THE INDUSTRY'S FIRST VIRTUAL EVENT IN ROMANIA - Why Data Virtualization is a Game Changer in Data Management

Why Data Virtualization
is a Game Changer in
Data Management
9th September 2020

Speakers
Rick F. van der Lans
Industry Analyst, R20
Alberto Pan
CTO, Denodo
Calin Lupsan
Founder & CEO, Intelligence

Agenda
1. Welcome and Opening Remarks – Calin Lupsan, Intelligence
2. Why Has Data Virtualization Revolutionized Data and
Application Integration – Rick F. van der Lans, R20
3. Enabling Agile Analytics and Digital Transformation with a
Enterprise-Wide Data Fabric – Alberto Pan, Denodo
4. Q&A

WE SOLVE YOUR TECHNOLOGY PAIN
Calin Lupsan
Founder & CEO, Intelligence

Copyright © 2020 R20/Consultancy B.V., The Netherlands. All rights
reserved. No part of this material may be reproduced, stored in a
retrieval system, or transmitted in any form or by any means,
electronic, mechanical, photographic, or otherwise, without the explicit
written permission of the copyright owners.
Why Has Data Virtualization
Revolutionized Data and
Application Integration
Rick F. van der Lans
Industry analyst
Email rick@r20.nl
Twitter @rick_vanderlans
www.r20.nl

Data hasn’t changed,
it’s just more of the same

Data usage has changed
Self-service BI
Embedded BI
Supplier- and Customer-driven BI
Applied AI in Text, Image, Video Analysis
Edge Analytics
Data Marketplace
Data Science
Automated decisions
…

Photo: Alex Iby
ETL ETLETL
Source
systems
Data martsStaging
area
Analytics &
reporting
Data
warehouse
The Classic Data Warehouse Architecture

Yesterday: Data Warehouse and Data Usage
Developers
IT specialists
Development Styles
Pre-programmed, auditable,
governable, formally tested
Report Types
Batch and online business
reports
Consumers
Business users
Legislators

Today & Tomorrow: Data Warehouse and Data Usage
Developers
IT specialists
Business Users
Development Styles
Pre-programmed, auditable,
governable, formally tested
Self-service, investigative
Pre-programmed
Self-service, investigative
Report Types
Batch and online business
reports
Customer-facing apps
Ad-hoc reports
Simple data retrieval
Ad-hoc reports
Data mining, statistics
Dark data analysis
Consumers
Business users
Legislators
External parties
Consumers
Business users
Business users
Business users
Data scientists
Business users and IT
Streaming analytics Business users, machines

Data
Processing
Specifications
Source
systems Analytics & reporting
Data Processing Specifications
Data structure specifications
Integration specifications
Transformation specifications
Data security specifications
Data cleansing specifications
Analytical specifications
Visualization specifications
Data privacy specifications

Data Processing Specifications and the
Classic Data Warehouse Architecture
ETL ETLETL
Source
systems
Data martsStaging
area
Analytics &
reporting
Data
warehouse

Data Virtualization to the Rescue

Data Virtualization Overview
production
application website
analytics
& reporting
mobile
App
internal
portal dashboard
Data Virtualization Server
SQL
databases
streaming
databases
social
media data
Hadoop,
NoSQL
databaseESB
messaging
unstructured
datalegacy
database
cloud
applications
private
data
applications

Amplifiers

DataVirtualizationServer
Virtual table pointing to source
Virtual table:
May contain row selections, column selections,
column concatenations, transformations,
column and table name changes, groupings,
aggregations, data cleansing, …
Data consumer
Developing Virtual Tables
Source

Layers of Virtual Tables
Enterprise data layer
Data consumption
layer
Data source
layer

Caching to Mimimize Access of Data Stores
Virtual table
with cache
Virtual table
without cache
Data source Data source

Different Users Accessing Different Virtual Layers
Reporting Data scienceSelf-service BI
Data consumption
layer
Source data layer

Evolutionary Development Approach
Canonical
Data model
Views for Data
Access
Imported
Data

Use Case 1: The Logical Data Warehouse Architecture
ETLETL
Source
systems
Staging area
Analytics &
reporting
Data warehouse
Other
data sources
Logical Data Warehouse Architecture
DataVirtualization
Big data
RepositoryMaster data

Use Case 2: Self-Service BI
Self-Service Reporting
Self-Service Analytics
Self-Service ETL
Self-Service Data preparation
Self-Service …

Heading for an Integration Labyrinth
Self-service
BI reports
Data processing
specifications
Data sources

One “Universal Semantic Layer”
Self-service
BI reports
Data processing
specifications
Data sources
Data
Virtualization
Server

Layers of Virtual Tables
Data consumption
layer
Data source
layer

Use Case 3: The Data Lake
Data sources
Investigative
analytics
ET
Data lake
ETL
ETL
ETL
Data science
ET
Photo: Chris Gallimore

Challenges of a Physical Data Lake
Big data too big to move
• Too slow to copy and bandwidth issues
Complex “T” moved to data usage
Company politics
Data privacy and protection regulations
Data in data lake is stored outside original security
realm
Metadata to describe data
Some sources are hard to copy
• For example, mainframe data
Refreshing of data lake
Management of data lake required
…
Data lake

The Logical (Virtual) Data Lake
Data sources
ETL ETL Cached Cached
The Logical Data Lake
Data Scientists

Bottom Layer is the Logical Data Lake
Data consumption
layer
Logical
Data
lake

Use Case 4: Big Data?
?
ETL ETLETL
Source
systems
Data martsStaging
area
Analytics &
reporting
Big data
ETL ETL ETL
Data
warehouse
?

Data Virtualization Makes Access to Big Data Easy
HDFS
Data Virtualization Server
MongoDB Cassandra SQL

Use Case 5: Cloud Integration
Business users
DataVirtualization
On premise
data sources
Cloud-based
data sources

www.lulu.com

Enabling Agile Analytics
and Digital Transformation
with a Enterprise-Wide
Data Fabric
Free your Data
Alberto Pan
CTO
September 2020

Agenda
1. Data Virtualization: Market Momentum
2. Denodo Vision: Enterprise Data Fabric
3. Case Studies
4. Q&A

40
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

42
Gartner Gives DV its Highest Maturity Rating
“Data Virtualization
can be deployed
with low risk and
effort to achieve
maximum value.”

43
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)

44
Customer Satisfaction
Why Customers Choose Denodo
▪ Gartner Peer Insights “Voice of the
Customer” (Jan 2019, Jan 2020)
▪ 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

Enterprise Data Fabric: Automate
Data Delivery

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”

47
Denodo’s Logical Data Fabric Enables Information Self-Service
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

48
Gartner Data Fabric
Data Fabric Net
Compounds Customers Products Claims
RDBMS/OLTP Traditional Analytics/BI Data Lakes Cloud Data Stores Apps and Document
Repositories
Flat Files
Third Party
Legacy
Mart
Data Warehouse
Mart
ETL ETL
XML • JSON • PDF
DOC • WEB
▪ A data fabric is an architecture pattern for the delivery of data objects regardless of deployment platforms and
data location (hybrid, multi-cloud).
▪ It utilizes AI/ML to provide actionable insights and recommendations.
▪ This results in faster and, in some cases, completely automated data access and sharing
▪ Supports both analytics and services orchestration, with integrated governance and security

Agile Analytics: Spectrum Health fights the
COVID-19 Pandemics

51
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)

52
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 Denodo 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

Regulatory Compliance with a Data Fabric:
CIT Group

54
Data Platform – Large Commercial Bank
• CIT Group: Large commercial bank grew through acquisitions
• One West Bank, Direct Capital Corporation (DCC)
• Breached SIFI threshold in 2013
• ‘Too big to fail’ financial institution
• Subjected to more scrutiny from federal regulators
• Participate in CCAR (‘stress tests’)
• Needs to provide a complete view of risk across complete organization
• Market, credit, third-party, …
• Used Data Virtualization to expose data to downstream applications and reporting

55
Data Platform and Regulatory Compliance

56
Speeding Up M&A Integration

57
Speeding Up M&A Integration

Expanding the Data Fabric: Biggest
Semiconductors Vendor

59
Single Project to Start Their Journey
DV as HR Services Layer
• Single point of entry for HR data consumption
• Scalable to on-premise and cloud data sources
• Seamless support for data source migrations
HR IT’s Worker Capability Migration:
• HR IT recently migrated and consolidated their HR
application layer and moved to consolidated data
warehouse environment.
• As an early adopter of data virtualization, HR IT was
able to easily repoint their business views/interfaces
to the new integrated views, preserving their logical
layer and preventing service disruption due to the
migration.
• Data virtualization has also allowed HR IT to easily
integrate cloud applications to fill the gaps in its
services portfolio.
HR DW1 HR DW2 HR DW3
Worker Business View
HR DW4
BaseViewBaseViewBaseViewBaseView
Int. ViewIntegrated View
HR Apps HR Apps HR Apps New HR App
HR Data Consumers

60
Expanding the Vision
DV as Digital Transformation Accelerator
• Fast data integration
• Easy transformation and mapping
• Ensure consistency with internal glossaries
• Flexible output channels
Federated approach:
• Central team manages the platform, ensures performance
and sets release guidelines
• “Stewards” team provides access to commonly used virtual
views
• Independent teams in every department / LOB create their
own views from common + specific views
• Unified security and governance layer for all data
consuming applications (human and apps)
M&A HR DW
MD Mapping Table
HR Data
Denodo VDP
SvcManagementDB Worker DB
HR DW
M&A Worker View
Intel Worker View
Intel Departements
Intel Worker LocationM&A Translator
CompanyCd Mapping
CostCenterMapping
M&A CC Extract
M&A Cost Ctr Detail
Intel Directory
Users
Groups
iPaaS
Worker Orchestration
ICAPP SQL DBaaS
Working Storage
24 HourTrigger
ICAPP PaaS
ID Reconcilliation
User Driven UI

61
Rapid Enterprise-wide Deployment
61
• 2013 – Initial purchase for HR project
• 2016 – 3 year ELA; multiple projects
• 2013 – <10 data sources, single server
• 2019 – 260+ data sources, 128 core in
production across multiple data centers
• 2013 – Single project team
• 2019 – Intel DV CoE guiding
18/26 BU’s in DV Project Use
• 2013 – 10 DV trained staff
• 2019 – 800+ DV trained staff

62
Benefits of Denodo
Value Driver Metric Goal Actual
Time to Develop Time to develop data service in days 50% 90%
Time to Deploy Time to Deploy data service in days 50% 90%
TTM Overall time it takes to make data service
available for use
60% 90%
Time to Engage Time it takes for business to engage with IT 75% 75%
Performance Performance of data services 50% 60%
Impact Analysis How fast can we perform impact analysis 50% 90%
Enterprise Architectural Alignment Ease at which data from disparate sources can
be integrated
Security, data classification High

Agile BigData Analytics and Single
Source of Truth with Denodo: Visa

Problem Solution Results
Case Study
64
Visa accelerates reporting and analytics time-to-market
using data virtualization
Visa is the worlds 2nd largest card payment organization facilitating Visa branded credit and debit cards.
With it’s 8000 worldwide employees, Visa earns $10B in yearly revenue and is headquartered in Foster
City, California. Visa’s global network processes $6.5 trillion or 100B transactions a year.Industry: Financial Services
▪ Visa’s revenue and pricing business unit was
looking for an agile data integration solution to
easily onboard new data sources, as
heterogeneous data proliferated throughout
Visa.
▪ Because of growing volume and complexity of
data, they also wanted a solution that can
provide unified view of enterprise data with
higher performance and scalability.
▪ Visa wanted a solution with low TCO, higher
flexibility and faster time-to-market, to provide
relevant information to business users.
▪ Visa deployed Denodo Platform for data
virtualization to virtually integrate data from
disparate sources, and restructure them to
meet the need without data replication.
▪ Visa wanted to leverage their existing DW, data
marts and data lake for historical data, while
providing real-time-access to information with
data virtualization.
▪ Reduced reporting and analytical turnaround
time by as much as 90% (from 3-15 months to
0,5-3 months on average)
▪ Provided 10x faster turnaround time for
strategic and operational intelligence, while
enhancing information with newer sources of
data.
▪ Increased efficiency in information
management practices, through better data
quality, data governance, data security and data
lineage.

65
II. Business Problem
Revenue & Pricing: Competitiveness | Analytics on Data Lake: Faster Time-to-market
A. Previous Solution:
▪ Physical data marts materialized
▪ Microstrategy on top containing all the semantics and reporting logic
▪ 30K reports SQL-based semantic layer built-into tool – “nightmare to manage”
B. Needs:
▪ Faster response to new information needs
▪ Manage Semantic Layer at Enterprise Level instead of Tool Level - reuse
▪ Integrate Heavy Analytics on 5 PB of data stored in data lake (Hadoop, DB2, Hive)
▪ Single version of truth needed – too many competing views / sources within Visa

66
Credit Cards Company – Reporting & Data Monetization
Project goals:
• Improve data reliability by defining certified data
(sources and metrics) in a logical layer
• Simplify business self-service – hide complexity of back-
end (complex snowflake schema, DB2/Hive dichotomy)
• Lower back-end data cost (move cold data to Hive)
Solution: DV / Denodo Layer above analytical systems:
• Business metadata to document, tag & classify datasets
• Easy-to-use business models across entire domain
• Complex models w/ 150+ joins and 1000s of columns
• Massive data sets with petabytes of information
• Reduced turnaround time by as much as 90% (from 3-
15 months to 0,5-3 months on average)

Thanks!
www.denodo.com info@denodo.com
© Copyright Denodo Technologies. All rights reserved
Unless otherwise specified, no part of this PDF file may be reproduced or utilized in any for or by any means, electronic or mechanical, including photocopying and microfilm,
without prior the written authorization from Denodo Technologies.
www.intelactsoft.com office@intelactsoft.com

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