Watch full webinar here: https://bit.ly/3zVUXWp In this webinar, we’ll be tackling the question of where our data is and how we can avoid it falling into a black hole. We’ll examine how data blackholes and silos come to be and the challenges these pose to organisations. We will also look at the impact of data silos as organisations adopt more complex multi-cloud setups. Finally, we will discuss the opportunities a logical data fabric poses to assist organisations to avoid data silos and manage data in a centrally governed and controlled environment. Join us and Barc’s Jacqueline Bloemen on this webinar to get the answer and further insights on how to better avoid falling into a #datablackhole. Hope to see you connected!

1. Discover How a Logical Data Fabric
Helps Organisations Overcome
Data Silos And Black Holes
Based on a BARC Topical Survey

2. Jacqueline Bloemen
Sr. Analyst, Data & Analytics,
BARC
Robin Tandon
Director of Product Marketing,
EMEA and LATAM Analyst, Denodo
Speakers

3. Data Black Holes:
How a logical data fabric helps organizations
overcome data silos and blackholes
Jacqueline Bloemen, Senior Analyst Data & Analytics
September 2021

4. Secure decision-making for the digital future of your company
4
Market analyst and consulting company for BI, analytics, CPM,
data management, ECM, CRM and ERP
Founded in 1999 – 50 employees – Offices in Würzburg, Vienna and Zurich
BARC Surveys & Research
Keeping you up-to-date with
software market developments
• Evaluation of current market and
technology development
• Software & vendor evaluation
• Measurement of user satisfaction
• Use and benefits of data and
software solutions
www.barc.de/research
BARC Consulting
Strategy, conception, technology
assessment and project coaching
• Data, analytics and AI strategy
• Data and analytics architecture
• Organization
• Software selection and rollout
• Workshops
www.barc.de/beratung
BARC Events
Further training to inspire your
business
• Conferences: DATA festival,
Digital Finance & Controlling,
SAP Data & Analytics
• Fairs: Big Data & AI World
• Community: Data & Analytics
Leaders’ Circle
• Seminars: BARC Academy
www.barc.de/events

5. Ongoing Debate on Data Architecture
5
All approaches follow similar rationale:
physically move data to one place to collect and compile it there.
But: for highly distributed data landscape with growing data volumes,
extensive data movement is not a viable concept.
Data Lake
(early 2010’s)
Data Warehouse
(late 1980’s)
Data Lakehouse
(late 2010’s)

6. Trying to overcome data silos by building
MORE data silos is like
rearranging the deck chairs on the Titanic.

7. 7
Data Black Holes
Global survey
Wide coverage
of industries…
…and company
sizes
> 300 participants
23%
21%
18%
17%
13%
7%
2%
Industry
Services
Public sector
IT
Banking and finance
Retail / Wholesale / Trade
Other
33%
40%
27%
Less than 500
500 - 4,999
5,000 or more

8. • 63% of participants have a
(semi-) centralized data
provisioning approach.
• 57% of companies lack
uniform data terminology.
• Centralized approaches to
data provisioning
• do not ensure and
• are not a prerequisite for
uniform terminology.
8
Which of the following describes your company‘s data landscape (n=315)?
Data black holes: the high cost of supposed flexibility

9. 2 out of 3 companies state that they frequently extract and copy data,
connect it with other data and prepare it for their individual needs.
Data users see supposed value in their
individual data preparation:
➢ efficiency and
➢ trusted results
However, working with data is particularly
time-consuming for companies lacking a
uniform business terminology.
9
Data black holes: the high cost of supposed flexibility
Most companies are unable to reduce the number of data silos they have!

11. 41%
of organizations are tied up
in maintaining existing data
problems instead of working
on the digital future of the
company.
55%
say that having not enough
resources available is a
major challenge to improving
their current data landscape.
46%
agree that a lack of
transparency concerning
data and analysis processes
is second most common
obstacle.
11
What are the main challenges in the use of data caused by the current data landscape? (n=318)
What organizational and technical challenges have you experienced in implementing approaches? (n=312)
Data silos prevent digital transformation
Companies with uniform terminology perform significantly better!

13. 66% state that they need to frequently extract and copy data, connect it with other data
and prepare it for their needs.
65% agree that the amount of data silos has not yet been significantly reduced.
Solution concepts must integrate data silos instead of fighting them!
13
Which of these approaches is your company taking to deal with the challenges caused by data silos (top
5)? (n=318)
Architecture and technology help balance centralized and
decentralized data requirements

14. Best-in-class companies
have recognized that
modern technologies
play an important role in
overcoming data silos
efficiently and effectively.
14
Which of these approaches is your company taking to deal with the challenges caused by data silos? By
best-in-class (n=89)
Architecture and technology help balance centralized
and decentralized data requirements

16. 75% share data and insights with
other users and departments.
But 37% claim that cross-divisional
collaboration and exchange of data
and data-driven insights is difficult.
Companies with uniform terminology
are 1.5x less likely to struggle with
collaboration issues than those
without.
16
Top 8 selection on “What business and culture-related challenges have you experienced in implementing approaches to
deal with the challenges caused by data silos?” (n=312)
Organizational silos weigh heavier than data silos - overcoming
them is a cultural journey

17. Three in four companies with centralized data provisioning and uniform
terminology have sufficient management support.
17
Organizational silos weigh heavier than data silos - overcoming
them is a cultural journey

19. Today‘s Summary -
Find More
Inspirations in the
Survey!
Data black holes: high cost of
supposed flexibility
Data silos prevent digital
transformation
Architecture and technology:
bridging de-/centralized data
requirements
Organizational silos weigh
heavier than data silos
19

20. Let‘s keep in touch!
20
Jacqueline Bloemen
Senior Analyst Data & Analytics
BARC GmbH, Würzburg
jbloemen@barc.de
+49-931-8806510

21. Logical Data Fabric
and Data
Virtualization
• How it helps break down the
barriers of data silos
• Robin Tandon, Product Marketing Director

22. 22
What are the problems with Data Silos
• BARC notes in their research study
• Data silos carry a high cost for supposed flexibility. Why ?
• Data Silos prevent digital transformation. How ?
• Data Silos stifle agility and innovation
• Data Silos can contribute to poor decision making

23. 23
Conflict Between Centralizing and Decentralizing Data
Stop collecting, Start connecting

24. 24
Logical Architecture – Path to the Future
Stop collecting, Start connecting

25. 25
What is a Data Fabric? In Layman Terms
1. “Integrate data” from disparate data sources
2. Securely deliver an “integrated view” of the different data objects
3. Consume the “integrated data” for analytics and operational purposes
4. Automate the entire process using AI/ML

26. 26
Logical Data Fabric – Technical Architecture

27. 27
IT Architecture is Unmanageable & Brittle because:
Key IT data Silo Dilemma
IT responds by
loosely stitching
together
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)

28. 28
A logical data layer – a “data fabric” – that provides high-performant, real-time, and secure access to
integrated business views of disparate data across the enterprise
The Denodo Platform
• 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

29. 29
Denodo Platform 8.0 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

30. 30
“Get it Real-time and Get it Fast!”
The Benefits of Data Virtualization
✔Complete enterprise information, combining
Web, cloud, streaming, and structured data
✔ROI realization within 6 months, with the
flexibility to adjust to unforeseen changes
✔An 80% reduction in integration costs, in
terms of resources and technology
✔Real-time integration and data access,
enabling faster business decisions

31. 31
Denodo Data Catalog
Data Catalog within Data Virtualization to seamlessly integrate data catalog and data delivery
Dynamic Catalog of curated, timely, contextual, and reusable
information assets and data services.
Govern – Fine-grained privilege that governs access to the catalog;
both metadata and information assets.
Describe – Ability to describe data assets with categorization, tagging,
annotations, lineage and other business-oriented metadata.
Usage-based metadata – who, when, what, why, and how of data
consumption.
Lightweight Data Preparation – Ability to transform, refine, and
customize data assets for business use.
Enhanced UI – Business-friendly user interface geared to roles such as
data stewards, data analysts, and citizen analysts

32. 32
Objectives for the Modern Data-Driven Business
1. Single entry-point to explore and query ALL data
• Users don’t want to waste time searching across different data sources
• IT doesn’t want their users having access to all their production systems
2. Create a self-service culture for data consumers
• Users don’t want to have to learn to code (SQL, Python, Java, etc)
• They want to use the tools they’re most comfortable with
3. Implement security & governance across multiple systems
• Leadership wants to reduce the amount of data that’s copied across the org
• Minimize the risk of a data breach & avoid creating multiple versions of truth

33. Festo Customer Use Case

34. Business Need Solution Benefits
34
Festo is dedicated to maximizing productivity and competitiveness for process manufacturing companies,
paving the way for their digital transformation. The company develops future-oriented products founded
on innovative, energy-efficient technologies, intuitive human-machine collaboration, and advanced
training.
Case Study
• Festo needed to optimize operational efficiency,
automate manufacturing processes, and deliver
on-demand services to its business consumers.
• Festo also needed its business users to become
self-sufficient with reporting and analysis and
reduce their reliance on IT for preparing and
surfacing the data they need.
• Festo’s business teams had launched strategic
projects to maximize energy efficiency, and they
needed to be able to provide instant visibility on
energy usage directly to the shop floor teams.
The Denodo platform supports Festo’s big data
analytics framework by
• Delivering enhanced insight across the business
without having to physically move data
• Simplifying data consumption, because data
virtualization is source-agnostic and provides a
single endpoint for accessing all data
• Quickly integrating new data sources and
making them available to user communities in
real time
• Facilitating smarter decision making via
additional information enrichment capabilities
• Increasing the speed and agility of both
business and IT, because business users can
now drive and maintain their own dashboards,
significantly increasing customer satisfaction.
• The Festo big data team developed a big data
analytics framework to provide a data
marketplace to better support the business. Using
the Denodo platform, this framework integrates
data from numerous on-premises and cloud
systems, including streaming data, machine data,
and data-at-rest, and provides access to the
integrated data in real time.
• Festo implemented the Denodo platform within
the big data analytics framework. The logical layer
delivered by Denodo provided virtual views that
were tailored for business analysts, data
scientists, and developers across multiple
departments.
As the world’s leading supplier of automation technology and technical education, Festo deploys its products and
services to help customers implement smart production capabilities while going digital.

35. 35
Conclusion
• A logical data fabric eliminates the high costs involved in flexibility by
providing a single virtualized layer to inquire against
• Digital transformation is encouraged by data virtualization, by providing
easy to use and governed data access
• Data Silos stifle agility and innovation
• The intelligent Denodo Platform, helps users and organizations understand
the data in real time encouraging agility innovation. Data views can be
changed on the fly as more data becomes available.
• A properly implemented logical data fabric, allows users access to all of the
data all of the time, facilitating better decision making

36. 36
Next Steps
Access Denodo Platform in the Cloud!
Take a Test Drive today!
www.denodo.com/TestDrive
GET STARTED
TODAY

37. Q&A

38. Thanks!
www.denodo.com info@denodo.com
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