Watch full webinar here: https://bit.ly/2EpHGyd Presented at Data Champions, Online Asia 2020 Businesses and individuals around the world are experiencing the impact of a global pandemic. With many workers and potential shoppers still sequestered, COVID-19 is proving to have a momentous impact on the global economy. Regardless of the current situation and post-pandemic era, real-time data becomes even more critical to healthcare practitioners, business owners, government officials, and the public at large where holistic and timely information are important to make quick decisions. It enables doctors to make quick decisions about where to focus the care, business owners to alter production schedules to meet the demand, government agencies to contain the epidemic, and the public to be informed about prevention. In this on-demand session, you will learn about the capabilities of data virtualization as a modern data integration technique and how can organisations: - Rapidly unify information from disparate data sources to make accurate decisions and analyse data in real-time - Build a single engine for security that provides audit and control by geographies - Accelerate delivery of insights from your advanced analytics project

1. Data Virtualization:
A key to real-time insights in a
post-COVID world
Chris Day
Director
APAC Sales Engineering
cday@denodo.com
Data Champions, Online
11 June 2020

2. Photo by John
Cameron on Unsplash

3. Photo by Mick
Haupt on Unsplash

4. Photo
by Allie on Unsplash
• Which Product or Brand?
• Available Resources?
• Supply & Delivery Network?

5. Business needs
Speed &
Agility

6. 6
Current Requirements in Data Management
1. Faster & more accurate decision making
▪ Significant increase in business speed & complexity of
requirements
2. Regulations, enterprise-wide governance & data security
▪ Thousand of new regulations worldwide: tax, finance, privacy, HR,
environmental, GDPR, etc.
3. IT cost reduction
▪ Huge data growth with associated storage and operational costs

7. 7
Challenges: Fragmentation of the Data Landscape
ETL
Data Warehouse
Kafka
Physical Data
Lake
ML/AI
SQL
interfac
e
IT Storage and Processing
Streami
ng
Analytic
s
Distributed Storage
Files
Bus. Tools, Ent. Apps,
Portals, Mobile…
Gov/S
ec
Gov/Sec
Gov/
Sec
G
o
v
/
S
e
c
Gov/Sec
Gov/Sec
Gov/Sec
Gov/SecGov/SecGov/SecGov/Sec
Bus.LogicBus.LogicBus.LogicBus.Logic
IT has to
implement Gov.
& Sec. at every
data source Bus. adds Data Logic in
every report, tool, etc.

8. 8
Modern Data Architecture

9. 9
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
2010s2000s
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

10. 10
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
2010s2000s
Post EDW
Time
LDW
Operational
Application
Operational
Application
Operational
Application
Data
Warehouse
Data
Warehouse
Data
Lake
?
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
Operational Application
Operational Application
Operational Application
IoT Data
Other NewData
Logical Data
Warehouse
Data Warehouse
Data Lake
Marts
ODS
Staging/Ingest
Data
Virtualization
√ Improved Time to Market by 50 to 90%
√ Improved Report Consistency
√ Reduce Duplication of Data
√ Improve Transparency
√ Reduced development Cost
√ Future Proof the architecture against
technology changes

11. 11
Gartner – Logical Data Warehouse
“Adopt the Logical Data Warehouse Architecture to Meet Your Modern Analytical Needs”. Henry Cook, Gartner April 2018
DATA VIRTUALIZATION

12. Gartner, Adopt the Logical Data Warehouse Architecture to Meet Your Modern Analytical Needs, May 2018
“When designed properly, Data Virtualization can speed data integration, lower
data latency, offer flexibility and reuse, and reduce data sprawl across
dispersed data sources.
Due to its many benefits, Data Virtualization is often the first step for organizations
evolving a traditional, repository-style data warehouse into a Logical Architecture”

13. 13
Modern, Agile Data Architecture
Abstracts access to disparate
data sources
Acts as a single repository
(virtual)
Makes data available in
real-time to consumers
DATA VIRTUALIZATION

14. 14
Data Virtualization Use Cases
AGILE BI
• Real-Time Dashboards
• Self-Service BI/Analytics
• Operational Analytics
• Virtual Data Marts
LOGICAL DW/DL
• Logical Data Warehouse
• Logical Data Lake
• DWH Offloading
• Big Data/Advanced Analytics
CLOUD SOLUTIONS
• Cloud BI Analytics
• DS for Cloud Apps
• Cloud Modernization
• Hybrid Data Fabric
DATA SOLUTIONS
• Data Services
• DS for Digital Apps
• DS for SVC/MDM Apps
• Application Migration

15. 15
Quiz
Where does your organisation store it’s data?
1. ‘In the cloud’
2. On-premise
3. Both ‘in the cloud’ and on-premise
4. Don’t know
Quiz number 1

16. CHALLENGE 1
Unify information from disparate
sources to make accurate decisions &
analyse data in real-time

17. 17
Customer Case Study - FESTO
• Founded 1925
• Annual revenues (FY
2018) €3.2 B
• Over 21,000
employees
• Headquarters in
Germany
• World´s leading
supplier of
automation
technology and
technical education.
BUSINESS NEED
• Optimize operational efficiency, automate manufacturing processes, and deliver on-
demand services to business consumers
• Find smarter ways to aggregate and analyze data
• An agile solution that enables the monetization of customer-facing data products
• Free business users from IT reliance to become self-sufficient with reporting and
analysis
THE CHALLENGE:
Find an agile way to integrate data from existing silos, including data
warehouse, machine data, and others, that will reduce dependencies
from business users on IT and provides quick turnaround and flexibility.

18. 18
Customer Case Study - FESTO
SOLUTION:
• Festo developed a Big Data
Analytics Framework to
provide a data marketplace to
better support the business
• Using the Denodo Platform to
integrate data from numerous
on-prem and cloud systems in
real-time
• A unified layer for consistent
data access and governance
across different data silos

19. 19
FESTO – Digital Transformation

20. 20
Quiz
How many locations are you storing your data?
1. One and only one
2. 2-5
3. More than 5
4. Don’t know
Quiz number 2

21. CHALLENGE 2
Build a single engine for security that
provides audit & control by geographies

22. 22
How does Data Virtualization support Compliance Needs?
Unified data delivery layer
that supplies every data
consumer with data:
No siloed delivery!
Therefore processes and
procedures regarding
regulations need to govern only
one information delivery layer.

23. 23
How does Denodo address Security?
Authentication
• Pass-through authentication
• Service accounts
Authentication
• User/password
• Kerberos and Windows SSO
• Web Service security: SAML, OAuth, SPNEGO
LDAP
Active Directory
Role based Authentication
Guest, employee, corporate
Schema-wide Permissions
Data Specific Permissions
(Row, Column level, Masking)
Policy Based Security
Data in motion
• TLSv1.2
Data in motion
• TLS v1.2
Encrypted
data at rest
• Cache
• Swap

24. 24
Customer Case Study - Asurion
• 290 million
consumers
• Annual revenues (FY
2016) $5.8 B
• Over 17,000
employees
• 49 Offices, 18
Countries
• Insurance &
Warranties on
digital devices
BUSINESS NEED
• Reduce time to create new services and products from months to weeks.
• Meet strict restrictions on migrating data out of countries of origin.
• Centralize companywide security management around a single point of control.
THE CHALLENGE:
Expand their data architecture to cope with global growth, while
exceeding the expectations of the customers.

25. 25
Asurion – Digital Transformation
SOLUTION:
• Asurion developed a hybrid
data layer across the cloud &
on-premise data.
• A single point of access to the
data ensuring security
compliance.
• Removed complexities of data
access from the consumers,
enabling better integration &
improved analtyics

26. CHALLENGE 3
Accelerate delivery of insights from
your advanced analytics projects

27. 27
The Data Scientist Workflow
A typical workflow for a data scientist is:
1. Gather the requirements for the business problem
2. Identify useful data
▪ Ingest data
3. Cleanse data into a useful format
4. Analyze data
5. Prepare input for your algorithms
6. Execute data science algorithms (ML, AI, etc.)
▪ Iterate steps 2 to 6 until valuable insights are produced
7. Visualize and share
Source:
http://sudeep.co/data-science/Understanding-the-Data-Science-Lifecycle/

28. 28
The Data Scientist Workflow
Source:
http://sudeep.co/data-science/Understanding-the-Data-Science-
Lifecycle/
A typical workflow for a data scientist is:
1. Gather the requirements for the business problem
2. Identify useful data
▪ Ingest data
3. Cleanse data into a useful format
4. Analyze data
5. Prepare input for your algorithms
6. Execute data science algorithms (ML, AI, etc.)
▪ Iterate steps 2 to 6 until valuable insights are produced
7. Visualize and share

29. 29
Customer Case Study - McCormick
• Founded 1889
• Annual revenues (FY
2017) $4.8 B
• Over 11,000
employees
• Joint ventures
across the world
• Multiple Brands
• Consumer &
Commercial
Products
BUSINESS NEED
• Unify disparate sources of data for machine learning use
• Make insights immediately available to the business users
• Provide flexibility to add and remove sources of data
• Increase collaboration through unified data catalog
THE CHALLENGE:
McCormick wanted to operationalize machine learning & evolve their
enterprise data services to broaden scope to business users and
support their digital transformation.

30. 30
• Multiple Brands
• Quality Improvement
• Which Data to Use?

31. 31
McCormick – Benefits of Logical Architecture
• Agile Data Delivery
• High Level of
Reuse
• Single Discovery &
Consumption
Platform

32. 32
Denodo’s Coronavirus Data Portal
File
Denodo Exp
ress
COVID-19
Edition
Data
Catalo
g
Data
Portal
JDBC
ODBC
API
GraphQL
GeoJSON
Sandb
ox Sandb
ox Sandb
ox

33. 33

34. 34
The Architecture
Sources
2. Combine
Combine,
Transform
&
Semantics
3. Consume
1. Connect
Consuming Applications
4.Dev/Ops

35. 35
Current Requirements in Data Management
1. Faster & more accurate decision making
▪ Data Virtualization – Single platform for all enterprise data
2. Regulations, enterprise-wide governance & data security
▪ Data Virtualization – Unified metadata management for
governance and security
3. IT cost reduction
▪ Data Virtualization – Minimise data management infrastructure

36. Data Virtualization:
1. Provides a single data platform, reducing risk &
increasing collaboration
2. Unifies disparate data sources in real-time
3. Supports self-service & data discovery
4. Centralises governance & security of enterprise
data assets
WA
YS

37. Q&A

38. 38
Next Steps
Access Denodo Platform in the Cloud!
Take a Test Drive today!
www.denodo.com/TestDrive
GET STARTED TODAY

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