Emerging Trends in Data Architecture – What’s the Next Big Thing?

Copyright Global Data Strategy, Ltd. 2023
Emerging Trends in Data Architecture:
What’s the Next Big Thing?
Donna Burbank
Global Data Strategy, Ltd.
January 26, 2023

Democratizing data
with a Knowledge
Graph powered
Semantic Data Layer
STARDOG
Navin Sharma
VP, Product

Challenge: Closing the last mile to Data
Democratization
CONSEQUENCES
Data still lacks
business
context
Enterprise
data lives
beyond the
Data Lake
Tools not
designed for
Citizen data
users
! ! !
THE PROBLEM: THE PROBLEM: THE PROBLEM:
While data has
been consolidated,
it is far from being
unified and
understood
Additional data
wrangling hinders
feature
engineering &
adhoc reporting
Missed business
opportunities
Wasted time and
effort
Limited data
usefulness
Specialized skills are
required to work with
data across the data
supply chain

Columns Concepts
Business Information Modeling shifts the focus
to the data consumers, from data producers

Enable Data Exploration and Discovery for
Citizen Data Users
ENCUMBERED ENABLED
Search
Query
Explore

Data access via federated query access
limits data sprawl
INCOMPLETE CONNECTED
APPS SEARCH REPORTS BI OTHER
ENTERPRISE APPLICATIONS
Which of three
compounds
combine to
produce X
results?
Q
APPS SEARCH REPORTS BI OTHER
ENTERPRISE APPLICATIONS
Which of three
compounds
combine to
produce X
results?
Q
Push-Down Queries to the sources
Single Query into the Semantic Layer

Close the last mile to true democratization
OUTCOMES
Harmonize
data with
business
meaning
Enable data
exploration
and discovery
Connect to
data beyond
the
lake(house)
✓ ✓ ✓
Compose a
reusable standards-
based vocabulary
independent of the
shape & location of
the underlying data
Link & Virtualize
access to
enterprise sources
to enable dynamic
data orchestration
Uncover new
patterns & answer
questions across
domains in a self-
serve capacity.
Improved data analyst
productivity
Faster development
of analytic projects
New revenue streams
uncovered

Growing levels of data volume and distribution are
making it hard for organizations to exploit their data
assets efficiently and effectively. Data and analytics
leaders need to adopt a semantic approach to their
enterprise data; otherwise, they will face an endless battle
with data silos.”
Gartner
“Leverage Semantics to Drive Business Value from Data”
November 2021

Gartner
In a data fabric approach, one of the most important
components is the development of a dynamic,
composable and highly emergent knowledge graph
that reflects everything that happens to your
data. This core concept in the data fabric enables the
other capabilities that allow for dynamic integration
and data use case orchestration
Gartner – How to Activate Metadata to Enable a Composable Data Fabric

Stardog
Company
Overview
Unite Data, Unleash Insight.
Stardog’s Enterprise Knowledge Graph platform connects
data based on business meaning into a flexible, reusable
semantic data layer for better insights faster.
We help customers across many industries empower data
citizens to make knowledge-informed decisions.
“Our primary objective is
to provide data at a higher
quality and relieve the
heavy lifting up front so
our data scientists can
actually work with the
data.”
— Head of IT Research, Top Global
Pharma
Select Stardog Customers

Get Started for Free on your EKG journey
https://www.stardog.com/get-started/

Databricks user? Look for Stardog under
Partner Connect

Emerging Trends in Data Architecture – What’s the Next Big Thing?

Global Data Strategy, Ltd. 2023
Donna Burbank
2
Donna is a recognised industry expert in data
management with over 25 years of
experience in data strategy, data governance,
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
data 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, US.

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 Data Mesh or Data Mess? Separating the Reality from the Hype
• April Master Data Management - Aligning Data, Process, and Governance
• May How do Data Governance & Data Architecture Support Each Other?
• June Why You Need Data Management – Getting Executive Buy-In
• July Artificial Intelligence and Machine Learning – Building the Right Architectural Foundation
• August Data Quality Best Practices (with Nigel Turner)
• September Best Practices in Metadata Management
• October Designing Data for Business Intelligence & Analytics – Where the Star Schema Fits in a
Modern Data Architecture
• December Enterprise Architecture vs. Data Architecture
3
This Year’s Lineup

What We’ll Cover Today
4
• With technological innovation and change occurring
at an ever-increasing rate…
• …it’s hard to keep track of what’s hype and what can
provide practical value for your organization.
• This webinar will share the results of a recent survey
on emerging trends in Data Architecture, along with
practical commentary & advice.

The Research
• Each year, DATAVERSITY and Global Data Strategy partner
to conduct a research survey & accompanying paper on
Trends in Data Management.
• This paper provides insight on the data management
trends over time and a look into the future.
• Available for free download on
• Dataversity.net
• Globaldatastrategy.com
5
The survey had a total of 181 participants, from
35 countries, and over 32 industries.

The Rise of the Data-Driven Business
6

Offense or Defense?
7
“Offense” “Defense”
• Focus on Opportunity
• Highlight Benefits
• Growth Mindset
• Focus on Regulation
• Highlight Risk Reduction
• Mindset of Caution

What’s Driving the Need for Data Management?
8
0.00% 10.00% 20.00% 30.00% 40.00% 50.00% 60.00% 70.00% 80.00%
Improving outcomes (e.g. health, education, etc.)
Improving product quality
Gaining competitive advantage
Increasing revenue and growth
Complying with regulations
Reducing risk
Supporting digital transformation
Improving customer satisfaction
Saving cost and increasing efficiency
Gaining insights through reporting and analytics
Top 10 Business Goals & Drivers for Data Management
(Select all that apply)

The Carrot vs. Stick Meter
9
0.00% 10.00% 20.00% 30.00% 40.00% 50.00% 60.00% 70.00% 80.00%
Improving outcomes (e.g. health, education, etc.)
Improving product quality
Gaining competitive advantage
Increasing revenue and growth
Complying with regulations
Reducing risk
Supporting digital transformation
Improving customer satisfaction
Saving cost and increasing efficiency
Gaining insights through reporting and analytics
Top 10 Business Goals & Drivers for Data Management
7
3

What is Digital
Transformation?
• Digital transformation can refer to anything from IT modernization (for
example, cloud computing), to digital optimization, to the invention of
new digital business models.
• The term is widely used in public-sector organizations to refer to modest
initiatives such as putting services online or legacy modernization. Thus,
the term is more like “digitization” than “digital business transformation.”
- Gartner Information Technology Glossary
10

The Digital Customer Experience is Driven by Data
11
While Digital Transformation was still a top
business driver,…
… this trend decreased by 7% while Customer
Satisfaction, a new entry in the Top 5
increased by 12%.
It’s not enough to just be “Digital” – the Customer Experience needs
to be improved by the data-driven Digital Experience.
12% 7%

Organizations are Looking at Data More Strategically in Future
12
0.00% 10.00% 20.00% 30.00% 40.00% 50.00% 60.00% 70.00% 80.00%
Self-Service Reporting & Analytics
Data Governance
Data Warehouse
Data Security
Business Intelligence & Reporting
Top Priorities 2022
42.00% 44.00% 46.00% 48.00% 50.00% 52.00% 54.00% 56.00% 58.00%
Data Strategy
Master Data Management
Metadata Management
Data Quality
Data Governance
Top Priorities 2023-2024
While Business Intelligence &
Reporting continue to be a top
priority…
Future plans focus on creating
trusted data…

The Importance of Metadata
13
Metadata Management as a Priority
Increased by 22%, putting it into the Top 5.
Self Service BI fell from the Top 5, with a
decrease of 2%.

Metadata is the “Who, What, Where, Why, When & How” of Data
14
Who What Where Why When How
Who created this
data?
What is the business
definition of this data
element?
Where is this data
stored?
Why are we storing
this data?
When was this data
created?
How is this data
formatted?
(character, numeric,
etc.)
Who is the Steward of
this data?
What are the business
rules for this data?
Where did this data
come from?
What is its usage &
purpose?
When was this data
last updated?
How many databases
or data sources store
this data?
Who is using this
data?
What is the security
level or privacy level
of this data?
Where is this data
used & shared?
What are the business
drivers for using this
data?
How long should it be
stored?
Who “owns” this
data?
What is the
abbreviation or
acronym for this data
element?
Where is the backup
for this data?
When does it need to
be purged/deleted?
Who is regulating or
auditing this data?
What are the technical
naming standards for
database
implementation?
Are there regional
privacy or security
policies that regulate
this data?

Use Cases for Metadata Management
15
From Trends in Data Management, 2022, DATAVERSITY, by Donna Burbank and Keith Foote

Data Management Challenges
16
0.00% 10.00% 20.00% 30.00% 40.00% 50.00% 60.00% 70.00%
Skills shortage
Data literacy
Data Quality issues (quality, accuracy, etc.)
Lack of Data Governance
Number of data silos
What Are Your Biggest Data Management Challenges?

Global Data Strategy, Ltd. 2022 www.globaldatastrategy.com
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
17

Who is Driving Data Management in the Organization?
18
0.00% 5.00% 10.00% 15.00% 20.00% 25.00% 30.00% 35.00% 40.00%
Don’t know
Project Manager
No one is driving Data Management
Chief Executive Officer (CEO)
IT Manager
Other (please specify)
Business Stakeholders
Data Architect
Data Analytics Leader
Chief Information Officer (CIO)
Chief Data Officer (CDO)
Data Governance Lead
Who Drives Data Management in the Organization?

0.00% 10.00% 20.00% 30.00% 40.00% 50.00% 60.00% 70.00% 80.00%
XML
Media files (videos, photos, etc.)
Non-relational cloud-based database (document, key/value etc.)
JSON
Legacy systems (e.g. Mainframe, COBOL, etc.)
Cloud object storage (including cloud data lakes)
Packaged applications (e.g. ERP, CRM, etc.)
Relational cloud-based database
Spreadsheets
Relational on-premises database
Top 10 Data Platforms & Sources - 2022
19
Which of the Following Data Sources or Platforms are you Using Today?

Top 10 Data Platforms & Sources – 2023-24
20
0.00% 10.00% 20.00% 30.00% 40.00% 50.00% 60.00%
Graph database
JSON
Real-time/streaming database
Non-relational cloud-based database (document, key/value…
Spreadsheets
Don’t know
Packaged applications (e.g. ERP, CRM, etc.)
Relational on-premises database
Cloud object storage (including cloud data lakes)
Relational cloud-based database
Which of the Following Data Sources or Platforms are you planning on using in the next 1-2 years?

3
Summary – Emerging Trends in Data Architecture
• Business insights and analytics continue to be a driver
as organizations seek to use data as a strategic
differentiator
• An “Offense” approach to data management is
growing, including a drive for customer satisfaction.
• A need for trusted data is driving the growth of data
governance, data quality, metadata, and more… as
organizations think more strategically about data
• Data Literacy and Skills are needed for effective
business-driven BI & Analytics
• While a wide range of tools and technologies are
available, relational databases continue to be a
foundation of organizational data architecture – tools
are evolving to a more best of breed approach.

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 Data Mesh or Data Mess? Separating the Reality from the Hype
• April Master Data Management - Aligning Data, Process, and Governance
• May How do Data Governance & Data Architecture Support Each Other?
• June Why You Need Data Management – Getting Executive Buy-In
• July Artificial Intelligence and Machine Learning – Building the Right Architectural Foundation
• August Data Quality Best Practices (with Nigel Turner)
• September Best Practices in Metadata Management
• October Designing Data for Business Intelligence & Analytics – Where the Star Schema Fits in a
Modern Data Architecture
• December Enterprise Architecture vs. Data Architecture
22
Join Us Next Month

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

Global Data Strategy, Ltd. 2021 www.globaldatastrategy.com
Questions?
Thoughts? Ideas?
24

Emerging Trends in Data Architecture – What’s the Next Big Thing?

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