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

Copyright Global Data Strategy, Ltd. 2019
Emerging Trends in Data Architecture
What’s the Next Big Thing?
Donna Burbank, Managing Director
Global Data Strategy, Ltd.
January 24, 2019
Follow on Twitter @donnaburbank
Twitter Event hashtag: #DAStrategies

Global Data Strategy, Ltd. 2019
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 recently awarded the
Excellence in Data Management Award from
DAMA International in 2016.
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 24 Emerging Trends in Data Architecture – What’s the Next Big Thing?
• February 18 Building a Data Strategy - Practical Steps for Aligning with Business Goals
• March 28 Data Modeling at the Environment Agency of England - Case Study (w/ guest Becky Russell from the EA)
• April 25 Data Governance - Combining Data Management with Organizational Change (w/ guest Nigel Turner)
• May 23 Master Data Management - Aligning Data, Process, and Governance
• June 27 Enterprise Architecture vs. Data Architecture
• July 25 Metadata Management: from Technical Architecture & Business Techniques
• August 22 Data Quality Best Practices (w/ guest Nigel Turner)
• Sept 26 Self Service BI & Analytics: Architecting for Collaboration
• October 24 Data Modeling Best Practices: Business and Technical Approaches
• December 3 Building a Future-State Data Architecture Plan: Where to Begin?
3
This Year’s Lineup

Today’s Topic
More than ever, data is driving the global economy as technological
advancements provide unpreceded opportunity with data and information.
• With the myriad choices available , it can be overwhelming to understand where to begin
• This presentation will cover some of the key trends in data management
• Focusing on what can provide practical value for your organization
4

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
5
Product
Focus
Data
Focus
1 Oct 15, 2018, World Economic Forum, “These are the 8 major forces shaping the future of the global economy”
The World Economic Forum sees today’s economy as driven by Data, not Goods & Services

Digital Transformation is a Key Driver
6
8 10
8 out of 10 of Global Data Strategy’s customers and prospects are engaged in Digital Transformation
The majority of our customers and
prospects are focusing their Data
Management efforts in support of
Digital Transformation.
This is consistent across all regions:
• North America
• Latin America
• Europe & UK
• Asia Pacific
Digital
Transformation
All Data
Management
Initiatives

Democratization of Data Management
An analysis of Global Data Strategy, Ltd’s customers shows a wide range of industries and sectors.
7
Not Just for the Big Players Anymore
Nonprofit
Finance &
Insurance
UtilitiesHealth Care
Education &
Universities
Government
Manufacturing
Media &
Entertainment
Retail
Restaurant

Maturity Assessment – Common Themes
• A strong data management foundation is needed to achieve digital transformation.
• Maturity assessments across a range of industries, regions, and organizations found common themes.
8
There are clear and visionary business goals, but are often not
well-aligned with a robust data strategy.
Gaps in alignment between business and tech
teams require Data Governance.
Often challenges in obtaining a “single version
of the truth” for core data.
Data Warehousing typically well-established.
New development underway in less mature
organizations.
BI tools & practices well-established, but often
gaps in self-service capabilities due to lack of
metadata / semantic layer.
Generally some analytics in place, but gaps in desired state due to (1)
high expectations and (2) lack of staff/skills.
Data Quality seen as important, but gaps in quality exist.
Dashboards common for quality monitoring.
Physical data models common, but
business-oriented models less so. Often
gaps in “big picture” system architecture.
Inventory typically exists, but lifecycle &
retention policies often weak.
Data integration generally solid with ETL and
also real-time data streaming, however silos
often exist.
Need-for metadata well known, but often confusion in
solutioning and implementation.

Industry Trends: Data Platforms are Currently in Use?
9
Relational Databases
are still clearly the
leader.
Spreadsheets are
ubiquitous
More Legacy
platforms (44.6%)
than Big Data (42.2%)
From Emerging Trends in Data Architecture, DATAVERSITY, by Donna Burbank & Charles Roe, October 2017
New Survey coming in
March! Stay Tuned…

Industry Trends: Emerging Technologies
10
Many looking to Big
Data Platforms
Movement to the
Cloud is popular
Uncertainty is
common.
• For those looking at new
technologies, there is a wide range
of responses.
• Big Data Platforms a leader
• Move to Cloud RDMBS
• Graph Database
• Real-time Streaming
• Internet of Things (IoT)
• Many are still uncertain, indicating
the vast rate of change and wide
array of choices available.
From Emerging Trends in Data Architecture, DATAVERSITY, by Donna Burbank & Charles Roe, October 2017

Gartner Hype Cycle
• Many of us are familiar with the Gartner
Hype Cycle, which is created for many of the
new and emerging technologies in the
market.
• The core premise is that we tend to
overestimate the effect of a technology in
the short run and underestimate the effect
in the long run.
11
The Hype Cycle is a branded representation from the Gartner analyst firm

Data Management Mountains of Momentum
12
We can do now with new
technology we couldn’t do before
We’ve been doing all along, and
now more people are buying in
We’ve been doing all along,
but we can do them much
better and differently now.
The market is hyping,
but we aren’t really
using yet in real-world
applications
We over-hyped, and are
now implementing more
realistic use cases.
We shouldn’t have done in
the first place, and we
learned our lesson.
We can do now with new technology we
couldn’t do before, but there is a gap
between hype and reality.

New Technology Transformation Provides Opportunity
• It’s an exciting time to be in data management, and there are myriad new technologies that are
transforming the way we do business.
• Cloud Provisioning
• Big Data Storage
• Real-time Data Streaming etc.
• Self-Service Data Prep & Reporting
• Open Data Sets
13

New Technology Transformation Provides Hype
• Some technologies, while exciting and innovative, have been over-hyped so that there is a gap
between expectations and realistic implementations.
• Artificial Intelligence (AI)
• Machine Learning
14

Hype Has Settled Into Reality
• Some technologies had seen a period of hype, but most organizations now have a more realistic
understanding and implementation reality.
• E.g. Data Lake
15
Source: Timo Elliott, timoelliot.com

Traditional Approaches Seeing Innovation
• Many of the traditional, tried-and-true data management fundamentals have seen innovation
through new technologies and approaches.
• Metadata Management
• Data Governance
• Data Warehouse
16
Source: TimoElliott.com
• Data Visualization
• Data Monetization
• AI/Machine Learning
• Microservices
• Data Strategy

Integrating the Data Lake & Traditional Data Sources
• The Data Lake has a different architecture & purpose than traditional data sources such as data warehouses.
• But the two environments can co-exist to share relevant information.
17
Data Analysis & Discovery – Data Lake Enterprise Systems of Record
Data Governance & Collaboration
Master &
Reference Data
Data Warehouse
Data MartsOperational Data
Security & Privacy
Sandbox
Lightly Modeled
Data
Data
Exploration
Reporting & Analytics
Advanced
Analytics
Self-Service BI
Standard BI
Reports

Data Visualization & Reporting
• Data Visualization has seen tremendous innovation
• Both resulting in and caused by a greater interest in business users and self-service
18
A Sea Change
from Microstrategy
from NASA
from Deb Roy at MIT

Metadata Innovation
• Metadata Management has been an established
practice for years, and is seeing a resurgence with the
rise of the data-driven organization.
• Data Catalogs – Not your grandfather’s Metadata
Repository.
19
In a recent DATAVERSITY survey, over 80%
of respondents stated that:
Metadata is as important, if not more
important, than in the past1.
1 Emerging Trends in Metadata Management, 2016, DATAVERSITY, by Donna Burbank and Charles Roe
Interest in Metadata is hotter than ever
Traditional Metadata Repository Modern Metadata Catalog
• Physical data structures – data dictionary
• Automated data lineage for source-target-mapping
• Top-down curation of metadata definitions
“Encyclopedia”
• Data standards and guidelines
• Business Glossary
• Physical data structures – data dictionary
• Automated data lineage for source-target-mapping
• Collaborative approach to metadata creation & curation
(“Wikipedia”)
• Trusted data sets and user ranking
• Machine Learning/AI automation for standards & lineage
• Business Glossary with “Google-style” search

Data Governance Evolution
• As with metadata management, Data Governance has seen a
similar transformation to a more collaborative, business-driven
approach.
• Often the focus is on business innovation, data strategy, and
digital transformation, and organizational structures are often
closely aligned.
20
Data Governance has been the leading driver
for Metadata Management in consecutive
DATAVERSITY surveys1.
This rise in demand has been fueled by
increased interest from business users.
1 Emerging Trends in Metadata Management, 2016, DATAVERSITY, by Donna Burbank and Charles Roe
Trends in Data Architecture, 2017, DATAVERSITY, by Donna Burbank and Charles Roe
Traditional Data Governance Modern Data Governance
• Management of data standards and lineage for regulatory
compliance and development efficiency
• Focus on compliance and policies
• Management of data standards and lineage for regulatory
compliance and development efficiency
• Focus on Collaboration and Information Sharing
• Increased interest and focus from Business stakeholders
• Driven by the “carrot” as well as the “stick”

Traditional Approaches Gaining Buy-In
• Many of the traditional, tried-and-true data management fundamentals have seen a resurgence
in interest and enthusiasm.
• Data Modeling
• Enterprise Architecture
• Data Governance
• Master Data Management
21

Business Goals Driving Interest in Foundational Architecture
22
Conceptual Data Model
Business Capability Model w/
Data Overlay
Business Process Model
As Organizations see Data as a Strategic Asset, a robust architectural foundation is a necessity.
• Data Models
• Business users actively engaged – from scientists, to early childhood teachers,
to water engineer, marketing reps, etc.
• Data Flow Diagrams
• “I never thought I’d use the word ‘data flow diagram’, but this is exactly what I
needed to see why my campaign data is wrong.” – CMO
• CRUD, Data Lineage, and Where Used
• Show gaps in interrelationships for MDM, Data Quality, and more.
• “You mean you’re not doing this already??” – Finance stakeholder
• Business Process w/ Data Overlay
• Critical for Master Data Management, Data Governance, and more
• Business Capability Model w/ Data Overlay
• Critical for Digital Transformation and Culture Change
Business Users and Initiatives driving growth in core architectural artifacts:

MDM: Understanding Your Customer
23
An increased focus on Customer Centricity is driving many MDM initiatives
Stefan Krauss
Occupation = Ski Instructor Purchased €500 in
outdoor gear in 2015
100% of purchases online
Top Finisher in Engadin Ski
Marathon 2010-2015
Member of Loyalty
Program since 2010
Prefers Text Message
Address = Pontresina, Switzerland

Hype Precedes Implementation
• Some technologies have seen a great deal of discussion and hype, but not as much actual
implementations to date.
• Blockchain
24

Blockchain
• Blockchain has received much attention in the media as a way to revolutionize financial and other
industries. According to Harvard Business Review:
25
Among respondents in a recent DATAVERSITY survey1, however, only
7.7% are actively using or pursuing use of Blockchain technology.
“With blockchain, we can imagine a world in which contracts are embedded in
digital code and stored in transparent, shared databases, where they are protected
from deletion, tampering, and revision. In this world, every agreement, every
process, every task, and every payment would have a digital record and signature
that could be identified, validated, stored, and shared. Intermediaries like lawyers,
brokers, and bankers might no longer be necessary. Individuals, organizations,
machines, and algorithms would freely transact and interact with one another with
little friction. This is the immense potential of blockchain.”
1 From Emerging Trends in Data Architecture, DATAVERSITY, by Donna Burbank & Charles Roe, October 2017

Some Things Might Have Been Left Undone
• With all innovation, there are a few mis-steps along the way. The following are some candidates
of things that haven’t panned out.
• “No documentation” Agile
• Data Lakes are replacements for Data Warehouses
• “Just buy a tool and it will fix everything.”
• Open Floor Plans!
26

Summary & Closing Thoughts: What’s the Next Big Thing?
What would a Crystal Ball
into our Data Architecture
Future Look Like?
27
Your thoughts & predictions?
• The rise in the data-driven business and digital transformation will
drive continued innovation & interest around data.
• Self-service approaches will be available for more and more
technologies as business users drive adoption.
• Metadata management will see innovation in automation, self-service,
and visualization.
• Visualization will make its way into technical solutions – storage,
lineage, traceability, etc.
• Increasing data volumes & real-time accessibility will continue to drive
growth in AI and machine learning.
• Foundational methodologies such as data modeling, change and
lifecycle management, etc. will gain adoption as business needs drive
the value of data as a business asset.

DATAVERSITY Data Architecture Strategies
• January 24 Emerging Trends in Data Architecture – What’s the Next Big Thing?
• February 18 Building a Data Strategy - Practical Steps for Aligning with Business Goals
• March 28 Data Modeling at the Environment Agency of England - Case Study (w/ guest Becky Russell from the EA)
• April 25 Data Governance - Combining Data Management with Organizational Change (w/ guest Nigel Turner)
• May 23 Master Data Management - Aligning Data, Process, and Governance
• June 27 Enterprise Architecture vs. Data Architecture
• July 25 Metadata Management: from Technical Architecture & Business Techniques
• August 22 Data Quality Best Practices (w/ guest Nigel Turner)
• Sept 26 Self Service BI & Analytics: Architecting for Collaboration
• October 24 Data Modeling Best Practices: Business and Technical Approaches
• December 3 Building a Future-State Data Architecture Plan: Where to Begin?
28
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.
29
Data-Driven Business Transformation
Business Strategy
Aligned With
Data Strategy
Visit www.globaldatastrategy.com for more information

Questions?
30
• Thoughts? Ideas?

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