DataEd Slides: Data Management Best Practices

Data Management Best Practices
Peter Aiken, PhD
Practicing Data Management Better
• DAMA International President 2009-2013 / 2018
• DAMA International Achievement Award 2001
(with Dr. E. F. "Ted" Codd
• DAMA International Community Award 2005
• 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)
• CDO Society (iscdo.org)
• 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 … PETER AIKEN WITH JUANITA BILLINGS
FOREWORD BY JOHN BOTTEGA
MONETIZING
DATA MANAGEMENT
Unlocking the Value in Your Organization’s
Most Important Asset.
2Copyright 2020 by Data Blueprint Slide #
Peter Aiken, Ph.D.

Trifacta | Data Management Best Practices
© 2020 Trifacta | Proprietary and Confidential 1
Matt Derda | Sr Manager, Customer & Product Marketing
Trifacta.com

The Evolution of Analytics & Data Management
2 Proprietary & Confidential.
Business with IT
Hybrid, Multi-Cloud
Interactions, Behaviors
Iterative, Collaborative
WHO? PEOPLE
WHAT? DATA
WHERE? PLATFORM
HOW? PROCESS
IT Led
Transactions
Top-down
On-prem

© 2020 Trifacta | Proprietary and Confidential
USE CASES
Data Onboarding
Data Science/ML
Reporting & Analytics
DATA PLATFORMS
Databases
Log Files
Spreadsheets
IoT Sensors
Apps
“It’s impossible to overstress this: 80% of the work in any data project is in cleaning the data.”
— DJ Patil, Former Chief Data Scientist of the United States
DATA PIPELINE
Discovering
Structuring
CleansingEnriching
Validating
The 80% Problem Is Well Understood

https://www.trifacta.com/blog/introducing-data-school/

Thank You
Contact Info | Trifacta.com
© 2020 Trifacta | Proprietary and Confidential 8

Four Current Data Truths
1. Data volume is still
increasing faster than
we are able to process it,
2. Data interchange
overhead and other
costs of poor data
practices are
measurably sapping
organization and individual resources–and therefore productivity,
3. Reliance on existing technology-based approaches and
education methods has not materially addressed this gap
between creation and processing or reduced bottom line costs, &
4. There exists an industry-type, whose sole purpose is to extract
data from citizens and then use it for to make money.
• Motivation
- Frustration–we are unsatisfied with current data exploitation
- Are we making progress? (No)
• How did we get here? (Building on proven research)
- DoD ➜ SEI ➜ MITRE ➜ CMMI
• Ingredients
- What is the Data Maturity Model? (DMM)
- Body of Knowledge (DM BOK)
• Understanding and applying them together
- Just a bit on strategy
- Three legged stool
- How does one get to Carnegie Hall?
• Where to next?
• Q & A?
Program

How Literate are we?
What is NAAL?
• a Nationally representative Assessment of English Literacy
among American Adults age 16 and older NAAL ➜ PIAAC (Program for the International Assessment of Adult Competencies)
PIAAC assesses three key competencies for 21st-century society and the global economy:
• Scale 1-500 – no statistically significant differences from 2012/14 to 2017
https://nces.ed.gov/surveys/piaac/current_results.asp
• Literacy: the ability to
understand, use, and
respond appropriately to
written texts.
• Numeracy: the ability to use
basic mathematical and
computational skills.
• Digital Problem Solving: the
ability to access/interpret
information in digital environments
to perform practical tasks. Referred
to as “problem-solving in
technology-rich environments (PS-
TRE)” in supporting documentation
and in previous publications.
Some measurements
• People
– 14% of people have a good understanding of
how to use business data
– 21% of those aged 16-24 classified themselves
as being data literate
– Future employees are underprepared for
data-driven workplaces
• 8% of companies have made changes in the way data is used
– 90% feel data is transforming the way they do business
• Business decision makers
– ⅓ feel that they can confidently understand, analyze and argue with data
– 32% said that they are able to create measurable value from data
– 27% said their data and analytics projects produce actionable insights
– 78% are willing to invest more time/energy into improving their data skillsets
– 24% of business decision makers, from junior managers to the C-suite,
feel fully confident in their ability to read, work with, analyze and argue
with that data — the fundamental skills that define a person's data literacy.
http://TheDataLiteracyProject.org
• Business decision makers

• In spite of increasing (big data/AI)
investments, % of firms
self-identifying as data-driven
is declining Source: Harvard Business Review, Feb 5, 2019 (Randy Bean and Thomas Davenport)
• Survey of industry leading, large corporations
• Firms must become much more serious and creative about
addressing the human side of data if they truly expect to derive
meaningful business benefits Source: 2018 Big Data & AI Executive Survey (NewVantage Partners)
Companies Are Failing In Their Efforts To Become Data Driven
30%
32%
34%
36%
38%
2017 2018 2019
31%
32.4%
37.1%
Forge a data culture
Created a data-driven organization
Treating data as a business asset
Competing on data and analytics
0.00% 25.00% 50.00% 75.00% 100.00%
Yes No
https://www.forbes.com/sites/ciocentral/2019/01/02/what-we-learned-from-top-execs-about-their-big-data-and-ai-initiatives/
2020
0
0.25
0.5
0.75
1
% 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

• Motivation
• Ingredients
• Where to next?
• Q & A?
Program
Motivation
• "We want to move our data management
program to the next level"
– Question: What level are you at now?
• You are currently managing your data,
– But, if you can't measure it,
– How can you manage it effectively?
• How do you know where to put time, money,
and energy so that data management best
supports the mission?
"One day Alice came to a fork in the road and
saw a Cheshire cat in a tree. Which road do I
take? she asked. Where do you want to go? was
his response. I don't know, Alice answered.
Then, said the cat, it doesn't matter."
Lewis Carroll from Alice in Wonderland

DoD Origins
• US DoD Reverse Engineering
Program Manager
• We sponsored research at the
CMM/SEI asking
– “How can we measure the performance
of DoD and our partners?”
– “Go check out what the Navy is up to!”
• SEI responded with an integrated
process/data improvement
approach
– DoD required SEI to remove the data
portion of the approach
– It grew into CMMI/DM BoK, etc.
Acknowledgements
version (changing data into other forms, states, or
products), or scrubbing (inspecting and manipulat-
ing, recoding, or rekeying data to prepare it for sub-
sequent use).
• Approximately two-thirds of organizational data
Increasing data management practice maturity levels can positively impact the
coordination of data ﬂow among organizations,individuals,and systems. Results
from a self-assessment provide a roadmap for improving organizational data
management practices.
Peter Aiken, Virginia Commonwealth University/Institute for Data Research
M. David Allen, Data Blueprint
Burt Parker, Independent consultant
Angela Mattia, J. Sergeant Reynolds Community College
A
s increasing amounts of data ﬂow within and
between organizations, the problems that can
result from poor data management practices
are becoming more apparent. Studies have
shown that such poor practices are widespread.
Measuring Data Management
Practice Maturity:
A Community’s
Self-Assessment MITRE Corporation: Data Management Maturity Model
• Internal research project: Oct ‘94-Sept ‘95
• Based on Software Engineering Institute Capability
Maturity Model (SEI CMMSM) for Software Development
Projects
• Key Process Areas (KPAs) parallel SEI CMMSM KPAs, but
with data management focus and key practices
• Normative model for data management required; need to:
– Understand scope of data management
– Organize data management key practices
• Reported as not-done-well by those who do it

13
CMMI Institute Background
• Evolved from Carnegie Mellon’s Software Engineering
Institute (SEI) - a federally funded research and
development center (FFRDC)
• Continues to support and provide all CMMI offerings
and services delivered over its 20+ year history at
the SEI
o Industry leading reference models - benchmarks and guidelines
for improvement – Development, Acquisition, Services, People,
Data Management
o Training and Certification program, Partner program
• Dedicated training, partner and certification teams to
support organizations and professionals
• Now owned by ISACA (CISO/M, COBIT, IT Governance,
Cybersecurity) and joint product offerings are planned
Source: Applications Executive Council, Applications Budget, Spend, and Performance Benchmarks: 2005 Member Survey Results, Washington D.C.: Corporate Executive Board 2006, p. 23.
Percentage of Projects on Budget
By Process Framework Adoption
…while the same pattern generally holds true for on-time performance
Percentage of Projects on Time
By Process Framework Adoption
Key Finding: Process Frameworks are not Created Equal
With the exception of CMM and ITIL, use of process-efficiency
frameworks does not predict higher on-budget project delivery…

Melanie Mecca
• Former CMMI Institute/Director of Data Management Products and Services
➜ datawise.inc/Sandhill
• 30+ years designing and implementing strategies and solutions for private
and public sectors
• Architecture/Design experience in:
– Data Management Programs
– Enterprise Data Architecture
– Enterprise Architecture
• DMM's Managing Author
Certified Partner, CMMI Institute
– melanie@datawise-inc.com
16
Data Management Maturity (DMM)SM Model
• DMM 1.0 released August 2014
o 3.5 years in development
o Sponsors – Microsoft, Lockheed
Martin, Booz Allen Hamilton
o 50+ contributing authors, 70+
peer reviewers, 80+ orgs
• Reference model framework of
fundamental best practices
o 414 specific practice statements
o 596 functional work products
o Maturity practices
• Measurement Instrument for
organizations to evaluate
capabilities and maturity,
identify gaps, and incorporate
guidelines for improvements.

‹#›
DMM Structure
Core Category
Process Area
Purpose
Introductory Notes
Goal(s) of the Process Area
Core Questions for the Process Area
Functional Practices (Levels 1-5)
rRelated Process Areas
Example Work Products
Infrastructure Support Practices
eExplanatory Model Components R equired for Model Compliance
17
18
“You Are What You DO”
• Model emphasizes behavior
o Proactive positive behavioral
changes
o Creating and carrying out
effective, repeatable processes
o Leveraging and extending across
the organization
• Activities result in work
products
o Processes, standards, guidelines,
templates, policies, etc.
o Reuse and extension = maximum
value, lower costs, happier staff
• Practical focus reflects real-
world organizations – enterprise
program evolving to all hands on
deck.

One concept for process
improvement, others include:
• Norton Stage Theory
• TQM
• TQdM
• TDQM
• ISO 9000
and focus on understanding
current processes and
determining where to make
improvements.
DMM Capability Maturity Model Levels
Our DM practices are informal and ad hoc,
dependent upon "heroes" and heroic efforts
Performed
(1)
Managed
(2)
Our DM practices are defined and
documented processes performed at
the business unit level
Our DM efforts remain aligned with
business strategy using
standardized and consistently
implemented practices
Defined
(3)
Measured
(4)
We manage our data as a asset using
advantageous data governance practices/structures
Optimized
(5)
DM is strategic organizational capability,
most importantly we have a process for
improving our DM capabilities
20
DMM Training and Certification
Partner Delivered Services
• Building EDM Capabilities
– Instructor-Led 3-day interactive class
– Comprehensive understanding of fundamental EDM
processes and practices
– Leads to CMMI Institute Enterprise Data
Management Associate (EDMA) certification
• Enterprise Data Management Expert (EDME)
– Instructor-led 5 day interactive class
– Employing the DMM to lead & implement EDM
programs
– Method and templates to lead a DMM Assessment
– Required for CMMI Institute’s Enterprise Data
Management Expert (EDME) certification
CMMI Institute Delivered Services
• eLearning – web-based Building EDM Capabilities
• 8-10 hour online class, bundled with DMM/exam fee
• Leads to EDMA certification.

• Motivation
• Ingredients
• Where to next?
• Q & A?
Program
Data$Management$
Strategy
Data Management Goals
Corporate Culture
Data Management Funding
Data Requirements Lifecycle
Data
Governance
Governance Management
Business Glossary
Metadata Management
Data
Quality
Data Quality Framework
Data Quality Assurance
Data
Operations
Standards and Procedures
Data Sourcing
Platform$&$
Architecture
Architectural Framework
Platforms & Integration
Supporting$
Processes
Measurement & Analysis
Process Management
Process Quality Assurance
Risk Management
Configuration Management
Component Process$Areas
Data
Governance
Data
Management
Strategy
Data
Operations
Platform
Architecture
Supporting
Processes
Maintain fit-for-purpose data,
efficiently and effectively
Manage data coherently
Manage data assets professionally
Data life cycle
management
Organizational support
Data
QualityData$Management$
Strategy
Corporate Culture
Data
Governance
Business Glossary
Metadata Management
Data
Quality
Data
Operations
Data Sourcing
Platform$&$
Architecture
Supporting$
Processes
Process Management
Risk Management
Data$Management$
Strategy
Corporate Culture
Data
Governance
Business Glossary
Metadata Management
Data
Quality
Data
Operations
Data Sourcing
Platform$&$
Architecture
Supporting$
Processes
Process Management
Risk Management
Data$Management$
Strategy
Corporate Culture
Data
Governance
Business Glossary
Metadata Management
Data
Quality
Data
Operations
Data Sourcing
Platform$&$
Architecture
Supporting$
Processes
Process Management
Risk Management
Data$Management$
Strategy
Corporate Culture
Data
Governance
Business Glossary
Metadata Management
Data
Quality
Data
Operations
Data Sourcing
Platform$&$
Architecture
Supporting$
Processes
Process Management
Risk Management
Data$Management$
Strategy
Corporate Culture
Data
Governance
Business Glossary
Metadata Management
Data
Quality
Data
Operations
Data Sourcing
Platform$&$
Architecture
Supporting$
Processes
Process Management
Risk Management
Data architecture
implementation
DMM℠ Structure of
5 Integrated
DM Practice Areas

Data architecture
implementation
Data
Governance
Data
Management
Strategy
Data
Operations
Platform
Architecture
Supporting
Processes
Maintain fit-for-purpose data,
efficiently and effectively
Manage data coherently
Manage data assets professionally
Data life cycle
management
Organizational support
Data
Quality
Data
Governance
Data
Quality
Platform
Architecture
Data
Operations
Data
Management
Strategy
3 3
33
1
Supporting
Processes
Optimized
Measured
Defined
Managed
Initial
Optimized
Measured
Defined
Managed
Initial
Optimized
Measured
Defined
Managed
Initial
Optimized
Measured
Defined
Managed
Initial
Your data foundation
can only be as strong
as its weakest link!
Optimized
Measured
Defined
Managed
Initial
• Before further construction could proceed
• No IT equivalent
Our barn had to pass a foundation inspection

Data Management Practice Areas
Data Management
Strategy
DM is practiced as a
coherent and
coordinated set of
activities
Data Quality
Delivery of data is
support of
organizational
objectives – the
currency of DM
Data
Governance
Designating specific
individuals caretakers
for certain data
Data Platform/
Architecture
Efficient delivery of
data via appropriate
channels
Data Operations
Ensuring reliable
access to data
Capability
Maturity Model
Levels
Examples of practice
maturity
1 – Performed
Our DM practices are ad hoc and
dependent upon "heroes" and
heroic efforts
2 – Managed
We have DM experience and have
the ability to implement disciplined
processes
3 – Defined
We have standardized DM
practices so that all in the
organization can perform it with
uniform quality
4 – Measured
We manage our DM processes so
that the whole organization can
follow our standard DM guidance
5 – Optimized
We have a process for improving
our DM capabilities
Assessment Components
‹#›
DMM Assessment Summary
Sample Organization
26

27
Cumulative Benchmark – Multiple organizations
Industry Focused Results
• CMU's Software
Engineering Institute (SEI) Collaboration
• Results from hundreds organizations in
various industries including:
✓ Public Companies
✓ State Government Agencies
✓ Federal Government
✓ International Organizations
• Defined industry standard
• Steps toward defining data management
"state of the practice"
Data Management Strategy
Data Governance
Platform & Architecture
Data Quality
Data Operations
Focus:
Implementation
and Access
Focus:
Guidance and
Facilitation
Optimized(V)
Measured(IV)
Defined(III)
Managed(II)
Initial(I)

Development guidance
Data Adminstration
Support systems
Asset recovery capability
Development training
0 1 2 3 4 5
Client Industry Competition All Respondents
Data Management Practices Assessment
Challenge
Challenge
Challenge
Data Program
Coordination
Organizational Data
Integration
Data Stewardship
Data Development
Data Support
Operations
High Marks for IFC's Audit
Leadership & Guidance
Asset Creation
Metadata Management
Quality Assurance
Change Management
Data Quality
0 1 2 3 4 5
TRE ISG IFC Industry Benchmarks Overall Benchmarks

1
2
3
4
5
DataProgramCoordination
OrganizationalDataIntegration
DataStewardship
DataDevelopment
DataSupportOperations
2007 Maturity Levels 2012 Maturity Levels
Comparison of DM Maturity 2007-2019
"While all improvement efforts begin
with the obligatory 'assessment' phase,
Carnegie Mellon’s CMMI and DMM
are the only proven frameworks that
have the added benefit of literally
decades of practice and benchmarking
data (Board, 2006). Organizations
not using the DMM risk an inability to
meaningfully compare results against
other organizations and, as a result,
adopt unproven methods."

Theory of Constraints - Generic
Identify the current constraints,
the components of the system
limiting goal realization
Make quick
improvements
to the constraint
using existing
resources
Review other activities in the process facilitate proper alignment and support of constraint
If the constraint
persists, identify other
actions to eliminate
the constraint
Repeat until the
constraint is
eliminated
Alleviate
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 data engineering and architecture!
Quality data engineering/
architecture work products
do not happen accidentally!

The DAMA Data
Management
Body of
Knowledge
Data
Management
Practice Areas
fromTheDAMAGuidetotheDataManagementBodyofKnowledge©2009byDAMAInternational
The DAMA Guide to the Data Management Body of Knowledge
Published by DAMA
International
• The professional
association for Data
Managers (40
chapters worldwide)
DMBoK organized
around
• Primary data
management
functions focused
around data
delivery to the
organization
• Organized around
several
environmental
elements
Data Management Practice Areas

Why isn't aren't my
data problems
solved by a data
warehouse?
Transform
40
Problems with forklifting
1. no basis for decisions
made
2. no inclusion of
architecture/
engineering concepts
3. no idea that these
concepts are
missing from
the process
4. 80% of
organizational
data is ROT
Less
Cleaner
More shareable
... data
Copyright 2020 by Data Blueprint
Making Warehousing Successful

Version 1
Data
Strategy
Data
Governance
BI/
Warehouse
Perfecting
operations in 3
data management
practice areas
1X
1X
1X
Metadata
Data
Quality
Version 2
Data
Strategy
Data
Governance
BI/
Warehouse
Perfecting
operations in 3
data management
practice areas
2X
2X
1X
Metadata

Version 3
Data
Strategy
Data
Governance
BI/
Warehouse
Reference &
Master Data
Perfecting
operations in 3
data management
practice areas
3X
3X
1X
(Things that further)
Organizational Strategy
(OpportunitiestoPractice)
NeededDataSkills
(Opportunitiestoimprove)
Datausebythebusiness
Lighthouse Project Provides Focus

• Motivation
• Ingredients
• Where to next?
• Q & A?
Program
Strategy Example 1
Good Guys
(Us)
Bad Guys
(Them)

Strategy Example 2
Good Guys
(Us)
Bad Guys
(Them)
General Dwight D. Eisenhower
• “In preparing for battle I have always found that
plans are useless, but planning is indispensable..”
– https://quoteinvestigator.com/2017/11/18/planning/

Strategy Guides Workgroup Activities
A pattern
in a stream
of decisions
Success Requires a 3-Legged Stool
People
Process
Technology

Change Management & Leadership
Copyright 2020 by Data Blueprint Slide # 51
Diagnosing Organizational Readiness
adapted from the Managing Complex Change model by Dr. Mary Lippitt, 1987
Culture is the biggest impediment to a
shift in organizational thinking about data!

No cost, no registration case study download
• Download
– http://dl.acm.org/citation.cfm?doid=2888577.2893482
or
http://tinyurl.com/PeterStudy
• Download
8
EXPERIENCE: Succeeding at Data Management—BigCo Attempts to
Leverage Data
PETER AIKEN, Virginia Commonwealth University/Data Blueprint
In a manner similar to most organizations, BigCompany (BigCo) was determined to benefit strategically from
its widely recognized and vast quantities of data. (U.S. government agencies make regular visits to BigCo to
learn from its experiences in this area.) When faced with an explosion in data volume, increases in complexity,
and a need to respond to changing conditions, BigCo struggled to respond using a traditional, information
technology (IT) project-based approach to address these challenges. As BigCo was not data knowledgeable,
it did not realize that traditional approaches could not work. Two full years into the initiative, BigCo was
far from achieving its initial goals. How much more time, money, and effort would be required before results
were achieved? Moreover, could the results be achieved in time to support a larger, critical, technology-driven
challenge that also depended on solving the data challenges? While these questions remain unaddressed,
these considerations increase our collective understanding of data assets as separate from IT projects.
Only by reconceiving data as a strategic asset can organizations begin to address these new challenges.
Transformation to a data-driven culture requires far more than technology, which remains just one of three
required “stool legs” (people and process being the other two). Seven prerequisites to effectively leveraging
data are necessary, but insufficient awareness exists in most organizations—hence, the widespread misfires
in these areas, especially when attempting to implement the so-called big data initiatives. Refocusing on
foundational data management practices is required for all organizations, regardless of their organizational
or data strategies.
Categories and Subject Descriptors: H.2.0 [Information Systems]: Database Management—General; E.0
[Data]: General
General Terms: Management, Performance, Design
Additional Key Words and Phrases: Data management, data governance, data stewardship, organizational
design, CDO, CIO, chief data officer, chief information officer, data, data architecture, enterprise data exec-
utive, IT management, strategy, policy, enterprise architecture, information systems, conceptual modeling,
data integration, data warehousing, analytics, and business intelligence, BigCo
ACM Reference Format:
Peter Aiken. 2016. Experience: Succeeding at data management—BigCo attempts to leverage data. J. Data
and Information Quality 7, 1–2, Article 8 (May 2016), 35 pages.
DOI: http://dx.doi.org/10.1145/2893482
1. CASE INTRODUCTION
Good technology in the hands of an inexperienced user rarely produces positive
results.
Everyone wants to “leverage” data. Today, this is most often interpreted as invest-
ments in warehousing, analytics, business intelligence (BI), and so on. After all, that
is what you do with an asset—you leverage it—so the asset can help you to attain
strategic objectives; see Redman [2008] and Ladley [2010]. Widespread and pervasive
Author’s address: P. Aiken, 10124C West Broad Street, Glen Allen, VA 23060; email: peter.aiken@vcu.edu.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted
without fee provided that copies are not made or distributed for profit or commercial advantage and that
copies bear this notice and the full citation on the first page. Copyrights for components of this work owned
by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or
republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request
permissions from Permissions@acm.org.
2016 Copyright is held by the owner/author(s). Publication rights licensed to ACM.
ACM 1936-1955/2016/05-ART8 $15.00
DOI: http://dx.doi.org/10.1145/2893482
ACM Journal of Data and Information Quality, Vol. 7, No. 1–2, Article 8, Publication date: May 2016.
A Musical Analogy
+ =
https://www.youtube.com/watch?v=4n1GT-VjjVs&frags=pl%2Cwn
Please raise your hand when you recognize this song

• Motivation
• Ingredients
• Where to next?
• Q & A?
Program
1. Data volume is still
increasing faster than
we are able to process it,
2. Data interchange
overhead and other
costs of poor data
practices are
measurably sapping
organization and individual resources–and therefore
productivity,
3. Reliance on existing technology-based approaches
and education methods has not materially
addressed this gap between creation and
processing or reduced bottom line costs, &
4. There exists an industry-type, whose sole purpose is
to extract data from citizens and then use it for to
make money.
Big changes
1. Process is more
important than results
at first
2. Failure is itself a lesson
3. People and process
aspects are not
receiving enough
attention

• Motivation
• Ingredients
• Where to next?
• Q & A?
Program
Upcoming Events
June Webinar
Approaching Data Governance Strategically
9 June 2020 @ 2:00 PM ET
July Webinar
Data Management + Data Strategy = Interoperability
14 July 2020 @ 2:00 PM ET
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Prerequisite to Digital Transformation
22-23 October 2020 @ 10:30 AM ET
Sign up for webinars at:
www.datablueprint.com/webinar-schedule
Brought to you by:

+ =
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Copyright 2020 by Data Blueprint Slide #
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DataEd Slides: Data Management Best Practices

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