DAS Slides: Data Virtualization – Separating Myth from Reality

Copyright Global Data Strategy, Ltd. 2020
Data Virtualization
Separating Myth from Reality
Donna Burbank
Global Data Strategy, Ltd.
September 24th, 2020
Follow on Twitter @donnaburbank
@GlobalDataStrat
Twitter Event hashtag: #DAStrategies

Global Data Strategy, Ltd. 2020 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.
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
@GlobalDataStrat
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
4
• Data virtualization is a practice that logically integrates data from disparate sources without
the need to physically move the data.
• While this can be an appealing prospect, there is a good deal of confusion around this
technology and how to use it to full advantage.
• This webinar will explain the pros and cons of data virtualization along with practical
use cases for implementation.

What is Data Virtualization?
5
Data Warehouse
011001101
101010010
Data Lake External Data Feeds Databases
Etc…
Data Virtualization is a logical data layer that integrates disparate data sources across the
enterprise without physically moving the data.
Data Virtualization Layer
Reporting &
Query Layer

What is Data Virtualization – Analyst Definition
Data virtualization — The utilization of logical views of data, which may or may not be cached in various
forms within the data integration application server or systems/memory managed by that application server.
Data virtualization may or may not include redefinition of the sourced data.
Gartner Magic Quadrant for Data Integration Tools, 18 August 2020 - ID G00450251
6

Data Virtualization Trends
7
• Data virtualization does not have the
widespread adoption that other data
management trends enjoy (e.g. DW),
according to a recent DATAVERSITY
survey.*
• Adoption has declined significantly with a
decrease of nearly 30% between
2020 and 2019.
Note: Respondents were able to select more than one response.
* Trends in Data Management, a 2020 DATAVERSITY® Report, by Donna Burbank and Michelle Knight

Data Virtualization Trends
8* Sharat Menon, Mark Beyer, Ehtisham Zaidi, Ankush Jain, Gartner Market Guide for Data Virtualization, November 16, 2018
• Although current adoption is low, many see positive adoption trends in the future.
• According to the Gartner global research and advisory company,
• Through 2022, 60 percent of enterprises will implement some form of data
virtualization as one enterprise production option for data integration
• Compared to less than 40 percent in 2018 and 11 percent in 2011.

Data Movement vs. Data Virtualization
9
ETL
Data
Warehouse
Cubes /
Semantic Layer
Source Systems BI / Reporting LayerData Consolidation &
Historical Trending
Data Transformation
& Movement
Traditional approaches, such as Data Warehousing, typically rely on moving data from
source systems to a centralized location.

10
Source Systems
BI / Reporting Layer
Data Virtualization leaves data in place, and provides a virtualization layer across these
systems for end user query and access.

11
Source Systems
BI / Reporting Layer
Disparate data sources and data types can be integrated via the virtualization layer –
without the need to move the data from its source.
External Data FeedsERP & CRM Systems Cloud Data Platforms
Etc..

Data Virtualization vs. ETL
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Extract, Transform, Load (ETL)
• Best when physical movement,
integration and storage of the data is
needed
• Enterprise-grade efforts where business
rule planning & enforcement is required.
• Batch updates & processing (e.g. nightly,
weekly, monthly)
• Supports data transformation, cleansing,
grouping, etc.
The two solutions can complement each other in an organization – it doesn’t need to be an either/or.
Data Virtualization (DV)
• Best when it is difficult, time-consuming,
or expensive to physically move data.
• Rapid prototyping or flexible environment
where frequent changes required.
• Near real-time updates of data
• Delegates queries, joins and aggregations
to the source system and returns the
rows.
ETL

Data Virtualization vs. BI Cubes
13
BI Cubes
• Easily understandable by business
users
• Provides user-friendly semantic layer
• Specific to a particular BI tool (vendor
lock-in)
• Facilitates “slicing & dicing” of data
Data Virtualization (DV)
• Requires SQL query skills – may be
difficult for business users
• Can be used across multiple BI tools
and solutions – avoid vendor lock-in
• Good solution for data scientists, BI
developers, etc.

Data Virtualization - What’s “Behind the Curtain”?
14
Data Virtualization isn’t “magic” and is generally supported by
platform vendors who provide a wide array of component services to
manage the virtualization layer.
Security
Query
Optimization
Data Catalog
Data Federation
Data Quality
Lifecycle
Management
Data
Governance
Data Services &
Interoperability
Infrastructure
Management
Etc.

What Data Virtualization is Not
15
• Data Virtualization is not an easy way out doing the hard work of:
• Data Modeling
• Data Quality
• Data Governance & Stewardship
• Privacy & Security
• Master & Reference Data
• Etc…

Use Cases
16
Data Virtualization Use Cases
• Integration with disparate data sources
– including a data warehouse
• Real-time data access
• Rapid Protoyping
• Data exploration
Not Data Virtualization Use Cases
• Enterprise Data Warehouse
• Centralized Master Data Management
• Reference Data
• Graph Data
• Document Management

Summary
• Data Virtualization provides a flexible way to integrate
data from disparate systems without the need to
physically move the data
• While current adoption is low, expectations are high for
future growth
• Data virtualization does not obviate the need for strong
data governance, security, etc.
• It is important to clearly understand the use cases in
order to select the correct fit for purpose solution
• Data virtualization can be a complementary technology
to other enterprise data solutions (e.g. data warehouse)

Trends in Data Management: A 2020 Report
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Whitepaper available for free download on
Dataversity.net at:
https://content.dataversity.net/DV2020DataManage
mentRP_DownloadWP.html

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.
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Data-Driven Business Transformation
Business Strategy
Aligned With
Data Strategy
Visit www.globaldatastrategy.com for more information

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 – with Nigel Turner
• 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
20
Join us next month

Questions?
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• Thoughts? Ideas?

DAS Slides: Data Virtualization – Separating Myth from Reality

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DAS Slides: Data Virtualization – Separating Myth from Reality