This document summarizes a webinar on data as a service. It discusses how data virtualization through Denodo can enable agile business intelligence by providing pre-aggregated data to users quickly. It describes how Denodo creates API access to data, allows for an enterprise data marketplace, and integrates machine learning models to power operational AI. A demonstration of a personal COVID-19 risk monitor is provided.

1. M IDDLE EA ST WEBINA RS
Three Dimensions of Data as a
Service
Alexey Sidorov
Data Management Director
MEA, Eastern Europe, Russia

2. 2
Agenda
1. Data as a Service
2. API Management / Data Monetization
3. Enterprise Data Marketplace
4. Operational AI / ML
5. Demonstration
6. Q&A

3. 3
Data as a Service
Foundation for Agile BI

4. 4
Data Challenges of Agile BI
Get the right data to the right person as fast as possible?
§ 20% of data has errors in it
§ 50% of data is inconsistent
§ It typically takes 7 days to get data to the end user
§ It isn't possible to do a cross-database
§ query on 70% of company data
§ 65% of the time, executives don't receive the data they need
§ 60% of the time, users can't do immediate online analysis
§ 75% of new key information sources are not passed on to users within the year
“What Agile Business Intelligence Really Means”, Wayne Kernochan

5. 5
Data as a Service
Building Blocks of Digital Transformation
• Data as a Service is a service that provides pre-aggregated and pre-calculated
analytical data – giving insights into key business metrics – to facilitate quicker and
better informed decisions.
• The traditional, static data from RDBMS, files, Data Lakes, NoSQL etc. has to be
integrated with the real-time data coming from:
• Message Queues (for example Kafka)
• Existing application APIs and Microservices
• Trained AI models and neuron networks

6. 6
API Management / Data Monetization

7. 7
Denodo as API Provider & Gateway
Denodo provides one-click, zero development REST
web services on top of any data model with full-fledge
capabilities:
§ Support XML, JSON, GEO-JSON, RSS and HTML
§ Support for hierarchical structures
§ Authentication with basic HTTP, Kerberos, OAuth 2.0
and SAML
§ Self documented with OpenAPI
§ Available in REST, OData, and GraphQL formats

8. 8
API Management / Data Monetization
Reference Architecture
Business Views
Standardized Views
Base Views
Security
DataGovernance
Query
Optimization
Self-Service
Discovery
DATA SOURCES
DATA CONSUMERS
API Management Layer
ODBC, JDBC
Rest, OData, GraphQL
DATA VIRTUALIZATION
Abstraction Layer
Unified Virtual Views
DISPARATE DATA
Any Format
At Any Speed
Reporting and
Analytics
API Management
Pricing
Billing
Collection
App APIs
API Usage
Internal and External Apps

9. 9
Enterprise Wide Data Access Layer
§ Intel accelerated the time of its
average services deployments from
180 hours to 8 hours.
§ Time-to-market (TTM) for data
delivery to business users has been
reduced by 90%
§ Post-merger data integration has
been greatly accelerated.

10. 11
Enterprise Data Marketplace

11. 12
Enterprise Data Marketplace
Enabling Agile BI and DataOps
Enterprise Data Access Layer where users can ‘subscribe’ to data via the
marketplace
§ Provide secure and managed access to data across the enterprise
§ Hide complexity, format, and location of actual data sources
§ Provide a ‘search and subscribe’ mechanism to allow users to find and access to
data services
Supports all data consumers, allowing them to search, request access to and
utilize data assets within a centralized marketplace.

12. 13
Enterprise Data Marketplace
Reference Architecture
Virtual Analytical Data Sets
Virtual Core Model
Base Views
Security
DataGovernance
Query
Optimization
DATA SOURCES
DATA CONSUMERS
ODBC, JDBC
Rest, OData, GraphQL
DATA VIRTUALIZATION
Abstraction Layer
Unified Virtual Views
DISPARATE DATA
Any Format
At Any Speed
Portals and
Dashboards
Reporting and
Analytics
Data Marketplace

13. 14
Enterprise Data Marketplace
§ Guardian was able to establish a data
services marketplace that simplified
companywide data access.
§ Stakeholders were easily able to find
and access any data asset owned by the
company, no matter where it was
stored.
§ Streamlined vital business processes
across the company.
Implementation Example

14. 15
Operational AI / ML

15. 16
Operational AI / ML
Operational AI: Anytime, Anywhere
§ Operational artificial intelligence, or operational AI, is a type of intelligent
system designed for real-world applications at commercial scale.
§ Applications include text analytics, advanced analytics, machine learning
algorithms tightly integrated with the routine usage of a business.
§ Practically, any AI/ML routine (e.g. python script that contains the ML
model/algorithm) can be exposed as a web service connected to Data
Virtualization platform and combined with the other data to provide deep
operational insights, predictions, and alerting. Dashboards and reports can
also access the ML model via Denodo.

16. 17
Operational AI / ML
Reference Architecture
Business Views
AI insights-enriched Views
Base Views
Security
DataGovernance
Query
Optimization
Real Time
Alerting and Dashboards
Reporting and
Analytics
DATA CONSUMERS
ODBC, JDBC
Rest, OData, GraphQL
DATA VIRTUALIZATION
Abstraction Layer
Unified Virtual Views
DISPARATE DATA
Any Format
At Any Speed
DATA SOURCESAI/ML Output

17. 18
Operationalization of Model Scoring
Prologis: Data Science Workflow
Occupancy
Starter Pack
Leasing
Starter Pack
Valuations
Starter Pack
§ The Denodo Platform acts as an intelligent data-access layer between data
sources and data consumers, abstracting data consumers from the
complexities of access.

18. 19
Demonstration

19. 20
Personal Covid-19 risk monitor
§ My Goal:
§ Real-time monitoring of my Covid-19 risk
§ Steps:
§ Get real-time data of the body temperature and saturation sensors from my smart
fitness wrist band
§ Integrate the real-time data with the information about the age and health history
§ Create the risk estimation algorithm based on those data
§ Visualize the results

20. 21
Personal Covid-19 risk monitor
Risk Model
General Information Health History Vaccination History
Smart Fitness band
with the temperature and
saturation sensors

21. 22
Summary

22. 23
Data Virtualization for DaaS
§ Data Virtualization allows you to quickly create enriched ‘purpose-built’ analytics
(Data as a Service) providing insights on key business metrics for decision makers
§ Results in quicker and better informed decisions
§ Data can come from internal and external data sources
§ No need to copy external data – retrieve directly from source
§ External can enrich the internal data provide information to decision makers
§ Data silos (and organization boundaries) can be broken down to provide best
available information to drive business decisions

23. Q&A

24. Customers
800+ customers
Many F500 & G2000
Head quarters : A Coruna Spain and Palo-Alto
California
Offices: Paris, Munich, London, Madrid, Dubai,
Riyahd …
Denodo
20 years of experience in Data Virtualisation
Recognized as the leader by independent analysts
(Forrester, Gartner)
Many IT Industry rewards and nominations
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25. Thanks!
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