Watch full webinar here: https://bit.ly/3xozd5W Companies today want to realize the value of data and share it across the enterprise. While unlocking the full potential of data for business users, these companies must also ensure that they maintain security requirements. Learn how you can successfully implement self-service initiatives with data governance to enable both business and IT to realize the full potential of any data in the enterprise. Watch Now On-Demand!

1. VIRTUAL SOLUTION SPOTLIGHT
Enabling Data Self-Service
with Security, Governance,
and Regulatory Compliance

2. Sponsor
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3. VIRTUAL SOLUTION SPOTLIGHT
Logical Data Fabric: Achieving
Balance Between Self-Service
Agility and Governance
David Stodder, Senior Director of Research, TDWI

4. DAVID STODDER
Senior Research Director
Business Intelligence
TDWI
dstodder@tdwi.org
@dbstodder

5. Agile Business Demands Drive Modernization
• Being data-driven: Organizations compete on
ability to make data-informed decisions
– Strategies for achieving positive outcomes despite
new variables and uncertainty
– Assessing situations in real time; complete,
relevant data to analyze patterns and trends
• Actionable intelligence
– Need for fresh, up-to-date data for all
stakeholders, internal and external
– Performance management: Latest data to
measure and optimize processes more frequently
• Agility: Data agility underpins business agility
– Innovation and new approaches to markets
Copyright © TDWI

6. Modernization Driver: New and Diverse Users
• Data democratization: Users
need freedom to visualize,
analyze, and share data
– Single views of data from
multiple sources
– Ad hoc, in response to
changing situations
– Adding external users, e.g.,
business partners
• Supporting daily
operational decisions: 40%
want to enable real-time data
integration and analytics
Data Integration Modernization Objectives
Expand data visualization,
BI, and analytics to more
users
51%
Increase data science,
predictive modeling, AI/ML
43%
Enable real-time data
integration and analytics
40%
Improve data insights for
resiliency and continuity
34%
Drive informed marketing,
sales, and service
33%
Reduce costs of data
integration and
management
31%
Provision single, complete
views or versions of truth
29%
Reduce risk (business,
market, financial, fraud)
29%
Improve data insights for
real-time engagement
27%
Source for all research quoted in this presentation: Q4 TDWI 2021 Best Practices Report
Copyright © TDWI

7. Modernization Driver: Expanding Data Universe
• Confines of legacy data warehouses
– Users need multiple sources of structured,
semi-structured, and unstructured data
– Single views of relevant data, whether on
premises or in the cloud (51% top priority)
• Digital transformation: Generating new
data and demands for data
– New user, partner, and customer touchpoints
• Trend toward real-time data and analytics
– Decision makers need flow of real-time data in
operations, business processes, supply
chains, manufacturing, and more
Image credit: FXTransparency
Copyright © TDWI

8. Challenges: Legacy Patterns and Data Silos
• Frustration: Users must pursue data outside
the legacy data warehouse confines
– 31%: Data integration and management too
inflexible for changing user requirements
– Users often limited to lagging (e.g., monthly)
updates; adding new data is slow
– Incomplete views: Users need all relevant data
to make good decisions and get things done
• Data silos and fragmentation grow
– 61%: Data quality, completeness, and
consistency are a major hindrance
– 38%: Data silos make access and portability too
difficult (e.g., across cloud platforms)
Image credit; Flickr
Copyright © TDWI

9. Challenges: Data Movement & Transformation
• ETL for DW requires much data movement
– Big effort to combine data from different sources
for up-to-date reports, dashboards, and analytics
– Continuous copying, transforming, and combining
data; latency and errors increase with volume and
speed demands; 31% reducing latency top priority
• Too much time on data prep and pipelines
– 35% say users spend more than 60% of time on
data preparation and pipelines
• Complexity challenges in hybrid multicloud
environments
– Users need unified views of data drawn from
physically distributed data stores
Copyright © TDWI

10. Obstacles to Users Realizing Value from Data
• Users need transparent
access to all relevant data
– Without knowing
intricacies of access
• Confines of legacy DW:
users’ views restricted
– Dynamic business
requires quicker data
transformations with less
data movement
• Real-time problems
– 31% say reducing
latency to gain real-time
views is a top priority
Most significant hindrances to data-informed
decisions and realizing value from data assets
Data quality, completeness, and
consistency concerns
61%
Cannot view or access all
relevant data in a single view
43%
Slow data updates and
refreshes; inadequate real-time
data access
39%
Data silos make access and
portability too difficult (e.g.,
across cloud platforms)
38%
Governance and regulatory
adherence concerns
33%
Too inflexible to adjust to
changing user requirements
31%
Self-service data blending from
multiple sources is too slow
and difficult
29%
Users cannot develop data
pipelines on their own
25%
Cannot scale to handle rising
data volume and variety
20%
Source: TDWI 2021 research.
Copyright © TDWI

11. Governance Challenges Amid Silos, Self-Service
• Diverse data silos and fragmentation
challenge adherence to governance rules and
regulatory policies
• Lack a single point of control for
governance protocols, user authentication,
and access control
– Self-service: Who is using the data and how?
– APIs: What data does our governance allow us
to expose? Who will access it?
• Hybrid multicloud challenges
– 67% say improvement is a high priority
Copyright © TDWI

12. Logical Data Fabric: Responding to Challenges
• The fabric: Universal and holistic
approach to integrating diverse and
distributed components
– Not limited to one location (data in cloud,
on premises, third party data services)
– Future-proof: Anticipates needs for
agility, greater scalability, data diversity,
and real-time requirements
• Objectives: Make it seamless to view,
access, and manage all data
– Improve governance and security
– Reduce costs of data management
• 64%: Important to create a single virtual
data repository or data fabric
• 67%: Governance across hybrid
multicloud is important
• 21%: Data fabric for connecting to and
querying data wherever it resides is part
of current strategy; 39% in the future
Copyright © TDWI

13. Critical Technologies: Data Virtualization
• Logical data fabric: Builds on data virtualization
experiences
– 29% currently using data virtualization layer; 30%
plan to use one
• User transparency: Abstraction layer across
multiple, distributed, heterogeneous repositories
– Data virtualization enables views and federated
querying, no matter where data physically resides
• Reducing demand for slow and costly data
movement, replication, and duplication
– Problem of synchronizing copies with sources
– Time and expense of pipelines for extraction
Image credit: Dataversity
Copyright © TDWI

14. Critical Technologies: Metadata Resource
• Logical data fabrics use metadata and higher-level
semantic knowledge about data
• Data catalogs: Resources about how data is defined,
modeled, and located
– 31% currently use one; 36% plan to
• Catalog integration with virtualization layer
– Faster, more complete viewing, querying, governance
– 36% are currently satisfied with integration; 25% say this
is a future priority
• Governance and data lineage
– Integration of data virtualization and catalog in logical
data fabric governance supports authorized access,
knowledge of data life cycle, and policy adherence
Copyright © TDWI

15. In Closing: Recommendations
 Achieve a better balance by developing strategies for
a logical data fabric
– Facilitates self-service data exploration and querying
– While also supporting less piecemeal and more
comprehensive governance and authorized access
 Improve user data transparency with a logical data
fabric
– Actionable intelligence based on complete, up-to-date data
– Move beyond limits of individual physical data repositories
 Use a logical data fabric for data-driven business
innovation
– Agility for analytics and development of new business
models and markets
Copyright © TDWI

16. Thank You
David Stodder
Senior Director of Research for Business Intelligence
TDWI (www.tdwi.org)
dstodder@tdwi.org
@dbstodder

17. Fireside Chat
How Fifth Third Bank
Achieved Regulatory Excellence
Through Self-Service Analytics

18. RAVI SHANKAR
Senior Vice President and
Chief Marketing Officer
Denodo
KAYLEIGH LAVORINI
Technical Product Owner –
Self-Service Analytics
Fifth Third Bank

19. Data Virtualization: How
to Logically Enable Self-
Service…
…Security, Governance, and Regulatory
Compliance
Ravi Shankar, SVP & CMO
November 2021

20. 20
COVID-19 Recession Forces Business Agility

21. 21
IT Architecture is Unmanageable & Brittle because the Connections are Hard Coded
Business Agility Demands “Technology Agility”
IT responds by
hard coding the
connections
among disparate
data sources
Any changes
break the flow
and affect
business
continuity
Business Wants All of the Data, Now
– So IT creates 100s to 1000s of brittle direct connections and
replicates large volumes of data
Inventory System
(MS SQL Server)
Product Catalog
(Web Service -SOAP)
BI / Reporting
JDBC, ODBC,
ADO .NET
Web / Mobile
WS – REST JSON,
XML, HTML, RSS
Log files
(.txt/.log files)
CRM
(MySQL)
Billing System
(Web Service -
Rest)
ETL
Portals
JSR168 / 286,
Ms Web Parts
SOA,
Middleware,
Enterprise Apps
WS – SOAP
Java API
Customer Voice
(Internet,
Unstruc)

22. 22
Top Trends in Data & Analytics, 2021

23. 23
Logical Data Fabric
Logical Data
Fabric
Consumers
Data
Science
Machine
Learning
Artificial
Intelligence
Mobile
Applications
Predictive
Analytics
Business
Intelligence
Relational NoSQL Unstructured Docs Cloud Sensors IoT
Sources
Unified Data Security
Abstraction Layer
Universal Access
Self-
Servic
e
Data
Catalo
g

24. Logical Data Fabric and Data
Virtualization

25. 25
Data Virtualization: Foundation for Logical Data Fabric
• Data Abstraction: decoupling
applications/data usage from data
sources
• Data Integration without replication
or relocation of physical data
• Easy Access to Any Data, high
performant and real-time/ right-
time
• Data Catalog for self-service data
services and easy discovery
• Unified metadata, security &
governance across all data assets
• Data Delivery in any format with
intelligent query optimization that
leverages new and existing
physical data platforms
A logical data layer that provides high-performant, real-time, and secure access to integrated business
views of disparate data across the enterprise

26. 26
Gartner Hype Cycle for Data Management, July 2021
“Data
Virtualization is in
the Plateau of
Productivity” –
Signifies very low
risk and high-level
ROI from
investments in
these DM tools
and architectures

27. 27
Data Virtualization Architecture
DATA CATALOG
Discover - Explore - Document
DATA AS A SERVICE
RESTful / OData
GraphQL / GeoJSON
BI Tools Data Science Tools
SQL
CONSUMERS
DATA VIRTUALIZATION
CONNECT
to disparate data
in any location, format
or latency
COMBINE
related data into views
with universal semantic
model
CONSUME
using BI & data science
tools, data catalog,
and APIs
Self-Service
Hybrid/
Multi-Cloud
Data
Governance
Query
Optimization
AI//ML
Recommendations
Security
LOGICAL
DATA
FABRIC
SOURCES
Traditional
DB & DW
150+
data
adapters
Cloud
Stores
Hadoop
& NoSQL OLAP Files Apps Streaming SaaS

28. Use Case Example

29. 29
Prologis: World’s Largest Industrial Property Owner

30. 30
Long delays in finding answers
to key business problems,
resulting in longer time-to-
value
“The fastest we could ever move in our old stack
was monthly releases and that was assuming we
were not bringing in any new data. If we were
bringing in new data, the best that we could do
was a month and a half to turn around a new
data set coming into the data warehouse and get
it modeled within the reporting tool to produce a
report. It was a huge amount of time. It was a
monolith.”
- VP of Data & Analytics, Real Estate
Drivers for Organizational Investment
Prior Challenges

31. 31
Centralized Data Access Model
Using Physical Architectures
Created Bottlenecks and
Limited Data Use
“In the old world, it was a centrally managed
capability where the business came to my team
with a request and then the output from my
team was a report. Even though my team was
pretty large at that time, we were always limited
because we couldn’t serve 2,000 employees with
a nine-person team and be nimble about it.”
-VP of Data & Analytics, Real Estate
Drivers for Organizational Investment
Prior Challenges

32. 32
Prologis: Logical Data Fabric using Data Virtualization

33. 33
Benefit: Improved Operational Efficiency
“From the [operations] perspective,
being able to provide business users
with the capability to start asking and
answering questions themselves … it
has really given us the ability to see
around the corner. There is no way
that my team, even if we dropped
everything, would have been able to
react to a situation like the pandemic
as fast as we did without Denodo.”
-VP of Data & Analytics, Real Estate
Customer Experience
SUMMARY
THREE-YEAR
FINANCIAL IMPACT
$1.7M
KEY VALUE CAPTURE
$1.7M improvement in
operational efficiency
from year-to-year
33% year-to-year growth
in Denodo’s annual
impact on operational
efficiency
Denodo’s data virtualization
software enables organizations to
create complex datasets using real-
time data across multiple data
sources that they were not able to
create before. These new datasets
can provide insight on decisions to
effectively reduce operations costs.

34. 34
Try Denodo!
Access Denodo in the Cloud!
Start your Free Trial today!
www.denodo.com/free-trials
GET STARTED TODAY

35. Thanks!
www.denodo.com info@denodo.com
© Copyright Denodo Technologies. All rights reserved
Unless otherwise specified, no part of this PDF file may be reproduced or utilized in any for or by any means, electronic or mechanical, including photocopying and microfilm, without prior the written authorization from Denodo Technologies.

36. CONTACT INFORMATION
If you have further questions or comments:
David Stodder, TDWI Kayleigh Lavorni
dstodder@tdwi.org kayleigh.lavorini@53.com
Ravi Shankar
rshankar@denodo.com
tdwi.org

37. Thank You to Our Sponsor
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