The document discusses the importance of data virtualization in modern analytics, emphasizing its ability to integrate diverse data sources and deliver real-time insights. It outlines challenges faced by enterprises, such as data silos and rapid data growth, while highlighting the increasing adoption of data virtualization solutions across various industries. Additionally, it touches on the technological advancements and use cases for data virtualization, indicating strong growth potential in the coming years.

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Why Data Virtualization Matters In
Your Portfolio
Noel Yuhanna, Principal Analyst
Forrester Research
Denodo Datafest 2018

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Who likes Cocktails?
Mojito

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A mojito requires several ingredients to make a
perfect one….
Mojito

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Wrong combination of ingredients can spoil a
cocktail…

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Analytics is similar … it needs the right blending of
business data to create meaningful insights ….
RDBMS
Hadoop
RDBMS
Data Lake
100sTB
Petabytes
Petabytes
Data Lake
Cloud
Source
EDW
NoSQL
NoSQL
10s Terabytes
100s Terabytes
EDW
Zettabytes

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What are the top data challenges? Forrester Survey
Data Governance, Data Silos and Data growth…..
Source: Forrester custom survey, 2017, 235 Large companies.
60%
65%
53%

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Data Challenges – Have changed over the years…
› More than 35% of enterprises have failed to get value from big data
projects largely because of skills, budget, complexity and strategy.
› Most organizations are dealing with growing multi-format data
volume that’s in multiple repositories -relational, nosql, hadoop, lake..
› Need has grown for more real-time and agile data requirements
› Lack of visibility into data across personas -- developer, data scientist,
data engineers, data architects, security etc..
› Traditional data platforms are failing to support new business
requirements – such as data warehouse, relational DBMS, and ETL tools.

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Trend: Orchestrating data more intelligently to achieve
actionable insights….
Orchestration
IntelligentCloud
Source
EDW
NoSQL
NoSQL
10s Terabytes
100s Terabytes
EDW
Zettabytes
RDBMS
Hadoop
RDBMS
Data Lake
100sTB
Petabytes
Petabytes
Data Lake

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Data Virtualization delivers a platform that focuses on real-
time, agile and intelligent orchestration ….
Cloud
Source
RDBMS
Hadoop
RDBMS
EDW
Data LakeNoSQL
NoSQL
100sTB
Petabytes
Petabytes
10s Terabytes
100s Terabytes
EDW
Zettabytes
Data Virtualization
Intelligent orchestration
Data Lake
Self-service, real-time, automated, secure

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“Data Virtualization” – A Modern Data Platform
› DV overcomes the data silos issue – not only from traditional data
sources, data lakes, NoSQL and Hadoop/Spark clusters etc.
› DV support zero-code/low-code for data preparation, integration,
transformation – converting data into actionable insights rapidly
› DV focuses on self-service for IT and business
› DV leverages AI/ML to automate the process and delivery of information
to various dashboards
› DV enhances data security by centralizing access controls.

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Data Virtualization – Strong Growth and Momentum
› Global DV adoption is around 35% across enterprises and likely to
double in the next four years by 2022. Most have so far leveraged in-
house development to build DV.
› DV in the cloud is growing rapidly. Estimated 30% of the all DV
deployments are in the cloud, with 55% of all new DV started in the cloud.
› Large scaled DV deployments that are running into Petabytes.
› Adoption is strong across all verticals including financial services,
healthcare, retail, oil and gas, government and tech.
› Number of inquiries at Forrester on DV has grown 50% vs. in year 2017.

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Data Virtualization: Forrester’s Reference Architecture

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Innovation in DV – 2018-2023
› Increased automation – with AI/ML in every layer of DV
› Self-service for business – simplified access to data
› Support for hybrid-cloud with seamless integration
› Intelligent security – to support data protection and compliance
› Global and distributed across various data centers/clouds
› And others..

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Top use cases for Data Virtualization are many --
› 360-degree view of customer, product, and business
› Fraud detection and risk analytics
› Centralized access control of critical data…
› Data landing/staging area for EDW, Hadoop, and data int.
› Integrated real-time analytics — across various silos
› Various dashboard — customers, partners, etc.
› Various vertical specific use cases . . .

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Trivia
In which year did Forrester
publish its first report on Data
Virtualization?

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Trivia
In which year did Forrester
publish its first report on Data
Virtualization?
January 2006 -- 12 years ago!

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Forrester research on Data Virtualization goes back
more than a decade …
DV Reference Architecture Forrester DV Wave’s 2008 to 2017
2012
2008
2017
2006
2010
2015
2007
2013

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Forrester Wave: Data Virtualization, Q4 2017
› Evaluated 13 vendors across 25 criteria.
› “Customers like Denodo’s easy-to-use,
simple yet sophisticated data modeling
capabilities; search capabilities; and support
for various big data sources.”
› “Customer references reported that they use
Denodo to support operational, analytical,
and big data workloads, a 360-degree view of
the customer, risk analytics, real-time
analytics, and predictive analytics. Customers
like Denodo's scale, performance, ease of
use, security, and business value.”

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Recommendations
› Start with a few data sources to integrate… don’t boil the lake!
› Create a DV team to succeed – EA, DE, security, business analysts..
› Leverage in-memory to improve performance – including Flash/SSD.
› Leverage ML and AI to automate various layers of DV – all layers.
› Keep security in mind from the start – access control/GDPR.
› Focus on low-code/zero-code – stop writing code for integration.
› DV can support multiple use cases – start with one and grow.

20. FORRESTER.COM
Thank you
© 2018 FORRESTER. REPRODUCTION PROHIBITED.
Noel Yuhanna
nyuhanna@forrester.com
Twitter: @nyuhanna