1. 1

2. Speakers
Alberto Pan
Chief Technology
Officer
Juan González
Consultor Senior
Líder Técnico
Marcelo Méndez
Gerente
General

3. Enabling Agile Analytics
and Data Governance with
Data Virtualization
Free your Data
Alberto Pan
CTO
March 2021

4. Agenda
1. Current Challenges in Data Management
2. Data Virtualization and the Logical Data Warehouse
3. Data Virtualization: What Analysts Say
4. Case Studies
5. Q&A

5. Current Challenges in Data Management
1. Faster & more complex demands for decision making
▪ Provide useful information for decision making at all organization levels
▪ New users with advanced analytical skills and needs: e.g. data scientists
▪ Solution? Self Service Initiatives lead by business users, etc. → Either too complex (direct
access) or too costly (specific data marts) , Governance and consistency problems
2. Regulations, enterprise-wide governance & data security
▪ Tens of new regulations worldwide: tax, finance, privacy, HR, environmental, etc.
▪ Ensure consistency in semantics of delivered data and data quality
▪ Enforce security policies
▪ Solution? Data Governance tools. Separate, static system for documentation→ get out of sync
easily, don’t enforce policies & don’t deliver data to users
3. Complexity of DM infrastructure: IT cost reduction
▪ Huge data growth, operation costs → IT is looking for cheaper and more flexible solutions
▪ Solution? Cloud, Data Lakes → Increase integration complexity in the short term. E.g. Gartner
says “83% of Data Lakes projects have failed”

6. 6
What is the Problem ?
Lack of Agility:
• No unified infrastructure (multiple data sources and
analysis / visualization tools)
• Integrating, transforming and combining data is slow with
traditional methods
Agility vs Governance:
• Inconsistent reports / Single Source of Truth
• Compliance with company glossaries and policies
• How to enforce consistent security, data quality and
governance policies across multiple systems
• Too much replicated data

7. 7
Do Data Governance Tools Solve the Problem ?
DG Tools allow:
• Informing about data assets and their level of quality
• Defining unified glossaries and terminology
• Defining data quality and data governance policies, and
managing/tracking changes
Disconnected from the data delivery process
• Do not ensure delivered data conforms to glossaries
• Do not enforce security, data quality and governance
policies in the data delivery process
• The problem of how to enforce these policies across multiple
data sources and consumption tools remain

8. 8
Gartner – The Evolution of Analytical Environments
This is a Second Major Cycle of Analytical Consolidation
Operational Application
Operational Application
Operational Application
IoT Data
Other NewData
Operational
Application
Operational
Application
Cube
Operational
Application
Cube
? Operational Application
Operational Application
Operational Application
IoT Data
Other NewData
1980s
Pre EDW
1990s
EDW
2010s
2000s
Post EDW
Time
LDW
Operational
Application
Operational
Application
Operational
Application
Data
Warehouse
Data
Warehouse
Data
Lake
?
Logical Data Warehouse
Data Warehouse
Data Lake
Marts
ODS
Staging/Ingest
Unified analysis
› Consolidated data
› "Collect the data"
› Single server, multiple nodes
› More analysis than any
one server can provide
©2018 Gartner, Inc.
Unified analysis
› Logically consolidated view of all data
› "Connect and collect"
› Multiple servers, of multiple nodes
› More analysis than any one system can provide
ID: 342254
Fragmented/
nonexistent analysis
› Multiple sources
› Multiple structured sources
Fragmented analysis
› "Collect the data" (Into
› different repositories)
› New data types,
› processing, requirements
› Uncoordinated views
“Adopt the Logical Data Warehouse Architecture to Meet Your Modern Analytical Needs”. Henry Cook, Gartner April 2018

9. 9
Denodo proprietary and confidential. DO NOT DISTRIBUTE
Gartner: Unified Data Integration, Delivery and Governance
Denodo

10. 10
Denodo’s Logical Data Fabric Links: Business Interface to Data
1. Single Access Point to all Data
at any location
2. Semantic Layer – Expose Data
in Business-Friendly form,
adapted to the needs of each
consumer
3. Up to 80% reduction in
integration costs, in terms of
resources and technology data
4. Consume data with any tool
and access technology (SQL,
REST, GraphQL, OData,…)
5. Single entry point to apply
security and governance
policies
6. Abstraction: change vendor /
location / processing engine
without affecting data
consumers

11. 11
Data Virtualization: Logical Data Delivery for the Business
Development
Lifecycle
Monitoring & Audit
Governance
Security
Development Tools
/ SDK
Scheduler
Cache
Optimiser
JDBC/ODBC/ADO.Net REST / GraphQL / OData
U
LoB
View
Mart
View
J
Application
Layer
Business
Layer
Unified View Unified View
Unified View
Unified View
A
J
J
Derived View Derived View
J
J
S
Transformation
& Cleansing
Data
Source
Layer
Base
View
Base
View
Base
View
Base
View
Base
View
Base
View
Base
View
Abstraction

12. 12
Data Virtualization for Data Governance
Single Entry Point
for Enforcing
Security and
Governance
Policies
Data on-premises
and off, combined
through the same
governed virtual
layer
Single Source of
Truth / Canonical
Views
Who is Doing /
Accessing What,
When and How
Fewer copies of
personal data.
Lineage of copies
is available.

13. Query Execution and Performance
Source
Abstraction
Virtual
Modelling
Business
Delivery
Query Optimizer
Security & Governance
Query Engine
Delegate processing to data sources
▪ Transparently switch workloads according to cost or performance
Most advanced execution engine for distributed scenarios
▪ Unique techniques automatically rewrite user queries to maximize
pushdown
▪ Leverage MPP capabilities to deal with large data volumes
Advancing Caching / Acceleration Mechanisms
▪ Selectively materialize subsets of the data for protecting data
sources and query acceleration
▪ Machine Learning for automatically proposing selective data
materializations for query acceleration

14. 14
Source: Gartner 2018 Data Virtualization Market Guide
In 2020, organizations utilizing data virtualization will spend 45% less
on building and managing data integration processes.”
Through 2022, 60% of enterprises will implement some form of data
virtualization as one enterprise production option for data integration.
Source: Gartner 2018 Data Virtualization Market Guide

15. 15

16. 16
Gartner Gives DV its Highest Maturity Rating
“Data Virtualization
can be deployed
with low risk and
effort to achieve
maximum value.”

17. 17
Source: Gartner Magic Quadrant for Data Integration, August 2018
Denodo continues to expand its leadership and mind share in data
virtualization, reaching almost 95% of Gartner client inquiries on the subject.”
Denodo grew at an impressive rate in 2018 and 2019... its leadership in
the Data Virtualization market is enabling its growth
Source: Gartner Market Share Analysis: Data Integration Worldwide, 2018 (published August 2019)
and 2019 (published April 2020)

18. 18
Customer Satisfaction
Why Customers Choose Denodo
▪ Gartner Peer Insights Customer’s Choice
Award (January 2021)
▪ Both in 2019 and 2020, the only vendor
where 100% of reviewers would
recommend Denodo
▪ 125+ verified reviews with overall score of
4.7 out of 5

19. 19
Spectrum Health (Michigan)
Regional Healthcare System (Hospitals,
Physicians and Plans)
• 170 service sites, including hospitals, urgent care
centers, primary care physician offices, community
clinics, rehabilitation, outpatient facilities and elderly
care.
• Revenue $6.9 billion with 39,000 employees and
volunteers
• Health plan with 1 million members
Primary Challenges
• Integrating multiple analytical data sources quickly
• Reconciling provider data from multiple sources
accurately (business impact)

20. 20
Spectrum Health 1st Project – COVID-19 Dashboard
COMPONENTS:
Tableau, Denodo, Oracle and SQL Server,
10+ other sources
TEAM:
1 Tableau developer, 2 Denodo
developers, 1 Denodo admin
DEVELOPMENT TIME:
• 2 days - Prototype
• 2 weeks – Production*server available
CHALLENGES:
• Very short timeframe
• No formal Data Fabric training
• Understanding performance
optimization (queries from hours to
less than a minute)
“Overall, I felt the team did an amazing job
and the platform did help us deliver value
much quicker than we would have been able
to going the traditional ETL route. It would
have take us at least 6 weeks.”
- Senior Information Architect

21. 21
Data Platform and Regulatory Compliance

22. 22
Speeding Up M&A Integration

23. 23
Speeding Up M&A Integration

24. About BHP
We are a leading global resources company
▪ Our purpose is to bring people and resources together
to build a better world.
▪ Our strategy is to have the best capabilities, best
commodities and best assets, to create long-term value
and high returns.
▪ At BHP, we have a unique perspective on the
extraordinary potential of natural resources to provide
the essential building blocks of progress.
▪ We are among the world’s top producers of major
commodities, including iron ore, metallurgical coal and
copper. We also have substantial interests in oil and gas.
▪ We have a global presence with operations and offices
across Australia, Asia, the UK, Canada, the USA and
Central and South America.
24
Data Virtualization Platform - September 2020

25. Multi-Location Hybrid Data Fabric
25
Problems:
• Repeated engineering effort
• Long lead-times
• Project-centric repositories: duplicate
data everywhere
Brisbane
Perth
Santiago
Houston
Cloud
Tenancy
Data
Lake
Data
Mart
Data
Mart
Analytics
Analytics
Analytics
Data Virtualization Platform - September 2020

26. Reference architecture
26
Data Source
✓ Application data stores
✓ SaaS / Cloud Applications
✓ Application interfaces
✓ Manual data sources
Data Fabric Consumers
✓ Enterprise &
Regional Data
Stores
Self Service Data Catalogue
Query
Optimisation
Query
Development
Data
Federation
Data
Discovery
Abstraction / Semantic Layer
Security Layer
Kerberos Delegation + Encryption in Transit + Extensive Auditing
Secure
Faster
Connect to data stores or direct to source Get access to the right data, fast.
Self service
Flexible protocols
✓ Analytics
✓ Self Service
✓ Business Intelligence
✓ Transactional Applications
✓ Bring your own tool
BHP Data Fabric - September 2020

27. Multi-Location Hybrid Data Fabric
27
Problems:
• Repeated engineering effort
• Long lead-times
• Project-centric repositories: duplicate
data everywhere
Brisbane
Perth
Santiago
Houston
Cloud
Tenancy
Data
Lake
Data
Mart
Data
Mart
Analytics
Analytics
Analytics
Data Virtualization Platform - September 2020

28. Enabling Agile Analytics
and Data Governance with
Data Virtualization
Demostración de producto
Juan González
Líder Técnico
March 2021

29. Revisión del modelo conceptual
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30. Revisión del modelo conceptual
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102. Q&A

103. Q&A
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