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

Copyright Global Data Strategy, Ltd. 2020
Emerging Trends in Data Architecture –
What’s the Next Big Thing?
Donna Burbank
Global Data Strategy, Ltd.
January 23rd, 2020
Follow on Twitter @donnaburbank
Twitter Event hashtag: #DAStrategies

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Director, Product Marketing
@DataStax

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Modern Data Diversity and Complexity
LEGACY DATA
INTEGRATION
REAL-TIME,
STREAMING, EVENTS
DISPARATE,
SILO’D DATA
DATA SECURITY /
SOVEREIGNTY
UNPREDICTABLE
SCALE
HYBRID, MULTI,
INTER-CLOUD

Modern Data Brings Workload Complexity
Today’s Related
Data and Complex
Workloads
Traditional Siloed
Data and Workload
Management
>

Data Management Evolution
{ : }
2020’s2000’s1980’s

Application Challenges with Advanced Data Workloads
Data Ingest
• Fast bulk and individual queries, and graph entity ingest/mutability
• Need atomicity guarantees
Data Model Flexibility
• Schema for easy and obvious data management and optimal
performance
API Flexibility
• Ability to query data or traverse a graph quickly
• e.g. traverse a graph from any object, or access an individual graph
object
Intelligent Indexing
• Support global traversing with forgiving search
• Leverage indexing for optimized performance
Connected Data
• Related disparate data transformation
• Analytics/algorithm, and graph execution
Intelligent Scaling
• Scale easily to meet workload demands
• e.g. Bind queries, traversals to local datasets, collocate neighborhoods
Security • Authorization for data objects and individual data entities

So, how do we solve for mixed workloads?
7
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• Machine Time < the time it takes to interrupt a user process
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Mixed Workload Coverage – Customer 360 Queries
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Machine
fast
CQL Search
Analytics
Responsetime
Simple Complex
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6. Show Dave what items are
trending for anyone with the
same profile while he looks for a
gift to purchase for Jenn in his
mobile app
1
DSE
Graph
4
5
3
2
Stream Processing
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9
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Global Data Strategy, Ltd. 2020
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.
Donna is also an analyst at the Boulder BI
Train Trust (BBBT) where she provides advice
and gains insight on the latest BI and
Analytics software in the market. She was on
several review committees for the Object
Management Group’s for key information
management and process modeling
notations.
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 two books: Data Modeling for the
Business and Data Modeling Made Simple
with ERwin Data Modeler 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
Twitter Event hashtag: #DAStrategies

DATAVERSITY Data Architecture Strategies
• January 23 Emerging Trends in Data Architecture – What’s the Next Big Thing?
• February 27 Building a Data Strategy - Practical Steps for Aligning with Business Goals
• March 26 Cloud-Based Data Warehousing – What's New and What Stays the Same
• April 23 Master Data Management – Aligning Data, Process, and Governance
• May 28 Data Governance and Data Architecture – Alignment and Synergies
• June 25 Enterprise Architecture vs. Data Architecture
• July 22 Best Practices in Metadata Management
• August 27 Data Quality Best Practices
• September 24 Data Virtualization – Separating Myth from Reality
• October 22 Data Architect vs. Data Engineer vs. Data Modeler
• December 1 Graph Databases: Practical Use Cases
3
This Year’s Lineup

What We’ll Cover Today
• 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 discuss the results of a recent
DATAVERSITY survey on emerging trends in data
architecture, along with practical commentary and advice.
4
Content is based on research from a 2019 DATAVERSITY survey
on “Trends in Data Management”.

What is Data Management?
The DAMA Data Management Body of Knowledge (DMBOK), defines data architecture as the following:
“Data Management is the development, execution, and supervision of plans, policies, programs, and
practices that deliver, control, protect, and enhance the value of data and information assets throughout
their lifecycles.”
5
DMBOK Definition

What is Data Management?
Survey respondents also provided a range of relevant definitions including:
“Data Management describes people, process, and technology to optimize, protect, and
leverage data as an asset.”
“Data Management is an organization capability supported by tools, processes, standards,
and people.”
“Data Management makes enterprise data effective and efficient by supporting business
activities.”
6
Survey Respondents Provided a Range of Views

A Successful Data Strategy links Business Goals with Technology Solutions
Level 1
“Top-Down” alignment with
business priorities
Level 5
“Bottom-Up” management &
inventory of data sources
Level 2
Managing the people, process,
policies & culture around data
Level 4
Coordinating & integrating
disparate data sources
Level 3
Leveraging data for strategic
advantage
Copyright 2020 Global Data Strategy, Ltd
Data Management Supports a Wider Data Strategy
www.globaldatastrategy.com

Data-Driven Business
Data-Driven Business is an impetus
for data management
• 70% of respondents feel that their organization
sees data as a strategic asset.
• 68% are looking to save costs and increase
efficiency
• 53% see digital transformation as a key driver
for data management
8
Data Management is the foundation of the Data-Driven Business

Business Optimization vs. Business Transformation
9
Digital Transformation is transforming business
Business Optimization
Becoming a Data-Driven Company
• Improving Efficiency
• Reduce Redundancy
• Eliminate Manual Effort
• Growing Revenue
• Improved Marketing Campaigns
• Data-driven Product Development
• Etc.
Business Transformation
Becoming a Data Company
• New Business Models
• Data is the product
• Monetization of information
• Digital Transformation
• Improved Marketing Campaigns
• Data-driven Product Development
• Etc.
How do we do what we do
better?
How do we do something
different?

Data is Driving the Future of the Global Economy
• “For most of the history of business,
the world’s leading companies have
been industrially-focused…
• …But today’s business reality is very
different. We live in a world of bytes –
and for the first time technology and
commerce have collided in a way that
makes data far more valuable than
physical, tangible objects.
• The best place to see this is in how the
market values businesses.”1
10
Product
Focus
Data
Focus
The World Economic Forum sees today’s economy as driven by Data, not Goods & Services
1 Oct 15, 2018, World Economic Forum, “These are the 8 major forces shaping the future of the global economy”

Democratization of Data Management
An analysis of Global Data Strategy, Ltd’s customers shows a wide range of industries and sectors.
11
Not Just for the Big Players Anymore
Nonprofit
Finance &
Insurance
UtilitiesHealth Care
Education &
Universities
Government
Manufacturing
Media &
Entertainment
Retail
Restaurant

Business Intelligence & Analytics
Business Intelligence & Analytics are key to gaining
business insight.
• 80% of respondents indicated that reporting and
analytics were key drivers for data management.
• 87% are implementing business intelligence
• 87% have a data warehouse in place
• 22% are using a data lake in conjunction with a
data warehouse
12
Business Intelligence & Analytics provide Business Insight

a number of respondents mentioned Data Governance in their comments as a way to align the various stakeholders around
common goals
Business Goals & Drivers
• Analytics and Reporting continue to lead the
business drivers for data management.
• Top drivers include:
• Gaining insights through reporting and analytics: 79.70%
• Saving cost and increasing efficiency: 68.42%
• Reducing risk: 66.92%
• Improving customer satisfaction: 58.65%
• Driving revenue and growth: 57.14%
• Supporting digital transformations: 53.38%
13
Gaining Business Insight through Analytics and Reporting continues to be a main business driver for today’s organizations.

Data Governance
Data Governance is critical in supporting the data-
driven business
• 76% have a current data governance initiative in
place or are planning one in the near future
• 86% consider data security a priority
• >50% identified improved collaboration
through using a defined data architecture
14
Data Governance improves collaboration and increases data accountability & protection

common goals
Who is Driving Data Management in an Organization?
• While Technical Roles still lead Data
Management activities, Business
Stakeholders are playing a larger part.
• From those who listed “Other”, Data
Governance Lead was a common
response.
15
A number of respondents mentioned Data Governance as a way to align various stakeholders around common goals

Data is an Asset, but Communication & Quality Remain an Issue
• While the majority of organizations see
data as an essential asset, and manage
security and compliance:
• All stakeholders across the organizations do
not take part in data management
• Communication is an issue
• Data Quality continues to be a challenge
• Formal data management metrics are not
tracked
16

Ethics in Data Management
17
1 United Nations Global Sustainability Goals
How can we use data for greater good?We can do this, but should we do this?
• Anecdotally in our practice, a notable change in 2019 is the increase in the number of clients asking to include
ethics as a formal part of there data governance and data management initiatives:
• Empathetic Customer Journey Mapping
• Analytics to support “Data for Good” -- community health and support initiatives
• Ethics as part of data governance principles and guidelines

Data Platform Evolution
Data Technology & Platforms continue to evolve
• 81% are using relational databases on-premises
• 71% are using spreadsheets as a data platform
• Future plans include a wide range of technologies:
• Cloud-based relational databases
• Graph databases
• NoSQL databases
• Big Data platforms
18
While relational databases remain the leading platform, new technologies are being added to the mix.

common goals
Current Platform Adoption
• Relational Database still dominate the data
management landscape
• Majority is on-premises
• Some Cloud Adoption
• Spreadsheets still ubiquitous, partly due to
the large interest from business users.
19
Relational database still dominate the market, both on premises and Cloud-based

common goals
Future Platform Adoption
• Future Plans still include a high percentage
of relational databases, with a higher
percentage of Cloud-based systems.
• A wider distribution of platform usage
indicates the variety of options and fit-for-
purpose solution – one size doesn’t fit all.
20
Future plans still feature relational databases, with a higher focus on Cloud Adoption, and a wider mix of technologies.

Future Technologies
• Currently implemented:
• Containerized technologies: 55.17%
• Kubernetes: 53.57%
• Serverless Computing (PaaS, FaaS, etc.):
45.45%
• Future Plans:
• Deep learning: 17.65%
• Industry 4.0: 33.33%
• Digital Twins: 8.33%
21
Future plans expand analytics focus to Deep Learning and Industry 4.0 .

Data Management Implementation Now & In the Future
• The Top Data Management components currently
implemented are :
• Business Intelligence and Reporting: 87.02%
• Data Warehouse: 86.55%
• Data Security: 85.95%
• Data Integration: 70.37%
• Document Management: 70.33%
• Data Governance: 61.11%
• Data Quality: 61.29%
• Those planned in the next 1-2 years include:
• Semantic Web Technologies: 76.00%
• Data Virtualization: 63.24%
• Data Science (Including AI or Machine Learning):
54.74%
• Big Data Ecosystems: 53.42%
• Self-service Analytics: 52.63%
• Metadata Management: 52.43%
• Data Governance: 38.89%
22

Prioritizing Efforts for 2020
23
So…
What’s the next Big
Thing?

Top 5 Predictions for 2020
24
1. The blurring of “Business” and “IT” roles will continue
2. The blurring of “Data Management” and “Business” will continue
(e.g. Digital Transformation, Industry 4.0)
3. Organizations will rely on a matrixed set of data-centric tools and technologies
(e.g. relational, NoSQL, graph, etc.)
4. Data governance and ethics will have an increased role in business operations
5. Analytics and BI will continue to be a strong driver, with an evolving focus more towards
AI and predictive analytics, rather than simple descriptive analytics/reporting.

White Paper: Trends in Data Management
• Download from www.globaldatastrategy.com
• Under ‘Whitepapers’
• Also available on Dataversity.net
25
Free Download

DATAVERSITY Data Architecture Strategies
• January 23 Emerging Trends in Data Architecture – What’s the Next Big Thing?
• February 27 Building a Data Strategy - Practical Steps for Aligning with Business Goals
• March 26 Cloud-Based Data Warehousing – What's New and What Stays the Same
• April 23 Master Data Management – Aligning Data, Process, and Governance
• May 28 Data Governance and Data Architecture – Alignment and Synergies
• June 25 Enterprise Architecture vs. Data Architecture
• July 22 Best Practices in Metadata Management
• August 27 Data Quality Best Practices
• September 24 Data Virtualization – Separating Myth from Reality
• October 22 Data Architect vs. Data Engineer vs. Data Modeler
• December 1 Graph Databases: Practical Use Cases
26
Join us next month

About Global Data Strategy, Ltd
• Global Data Strategy is an international information management consulting company that
specializes in the alignment of business drivers with data-centric technology.
• Our passion is data, and helping organizations enrich their business opportunities through data and
information.
• Our core values center around providing solutions that are:
• Business-Driven: We put the needs of your business first, before we look at any technology solution.
• Clear & Relevant: We provide clear explanations using real-world examples.
• Customized & Right-Sized: Our implementations are based on the unique needs of your organization’s
size, corporate culture, and geography.
• High Quality & Technically Precise: We pride ourselves in excellence of execution, with years of
technical expertise in the industry.
27
Data-Driven Business Transformation
Business Strategy
Aligned With
Data Strategy
Visit www.globaldatastrategy.com for more information

Questions?
28
• Thoughts? Ideas?
www.globaldatastrategy.com

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

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