The document discusses data virtualization and its role in addressing the complexities of modern IT architecture for improved data accessibility and real-time decision-making. It highlights the inefficiencies of current data management practices and the potential revenue increase from better data usability, alongside benefits such as reduced service response times and enhanced predictive maintenance in IoT applications. By adopting logical data architectures, businesses can achieve significant cost reductions and scalability improvements while adapting to fast-evolving data landscapes.

6. Data Virtualization is
Changing the Game
Company & Solution Overview
Emiliano Pierdominici,
Partner & Channel Sales Director
South Europe & Middle East

7. Why Data Virtualization?

8. 3
Digital Transformation
“Technology changes, economic laws do not”
Information Rules - C. Shapiro, H. Varian

9. 4
IT – Business Dilemma… Growing Complexity
IT Architecture is Unmanageable & Brittle because:
No One Focuses on Data Delivery
– So create 100’s to 1K’s of brittle direct connections and
replicate large volumes of data
Inventory System
(MS SQL Server)
Product Catalog
(Web Service -SOAP)
BI / Reporting
JDBC, ODBC,
ADO .NET
Web / Mobile
WS – REST JSON,
XML, HTML, RSS
Log files
(.txt/.log files)
CRM
(MySQL)
Billing System
(Web Service - Rest)
ETL
Portals
JSR168 / 286,
Ms Web Parts
SOA, Middleware,
Enterprise Apps
WS – SOAP
Java API
Customer Voice
(Internet, Unstruc)
IT Slowing Down
By2020
▪ 500% growth in Data &
Device Avalanche
▪ Due to lack of data
accessibility today
< 0.5% of all data is
ever analyzed and used
Source:
Business Speeding Up
To remain competitive,
by 2020, Business
Decision Speed &
Analysis Sophistication
Requires 300% Increase
Source:

10. 5
Companies Data Value Still Not Used
2 B $ annually would be the average
revenue boost to Fortune 1000 companies
from increasing data’s usability by just
10%, according to research at the
University of Texas

11. 6
Logical Data Integration: the Path to the Future
Adopt the Logical Data Warehouse Architecture to Meet Your
Modern Analytical Needs”. Henry Cook, Gartner April 2018

12. 7
What is a Logical Data Architecture?
Infrastructure Platforms
Source
Source
Source
Source
Source
Source
Access/Virtualization/Federation
Infrastructure Platforms
Ingest: ETL, ELT, Replication, Streaming, Business Rules
LDW Noncore
Systems
Legacy MI
Remote and Third-
Party Systems
ODS
Platform
ODS
MDM
Traditional Data Warehouse
Platforms
Data Warehouse(s)
Data Marts
Sandboxes
Nonrelational Platforms
Data Lake(s)
Sandboxes
Reporting
Accelerators
Self-Service Reporting/
Data Sourcing
APIs
Operational
Reporting
Data Science, AI, Machine
Learning, Statistical,
Predictive
Query and Reporting
Standard & Ad Hoc
Metadata/Model,
Governance,
Change
Control,
Security,
Serviceability,
Reliability,
Availability,
Recoverability
“Adopt the Logical Data Warehouse Architecture to Meet Your Modern Analytical Needs” - Henry Cook, Gartner April 2018

13. 8
Logical Hybrid/Multi-Cloud Architecture

14. Benefits

15. 10
Fast Data for Rapid Decision Making
Research done with University Roma Tre –
Department of Economics and Mangement on:
170 projects
21 Industries
5 Continents
Fast Data Delivery is the Business Value of an
innovative technology as Data Virtualization
Times Savings & Speed deliver huge business
benefits in terms of cost reduction, revenue
increase and risk mitigation

16. 11
Real Time Data World
150 Billion devices connected in 2025
300% Data Volume Growth 2021-25
500% Real Time Data Growth 2021-25

17. 12
Need for Speed
“The need for speed has never been
greater”
(McKinsey & Company - “Ready, set, go: Reinventing the
organization for speed in the post-COVID-19 era”).

18. 13
Data Virtualization Value Chain

19. Case Studies

20. 15
Logical Data Lake and Data Services

21. 16
Customer 360 in Real Time
▪ Higher customer loyalty and
retention due to reduction in service
response time from 6 minutes to 2
minutes, a 66% reduction.
▪ Increased scalability of their data
infrastructure systems to support
business growth.
▪ Greater revenue opportunities from
upselling and cross-selling.

22. 17
IoT Integration for Predictive Maintenance Service
▪ With enhanced insight into part
failure rates, Caterpillar has been
able to optimize pricing and the
blend of parts and services.
▪ Caterpillar distributors and
customers were able to reduce
warranty costs for parts failure.
▪ Twofold boost in revenue – through
selling more parts and services and
through selling telematics in a DaaS
model.

23. 18
Cloud Modernization
▪ Efficiency and reliability – Transition to the
cloud has been embraced as a model for
innovation and agility
▪ Agility – Rapid prototyping of new data
sources with proper governance structure
▪ Finally, the Denodo Platform’s query
optimization capabilities greatly accelerate
Logitech’s data science and analytics
efforts giving it a 40% to 70% performance
improvement with the same volume of
data at 30% less cost.