This document summarizes a webinar on data virtualization presented by Denodo. It discusses common data challenges faced by data scientists, including spending significant time locating, transforming, and preparing data from various sources. It then introduces data virtualization as a solution, which provides a centralized catalog and logical view of data that reduces the data science workflow from months to days. Examples are given of customers like a healthcare company and industrial real estate company using Denodo's data virtualization platform to more easily discover, access, and analyze their diverse data sources. Key benefits highlighted include increased data and analytics agility, reduced data preparation time, and enabling self-service analytics.

1. DATA VIRTUALIZATION
Packed Lunch Webinar Series
Sessions Covering Key Data Integration
Challenges Solved with Data Virtualization

2. Advanced Analytics and Machine
Learning with Data Virtualization
Inessa Gerber
Director of Product Management

3. Agenda
1. Data Trends and Challenges
2. The Life of the Data Scientist
3. Data Virtualization at a Glance
4. Enabling the Data Scientist
5. Customer Use-Cases
6. Q & A

4. 4
Perspective on Emerging Technology
Are you ready for the data evolution?
Source: 2020 State and Local Tech Forecast, NASCIO, January 22, 2020

5. 5
AI & Advanced Analytics need Trusted Data
Are you ready to embrace the future?
Source: Delivering on Digital Government – Achieving the Promise of Artificial Intelligence Survey 2019, CDG, IBM, and NASCIO, October 1, 2020
Data Organization & Hygiene
do not feel that their state has their data organized in
a manner to be successful with artificial intelligence
today.
Data Assessments
have not completed an assessment of their data to
ensure that it is usable, accessible and cleansed
enough to effectively leverage artificial intelligence.
A Framework for Risk
do not have framework for evaluating risk for
emerging technologies like artificial intelligence.
Policy
do not have a policy governing the responsible and
ethical use of artificial intelligence.
42%
51%
57%
72%
What are the most significant challenges
or barriers to the AI adoption?
Legacy IT infrastructure
Cultural concerns inside the organization
Lack of necessary staff skills for AI
Organizational data silos
Lack of executive support
45%
33%
27%
24%
2%

6. 6
Data, Data, Data…. Where ever you are?
What do organizations have to work with…
Data is available in a vast array of formats & systems.
The Data Science projects need to consume, trust, and
understand the data.
▪ Files (CSV, logs, Parquet)
▪ Relational databases (EDW, operational systems)
▪ NoSQL systems (key-value pairs, document stores,
time series, etc.)
▪ SaaS APIs (Salesforce, Marketo, ServiceNow,
Facebook, Twitter, etc.)
▪ And many more to come…

7. 7
Data, Data, Data…. The Data Scientist is out to get you.
What do organizations have to work with…
Source: Delivering on Digital Government – Achieving the Promise of Artificial Intelligence Survey 2019, CDG, IBM, and NASCIO, October 1, 2020
The Data Science uses many tools, and requires an array of expertise and data knowledge. A complex
and an exciting data journey.

8. The Daily Data Science struggles
Where does the time go?

9. 9
The Data Science Workflow
The daily life of the data scientist
1. Gather the requirements for the business problem
2. Identify and find useful data
3. Transform and Cleanse data
4. Analyze and explore the data
5. Prepare data 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/

10. 10
The Data Science Workflow
1. Gather the requirements for the business problem
2. Identify and find useful data
3. Transform and Cleanse data
4. Analyze and explore the data
5. Prepare data 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/
The daily life of the data scientist

11. 11
The Data Science Workflow
Data scientists spend most of their time looking
for data, analyzing it, and massaging it into the
formats required for the ML/AI processing
The daily life of the data scientist
Source: https://hackernoon.com/the-ai-hierarchy-of-needs-18f111fcc007

12. 12
The Data Science Workflow
New data copies and working in isolation can
lead to incorrect decisions and outdated results,
as well as cost inefficiency.
The daily life of the data scientist
Source: https://hackernoon.com/the-ai-hierarchy-of-needs-18f111fcc007

13. Data Virtualization
What is it and how it enables the Data Scientist?

14. 14
Logical Data Architecture
DATA
VIRTUALIZATION

15. 15
Decoupling IT from the Consumers
IT: Flexible Source Architecture
Business: Flexible Tool
Choice
Business can now
make faster and
more
sophisticated
decisions as all
data accessible
by any tool of
choice
IT can now
move at slower
speed without
affecting the
business

16. 16
Denodo Platform – How does virtualization work?
DATA CATALOG
Discover - Explore - Document
DATA AS A SERVICE
RESTful / OData
GraphQL / GeoJSON
BI Tools Data Science Tools
SQL
CONSUMERS
LOGICAL
DATA
FABRIC
SOURCES
Traditional
DB & DW
150+
data
adapters
Cloud
Stores
Hadoop
& NoSQL OLAP Files Apps Streaming SaaS
U
Customer 360
View
Virtual Data
Mart View
J
Unified
View
Unified
View
Unified
View
Unified
View
A
J
J
Derived
View
Derived
View
J
J
S
Transformation
& Cleansing
Base
View
Base
View
Base
View
Base
View
Base
View
Base
View
Base
View
Abstraction
CONNECT
COMBINE
CONSUME

17. 17
The Data Science Workflow
1. Gather the requirements for the business problem
2. Identify and find useful data
3. Transform and Cleanse data
4. Analyze and explore the data
5. Prepare data 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/
The daily life of the data scientist

18. 18
Data Catalog – Data for all
Centralized, Secure, and Governed access for data discovery, user collaboration, and data services

19. 19
Data Catalog – Data for all
Centralized, Secure, and Governed access for data discovery, user collaboration, and data services
AI driven – Recommendations
& shortcuts to most used datasets.

20. 20
Data Catalog – Navigation
Navigation:
• Smart search
• Content Search
• Context Search
• Filter based selection

21. 21
Data Catalog – Data Lineage
Data Lineage:
• Graphical view
• Detailed information
• User friendly interface

22. 22
Data Catalog – Query Wizard
Query Wizard:
• Drag & Drop
• Query Customization
• Smart auto-complete

23. 23
Denodo Notebook
▪ Based on Apache Zeppelin
▪ Support for SQL queries,
charts, and code in Python,
R, Spark, etc.
▪ Improved multi-user support
▪ Fully integrated with
Denodo’s security system and
SSO capabilities
▪ Accessible from the catalog
Denodo includes preconfigured Data Science Notebook as part of the unified environment

24. Customer Use Cases

25. Problem Solution Results
Case Study
25
Scottish healthcare company uses Denodo to rebrand itself as a
health and wellness company and helps the government to identify
places where healthcare benefits and resources were not distributed
consistently in sync with the rest of the country.
• The company had more than a thousand point to point
data sources and was looking for increased agility in
terms of making more data available for research
purposes by integrating sources from other public sector
companies to its central data warehouse.
• Given the high number of individual data sources that fed
the central Datawarehouse, the company was looking for
a solution which would reduce the data sprawl by
allowing them to connect to more sources reducing the
need for data replication.
• The company wanted to start doing predictive and
prescriptive analytics on its data by providing the
relevant data in real time to its team of data scientists.
The company used Denodo to
• Architect a logical Datawarehouse to
connect its central Datawarehouse with
all its sources including the data lake with
unstructured data.
• Create a single, secure, governed and
audited point of entry for all data.
• Connect any analysis tool such as
Tableau, BO, Qlik, SPSS and also make
the data available to its data science
team.
In two years after going live with DV the
company implemented 12 new healthcare
projects in production environments and has
another 12 in the pipeline. DV was used to
• Create a national comparative reporting
on chemotherapy data.
• Identify a core group of people who need
to be evacuated in times of emergency.
• Combine the data from NHS and alcohol
and drug commission data to identify the
efficacy of intervention services provided.
NHS Scotland, is the publicly funded healthcare system in Scotland. It operates 14
territorial NHS Boards across Scotland, seven special non-geographic health boards
and NHS Health Scotland.

26. 26
Current Architecture
NSS used the Denodo platform to:
▪ Architect a Logical Data Warehouse. LDW connects its
central Data warehouse with all its sources including the
data lake with unstructured data.
▪ Create a single, secure, governed and audited point of
entry for all data.
▪ Connect any analysis tool such as Tableau, BO, Qlik, SPSS
and also make the data available to its data science team.

27. Problem Solution Results
Case Study
27
Global industrial real estate company creates a new data architecture
and successfully launches data analytics program for cost
optimization.
• Create a single governed data access layer to create
reusable and consistent analytical assets that could
be used by the rest of the business teams to run
their own analytics.
• Save time for data scientists in finding ,
transforming and analyzing data sets without
having to learn new skills and create on data
models that could be refreshed on demand.
• Efficiently maintain its new data architecture with
minimum downtime and configuration
management.
• Denodo was used to create a logical data
warehouse that made the data available for
analytics. Data Catalog feature used by data
scientists to find the data easily.
• Denodo was used to leverage the microservices
architecture to push enterprise data into the data
modals and then get the result set back into
Denodo to make them available for consumption.
• Terraform used to script a lot of configurations that
were running Denodo. CICD pipeline used to
automate the Denodo configuration backup.
• The analytics team was able to create business
focused subject areas with consistent data sets that
were 30% faster in speed to analytics.
• Denodo made it possible for Prologis to quick start
advanced analytics projects.
• Denodo deployment was as easy as a click of a
button with centralized configuration management
and easy to upgrade and scale up and down
according to the workload.
Prologis is the largest industrial real estate company in the world, serving 5000 customers in over 20 countries and
USD 87 billion in assets under management. Prologis was ranked the top U.S.company and sixth overall among the
2019 Global 100 Most Sustainable Corporations in the World at the World Economic Forum in Davos.

28. 28
Current Architecture
▪ Denodo was used to leverage the microservices architecture to push enterprise data into the data models,
process it and then get the result set back into Denodo to make them available for consumption
▪ Denodo made it possible for Data Scientists to quick start advanced analytics projects.
▪ Allowed Data Scientists to use native language closest to them

29. 29
Key Takeaways
▪ Denodo plays a key role in the Data Science ecosystem by
reducing data exploration and analysis timeframes. It
enables the Data Scientist and drives data insights.
▪ Data Virtualization broadens the data usage, by making
it accessible to different personas in your organization in
the most familiar form.
▪ Denodo fosters self-service and data sharing culture
with flexible and scalable architecture for business and IT.
▪ Denodo uses AI internally within the product offering,
making it a smart solution for the data fabric.

30. Q&A

31. 31
Next Steps
Access Denodo Platform in the Cloud!
Start your Free Trial today!
www.denodo.com/free-trials
GET STARTED TODAY

32. Thanks!
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