Watch full webinar here: https://bit.ly/3N46zxX Cloud migration brings scalability and flexibility, and often reduced cost to organizations. But even after moving to the cloud, more often than not, organizational data can be found to be siloed, hard to access and lacking centralized governance. That leads to delay and often missed opportunities in value creation from enterprise data. Join Amit Mody, Senior Manager at Accenture, in this keynote session to learn why current physical data architectures are hindrance to value creation from data, what is a logical data fabric powered by data virtualization and how a logical data fabric can unlock the value creation potential for enterprises.

1. Accelerate Cloud Migrations and
Architecture with Data Virtualization
Amit Mody
Sr. Manager, Accenture

2. 2
Technology innovation at scale
Accenture develops and scales innovation serving 120 countries through an unparalleled network of more than 50 worldwide
Delivery Centers, comprising of Advanced Technology Centers and Intelligent Operations Centers.
• World’s largest modern software engineering practice - Everest Group
• 30+ years in software delivery excellence & 330K+ technology specialists
• We work with our leading ecosystem partners across digital, cloud, security, data
and analytics, automation, artificial intelligence, Blockchain | Multiparty systems &
industry X.0, IoT, as well as immersive experiences
• CloudFirst – $3B committed investment over next there years, 1500+ clients, 36K
projects with 80% of the global fortune 100 in 68 countries, 81K+ cloud certifications
in the MAAGs
• Data & AI – 30K+intelligent automation engineering service experts, 2.2K patents &
patents pending, 4K data scientists, 100+ technology alliances, 7 Data Innovation
Centers & Data Studios
• Security – 9 Global Cyber Fusion Centers, 1.5K security clients across 67 countries,
100M+ digital identities managed, 25M+ endpoints monitored, 5K security risks
mitigated per year
• Innovation – 8 R&D labs, 39 Liquid Studios globally, 2.3k issued and pending
patents globally in Accenture Labs, including 220 in the past year
• Application Management – 1.2K clients, 50+ delivery centers, 117K application
management professionals, 67K tickets managed per day
For more details on our delivery centers please visit - https://www.accenture.com/us-en/services/technology/delivery-centers

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No real time access to the data
Time to market is the biggest issue – takes significant time
to create value from the data
Large Enterprises have Federated data landscape with
data distributed across cloud, on-prem and multi-cloud
There is not a centralized way of securing and governing
data
Significant increase in infrastructure and storage cost
Impediments in the current data environment

4. 4
What is a Data Fabric?
01 02
Integrate data from disparate
data sources
Securely deliver an integrated view
of the different data objects
03 04 Automate the entire process using
AI/ML
Consume the integrated data for
analytics and operational
purposes
In Layman Terms

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Data Virtualization: Logical Data Fabric
A logical data layer – a “data fabric” – that provides high-performant, real-time, and secure access to integrated
business views of disparate data across the enterprise
• Data abstraction: decoupling
applications/data usage from
data sources
• Data integration without
replication of physical data
• Easy access to any data, high
performant and real-time/
right-time
• Data catalog: for self/service data
services and easy discovery
• Unified metadata security &
governance across all data assets
• Data delivery, in any format with
intelligent query optimization that
leverages new and existing
physical data platforms

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Logical Data Fabric vs. Physical Data Fabric
The Logical Data Fabric will significantly increase accessibility to the data and increase the speed to market
for new analytics initiatives and application integration.
The reduction of ETL by
Data Virtualization (DV)
Technology combined with
a Semantic layer can
deliver improved ROI
home-grown data platform
80% Time/Effort is spent in
Data Plumbing and only
20% is spent in Analysis
/Reporting and Generating
Insights
TODAY
TOMORROW
Today, Physical Data
Fabric focuses on:
• Daily Operations:
ETL, Batch Operations,
Infrastructure Updates,
Patches and Upgrades,
Security Compliance
• Rising Costs
And is missing:
• Data Catalog
• Data Lineage
• Data Quality
• Compliance
• Staff availability to
focus on deploying new
business features
Tomorrow, Data Fabric as
a Service:
• Removes all the
operational burden
• Provides a predicable cost
profile
• Adds Data Lineage,
Tagging and Data Catalog
And allows Business
to fully focus on:
• Deploying new business
features at speed
• Adopting a culture of
data stewardship and
data quality

7. 7
How is Data Virtualization different from
incumbent approaches?
Data virtualization: alternate approach
• Time to Market – less than 3 months
• Ability to really be AGILE
• ROI– higher, returns after 1 yr to 3 yrs
• Data is delivered real-time, fresh from the source
• Distributed federated data accessible using a data wrapper (DV)
across all sources of data across clouds/on-premise as a single
source of data
• Suitability : Organizations with Decentralized structure and several
technologies and toolset , Several products , M&A strategy
• Compute cost for running the queries on the DV platform needs to be
factored in as you consider moving from the centralized DW to a DV
AS-IS Data Flow: Incumbent Tech
• Time to Market : typically, 1 yr
• ROI - Lower , takes about 5 to 7 yrs for returns to kick in.
• Leads to data quality and accuracy issues as a result of the
approach being tied to data movement and Batch.
• Creates data snapshot and silos as we move data from the
source system to the DW. Overtime, you would have copies of
data in multiple places
• Suitability : Data Transformation is required to the data and data
needs to be stored in a different schema. Organization with
strong central data governance in place
Although a technology solution, Data Virtualization has profound Business Implications on Information Agility and Faster decision making in the Enterprise.

8. 8
HEALTHCARE – DATA BRIDGE – DEEP DIVE
THE CHALLENGE THE SOLUTION HIGHLIGHTS
• Client has traditional EDW set up in Teradata and
ODS is built on DB2
• Large number of business teams using Teradata to
build their own DataMart's on top of existing data
assets built through EDW/Data lake
• As client wants to reduce the use of Teradata and
move to data lake Gold layer built on Hadoop.
• Client also has road map to move data assets to
cloud
• Business was not willing to change their approach
on moving from Teradata to data lake
• Business teams pointed multiple challenges
related to data asset migration
• Accenture proposed Data bridge solution
• As part of this solution Denodo was selected
as DV tool along with Spring boot and
ReactJS technologies to build subscription
driven data virtualization
• Business teams doesn’t need to know where
the data is coming from and not required to
build their Datamart's on Teradata
• This approach would help IT move data
assets to cloud in phased manner and
migrate assets to new data technologies
without impacting business needs
• Accenture completed PoC and showed the
results to client based on PoC client went
ahead procured Denodo as DV tool
• Accenture solution also helped to get
business approval to move forward with
solution
• This approach would reduced overhead on
Teradata and business will focus more data
analytics
Company Profile
Client is the largest customer-owned health insurance company in the United States. It offers a wide variety of health and life insurance. The company employs nearly 23,000
people and serves nearly 16 million members. Revenue - $35.9 bn products and related services, through its operating divisions and subsidiaries

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HEALTHCARE – DATA BRIDGE – OVERVIEW
Solution Architecture
Illustrative
Illustrative
1 Trusted data sources are registered
2
User interfaces provides ability to
connect new data sources and
define semantic views (business
friendly views)
3
Workflow to execute approval
processes and automate manual
process
4
Virtualization layer Denodo
connects to any trusted data source
5
Semantic views provide business
friendly representation of data
6
Subscription repository maintains
record of subscriber info (who, what,
where and how)
7
Data pushed to consuming apps
based on subscription details
SQL
/
JDBC
/
ODBC
DATA
SOURCES
Curated Data
Assets
Domain Data
Assets
Integrated Data
Assets
EDW
SUBSCRIPTION
REPOSITORY
Subscriber 1
Subscriber 2
Subscriber 3
Subscriber N
CONSUMING
APPS
(Gemfire)
File
Teradata
Cloud DB
USERS
VIRTUAL
DB
SEMANTIC
VIEW
USER INTERFACE WORKFLOW
DATA BRIDGE
1
2 3
4 5 6 7

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