Improving Data Literacy Around Data Architecture

Copyright Global Data Strategy, Ltd. 2022
Improving Data Literacy Around Data Architecture
Donna Burbank
Global Data Strategy, Ltd.
May 26th, 2022

Global Data Strategy, Ltd. 2022 www.globaldatastrategy.com
Donna Burbank
2
Donna is a recognised industry expert in
information management with over 20 years
of experience in data strategy, information
management, data modeling, metadata
management, and enterprise architecture.
Her background is multi-faceted across
consulting, product development, product
management, brand strategy, marketing, and
business leadership.
She is currently the Managing Director at
Global Data Strategy, Ltd., an international
information management consulting company
that specializes in the alignment of business
drivers with data-centric technology. In past
roles, she has served in key brand strategy
and product management roles at CA
Technologies and Embarcadero Technologies
for several of the leading data management
products in the market.
As an active contributor to the data
management community, she is a long time
DAMA International member, Past President
and Advisor to the DAMA Rocky Mountain
chapter, and was awarded the Excellence in
Data Management Award from DAMA
International.
She has worked with dozens of Fortune 500
companies worldwide in the Americas,
Europe, Asia, and Africa and speaks regularly
at industry conferences. She has co-authored
several books and is a regular contributor to
industry publications. She can be reached at
donna.burbank@globaldatastrategy.com
Donna is based in Boulder, Colorado, USA.
Follow on Twitter @donnaburbank
@GlobalDataStrat
Twitter Event hashtag: #DAStrategies

DATAVERSITY Data Architecture Strategies
• January Emerging Trends in Data Architecture – What’s the Next Big Thing?
• February Building a Data Strategy - Practical Steps for Aligning with Business Goals
• March Master Data Management – Aligning Data, Process, and Governance
• April Data Governance & Data Architecture: Alignment & Synergies
• May Improving Data Literacy Around Data Architecture
• June Business Intelligence & Data Analytics: An Architected Approach
• July Best Practices in Metadata Management
• August Data Quality Best Practices
• September Business-centric Data Modeling
• October Graph Databases: Benefits & Risks
• December Enterprise Architecture vs. Data Architecture
3
This Year’s Lineup

What We’ll Cover Today
• Data Literacy is an increasing concern, as
organizations look to become more data-driven.
• As the rise of the citizen data scientist and self-service
data analytics becomes increasingly common, the
need for business users to understand core Data
Management fundamentals is more important than
ever.
• At the same time, technical roles need a strong
foundation in Data Architecture principles and best
practices.
• This webinar covers key components of Data Literacy,
and practical ways to implement a Data Literacy
program in your organization.
4

The Need for Data Literacy
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* based on research from a 2021 DATAVERSITY survey on “Trends in Data Management” by Donna Burbank and Michelle Knight
• 40% of organizations cite collaborating between
business and IT as a major impediment to data
management success.
• >50% identified improved collaboration through
using a defined data architecture
• 47% of organizations cite Skills Shortage as one
of their biggest challenges
• 32% of organizations of organizations are looking
to implement new self-service BI efforts in 2022-
2023 (36% have in place already).

What is Data Literacy?
Data literacy is the ability to read, write and communicate data in
context, including an understanding of data sources and constructs,
analytical methods and techniques applied, and the ability to
describe the use case, application and resulting value.
- Gartner
6

Data Literacy supports the notion that everyone across the
organization (both business and IT)
Routinely asks the question:
7
What about the Data?

While the focus of Data Literacy is often on analytics and data-driven decision-making,
Data Literacy is also critical to:
Operational Data – using data in the organization’s “day job” (application data, data entry, etc.
Master Data – ensuring a single, accurate, secured view of Customer, Patient, Student,
Product, etc.
Application Development – including data as a critical portion of the software development
lifecycle (SDLC)
And more… any activity or decision that affects data should invoke the question….
“What about the data?”
8

Data Literacy for Reporting & Analytics
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ACME Inc. Sales Dashboard
❑ Product: Widget 1
❑ Region: NA
201
8
2019 2020 2021 2022
Data Literacy for reporting & analytics includes:
• Data-driven culture
• Do we use dashboards in our sales meetings?
• Or go by “gut feel”?
• How can we integrate analytics into our sales cycle
(e.g. predictive next best offer)
• Data Governance
• How do we define “Total Revenue”?
• What countries are included in South America?
• Data Quality
• Are these revenue numbers accurate?
• What’s the source of the product data?
• Data Architecture
• How are we storing the data to accurately &
efficiently to slice and dice for these reports?
Super Widget
Pack
Widget 1
Widget 2
What about
the data?

Data Literacy for Operational / Application Data
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ACME Customer Order Form Data Literacy for operational/application data includes:
• Data-driven culture
• How do we best use data to support the customer
journey? (or patient, student, citizen, etc.)
• Automated drop-downs & pre-fill
• Analytics-driven Recommendations
• Data Governance
• Who owns the customer onboarding process?
• How/where do we source the Product Master Data?
• How can/should we use customer data?
• Data Quality
• What’s the source of the product data?
• Can we use master & reference data for easy drop-
downs?
• Data Architecture
• What is the data model behind this application? e.g.
Can a customer have more than one address? e.g.
Shipping / Billing?
First Name Family Name
Address
City State/Region Postal Code
Widget 1
Product
Widget 1
Widget 2
Widgget 2
Widget 2
Widget 3.
Price
Customer who bought Widget 1 also bought Thingy X.
❑ Do you want to be included in our mailing list?
What about
the data?

Data Literacy is for both Business & Tech
Data Literacy Skills for Business
• How to define reporting measures
• Business rules (e.g. data quality) –
dimensions of data quality
• Privacy, Governance, Accountability
• Data Sharing Agreements
• Basic use cases for:
• Analytics, AI/ML
• BI / Analytics
• Basic BI skills for self-service
• … and more
11
and Data Architecture can help bridge the gap between Business and IT
Data Literacy Skills for Tech / IT
• Relational Algebra
• Physical data models
• Basic statistics and data science
• Databases…. i.e. not embedding
data objects in code
• APIs and Data Sharing
• Basic data privacy regulation
(e.g. PCI, PII, HIPAA, etc.)
• … and more
Data Architecture Tools to Bridge the Gap
• Business Glossary
• Business-centric data models
(conceptual, logical)
• Bus matrices for BI
• CRUD matrices for data integration and
master data
• Data-centric user stories, customer journeys,
etc.
• Data Quality Dashboards
• … and more

Data Literacy Roles - Business
• Many Data Literacy Programs involve education, training, and certifications
• Badges and Gamification
• Certification Levels for various use cases
• Data Citizen, Data Expert, Data Creator
• Data User, Self-Service BI User, Data Developer, etc.
• Data Governance Roles are also a form of Data Literacy
• Data Owner
• Data Steward
• Data Custodian
• Business Data Sponsor
12

Data Literacy Roles – Tech / IT
• Data-centric roles should have a strong foundation in the basic of data management
13
• Application Development roles should also have data foundation basics:
• Application developer
• Scrum master
• Other related roles have impact on data, e.,g.
• Privacy and Security
• Infrastructure
• Industry Certifications are helpful foundations, e.g.
• DAMA CDMP
• TDWI CBIP
• Data architect
• Data modeler
• Data engineer
• BI / Report developer

14
Some Data Architecture & Data Management
“Tools of the Trade”
that Support Data Literacy

Bus Matrix
• A bus matrix is a data
architecture planning tool
used to design appropriate
reporting metrics based on
business processes and
then indicate which data
dimension(s) should be
conformed against those
metrics
• To “conform” a data
dimension is a way to
standardize its meaning so
it can be re-used in
multiple data models and
that it can be related to
different metric categories
consistently
Reporting view that reconciles key metrics (facts) across shared data domains (dimensions)
15
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Customer Inquiry X X X X X X X
Web Navigation X X X X X X X
Booking X X X X X X X
Stay X X X X X X
Consumption X X X X X X X X
Location Tracking X X X X X X
Feedback X X X X X X X X X X
Capacity X X X X X
Marketing activity X X X X X
MEASURES
COMMON DATA DIMENSIONS

A Data Model Is a Visual Representation of Core Concepts
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From Data Modeling for the Business by Hoberman, Burbank, Bradley, Technics Publications, 2009
A data model is a graphical view of the core concepts important to the organization.
It’s a great way to inventory and prioritize data areas.
Humans tend to think in Pictures.

Levels of Data Models
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Conceptual
Logical
Physical
Purpose
Communication & Definition of
Business Concepts & Rules
Clarification & Detail
of Business Rules &
Data Structures
Technical
Implementation on
a Physical Database
Audience
Business Stakeholders
Data Architects
Data Architects
Business Analysts
DBAs
Developers
Business Concepts
Data Entities
Physical Tables
Business Stakeholders
Data Architects
Enterprise
Subject Areas
Organization & Scoping of main
business domain areas

Businesspeople can (and should) understand basic data models
For example, instead of talking about “Cardinality”,
Think of how a child might answer the question “How many?”
One = 1 finger
More than one = several fingers
A Department may contain zero, 1 or more Employees
Questions to ask when creating
relationships:
• Must I have one?
(i.e. Is it required?)
• Is it possible to have more than one?
It’s all in how you explain it… a 5-10 minute “data modeling 101” should be enough

Avoid “Death by Data Modeling”
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• “We’re just going to sit in this room for a few days
until we scope out the entire enterprise data model
plastered across these three walls.
• Just about 1000 entities or so…
• First off, what is the data type for account code? …”

Avoid Technical / Database Terms
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• Use business-centric language (e.g. Customer)
• Avoid overly technical terms (e.g. DB07_cust, dimCUST)
• Or Overly-generic terms (e.g. Party, Entity)…unless the business uses them.
Customer DB07_cust dim_cust Party

Global Data Strategy, Ltd. 2022 www.globaldatastrategy.com 21
Customer
Prospect
Salesperson
Is Father Of
Is Evaluating
Sells
Household
Product
Buys
Buys
Customer
Support Rep
Supports
Sells To
Is Trained On
Client
A Data Model Describes a Business
• When working with a business audience, the importance of a data model is in communication.
• Use creative ways to show models to a business audience.
• The data model should tell the “story” of the business.
Be Creative in your Data Model Presentation

Process Models & CRUD Matrices
• Linking processes to the data that is Created, Updated, or Deleted (CRUD) is
important to understanding data usage and is critical to operational data literacy
Customer Order Account Invoice Product
Receive Customer Order R C C, R
Process Customer Order C,R,U R,U R
Fill Order R,U R,U R,U
Send Invoice R,U R,U C
CRUD Matrix
Business Process Model

Completeness
Data is present
when required - no
blank or ‘NULL’
(empty) values
Consistency
Values in one data
set are consistent
with values in
another data set
Accuracy
Data correctly
describes the "real
world" object or
event being
described
Reasonableness
Data value is
appropriate to the
object it is
describing
Uniqueness
Duplicates do not
occur, and data
instances are only
represented once
in the data set
Conformity
Data is valid with
respect to the
syntax (format,
type, range) of its
definition
Timeliness
Data is up to date,
and refreshed or
deleted as
required
Data Quality – It’s more than “Is it right?”
Understanding the Dimensions of Data Quality
Is it there?
Is it the same value
across systems?
Is the value w/in
thresholds of tolerance
or level of granularity?
Is it conforming to
standard values or
format?
Is the data there when
I need to use it?
Is it a duplicate
record?
Is the value aligned
with operational
context?
QUESTIONS

Tools &
Platforms
Data Governance is a Key Foundation to Data Literacy
Vision & Strategy
Organization &
People
Processes &
Workflows
Data Management
& Measures
Culture & Communication
Copyright Global Data Strategy, Ltd. 2021

Summary
• Data Literacy is an increasing concern, as
organizations look to become more data-driven.
• Data literacy is the responsibility of both business and
technical stakeholders in an organization.
• Data literacy goes beyond analytics and includes
operational data and data governance, among other
data-centric initiatives.
• Many roles across both business and IT need data
literacy training.
• Data architecture provides helpful tools to enable
communication between the business and IT.
25

DATAVERSITY Data Architecture Strategies
• January Emerging Trends in Data Architecture – What’s the Next Big Thing?
• February Building a Data Strategy - Practical Steps for Aligning with Business Goals
• March Master Data Management – Aligning Data, Process, and Governance
• April Data Governance & Data Architecture: Alignment & Synergies
• May Improving Data Literacy Around Data Architecture
• June Business Intelligence & Data Analytics: An Architected Approach
• July Best Practices in Metadata Management
• August Data Quality Best Practices
• September Business-centric Data Modeling
• October Graph Databases: Benefits & Risks
• December Enterprise Architecture vs. Data Architecture
26
This Year’s Lineup

Global Data Strategy, Ltd. 2022
Who We Are: Business-Focused Data Strategy
Maximize the Organizational Value of Your Data Investment
In today’s business environment, showing rapid time to value for
any technical investment is critical.
But technology and data can be complex. At Global Data Strategy,
we help demystify technical complexity to help you:
• Demonstrate the ROI and business value of data to your
management
• Build a data strategy at your pace to match your unique culture
and organizational style.
• Create an actionable roadmap for “quick wins”, which building
towards a long-term scalable architecture.
Global Data Strategy’s shares experience from some of the largest
international organizations scaled to the pace of your unique team.
www.globaldatastrategy.com
Global Data Strategy has worked with organizations globally in the
following industries:
Finance · Retail · Social Services · Health Care · Education · Manufacturing
· Government · Public Utilities · Construction · Media & Entertainment ·
Insurance …. and more

Questions?
Thoughts? Ideas?
28

Improving Data Literacy Around Data Architecture

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