA Person Year Saved 93
Total Cost to Cleanse/Total DLA Savings to Cleanse NSN's: $5.5 million
Quantitative Benefits

NSNs 2,000,000
NSNs 150,000
Total Time (in minutes) 750,000
Minutes needed 750,000
Total Person-Years 7
Total Cost to Cleanse/Total DLA Savings to Cleanse NSN's: $420,000

NSNs 2,000,000

Why should a knowledge worker
• with a PhD in Chemical Engineering
• have to know whether this product was
Y2K compliant?

International Chemical Company Engine Testing
• $1billion (+) chemical company
• Develops/manufactures additives
enhancing the performance of oils
and fuels ...
• ... to enhance engine/machine
performance
– Helps fuels burn cleaner
– Engines run smoother
– Machines last longer
• Tens of thousands of
tests annually
– Test costs range
up to $250,000

1.Manual transfer of digital data
2.Manual file movement/duplication
3.Manual data manipulation
4.Disparate synonym reconciliation
5.Tribal knowledge requirements
6.Non-sustainable technology
Improving Knowledge Worker Productivity

Data Integration Solution
• Integrated the existing systems to
easily search on and find similar
or identical tests
• Results:
– Reduced expenses
– Improved competitive edge
and customer service
– Time savings and improve operational capabilities
• According to our client’s internal business
case development, they expect to realize
a $25 million gain each year thanks to
this data integration

• 2 person months = 40 person days
• 683 + 1478 ≈ 2,000 attributes
• 2,000 attributes mapped onto 7,000
• 2,000/40 person days = 50/day
or 50/8 hours = 6.25 attributes/hour
and
• 7,000/40 days = 375/person day
or 375/8 hours = 46.875 attributes/
hour
• 6.25 + 46.875 ≈ 52/60
• Locate, identify, understand, map,
transform, document
• 1 attribute/minute!
Platform: UniSys
OS: OS
1998 Age: 21
Data Structure: DMS (Network)
Physical Records: 4,950,000
Logical Records: 250,000
Relationships: 62
Entities: 57
Attributes: 1478
Legacy System
#1: Payroll
Legacy System
#2: Personnel
Platform: Amdahl
OS: MVS
1998 Age: 15
Data Structure: VSAM/virtual
database tables
Physical Records: 780,000
Logical Records: 60,000
Relationships: 64
Entities: 4/350
Attributes: 683
Characteristics Logical Physical
Platform: WinTel Records: 250,000 600,000
OS: Win'95 Relationships: 1,034 1,020
1998 Age: new Entities: 1,600 2,706
Data Structure: Client/Sever RDBMS Attributes: 15,000 7,073
Predicting Engineering Problem Characteristics
New System
Extreme Data Engineering!

Logistics Company
• Fortune 450
• Room of 100 associates
• Manually correcting every
item on every customer invoice
• Upon noting this to the
responsible manager - the reply:
– This is the best quarter
– Of the best year
– I've ever had
– Perhaps I need
to double the
number in
that room?

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Success Stories

67
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Success Stories

Motivations for doing more with data
• Because data
points to where
valuable things are
located
• Because data has
intrinsic value by
itself
• Because data
has inherent
combinatorial
value
• Valuing Data
– Use data to measure
change
– Use data to manage
change
– Use data to motivate
change
• Creating a
competitive
advantage with data
Improve your
organization’s data
Improve the way your
people use its data
Improve the way your
data and your people
support your
organizational strategy

What did Rolls Royce Learn
from Nascar?
• Old model
- Sell jet engines
• New model
- Sell hours of powered thrust
- “Power-by-the-hour”
- No payment for down time
- Wing to wing
- When was this new model invented?
https://www.youtube.com/watch?v=RRy_73ivcms

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

71
Program
Success Stories

UbiquitousMysteryObject

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

z
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30

Senior Army Official
• Room full of Stewards
• A very heavy dose of management support
• Advised the group of his opinion on the matter
• Any questions as to future direction
– "They should make an appointment to speak directly with me!"
• Empower the team
– The conversation turned from "can this be done?" to
"how are we going to accomplish this?"
– Mistakes along the way would be tolerated
– Implement a workable solution in prototype form

Managing Data with Guidance?
• Federal employees
• 44 users from whitehouse.gov
• Thousands of military and
government e-mails
• Canadian citizens
• One-fifth of Quebec

Ashley
Madison
37,000,000
25,000,000
OPM
70,000,000
Target
How the Government Jeopardized Our National
Security for More than a Generation

Target Corporation's Database Contents
• Your age
• Marital status
• Part of town you live in
• How long it takes you to drive
to work
• Estimated salary
• If you have recently moved
• Credit cards carried in your
wallet
• What websites you visit
• Your ethnicity
• Your job history
• The magazines you read
• Work commute
• Sexual preferences
• If you’ve ever declared
bankruptcy or got divorced
• The year you bought (or lost)
your house
• Where you went to school(s)
• What kinds of topics you talk
about online
• Whether you prefer certain
brands of coffee, paper
towels, cereal or applesauce
• Your political leanings,
reading habits, charitable
giving and
• The number of cars you own

Key Findings
• Preventable
• Leadership failed
– To heed repeated
recommendations
– To sufficiently respond to
growing threats of sophisticated
cyber attacks, and
– To prioritize resources for
cybersecurity
• 2014 data breaches were
likely connected and
possibly coordinated to the
2015 data breach
• OPM misled the public on
the extent of the damage of
the breach and made false
statements to Congress
https://oversight.house.gov/report/opm-data-breach-government-jeopardized-national-security-generation/
How the Government Jeopardized Our
National Security for More than a
Generation

Take Aways
• Quality data requires a context specific definition
• Most business problems have data challenges (hidden data
factories) at their root
• All advanced data practices depend on quality data
• AI/ML are suffering from lack of training data
• Few 'easy' fixes exist
• Successful data quality stories demonstrate
– Tangible ongoing savings
– Innovative data uses
– Outcomes more important than money

References & Recommended Reading

Event Pricing
• 20% off
directly from the publisher on
select titles
• My Book Store @
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• Enter the code
"anythingawesome" at the
Technics bookstore checkout
where it says to
"Apply Coupon"

Necessary Prerequisites to Data Success:
Exorcising the Seven Deadly Data Sins
8 December 2020
Data Strategy:
Plans Are Useless but Planning is Invaluable
14 January 2021
Data Management Best Practices/
Practicing Data Management Better
9 February 2021
© Copyright 2020 by Peter Aiken Slide # 87
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peter@plusanythingawesome.com +1.804.382.5957
Questions?
Thank You!
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