Analyst Keynote: Forrester: Data Fabric Strategy is Vital for Business Innovation

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Advancing Cloud, Analytics, and
Data Science with Logical Data Fabric
The Agile Data Management and Analytics Conference

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#DenodoDataFest
Data Fabric Strategy Is Vital For Business
Innovation
• Vice President, Principal Analyst
• Forrester Research
N o el Yuha nna

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© 2020 FORRESTER. REPRODUCTION PROHIBITED.
© 2020 Forrester. Reproduction Prohibited.
Data has become the most critical asset for any
business to succeed in this digital transformation era
Increases revenue
Retains customers
Delivers new products
and services
Enables innovation
51%
Commercializing
data!
Improves customer experience
Expand to new
markets

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But your data is spread across hybrid and multiple
clouds …. creating new data management challenges!
DB
ERP/CRM
Web Apps
Analytics
Insights
MF
eCommerce
Cloud - AWS
Big data Apps
Hadoop
Data
warehouse
Cloud - Azure
Cloud
apps
SaaS
Workday
SF
Cloud - Google
On-premises
AUSTIN
On-premises
AUSTIN
On-premises
NEW YORK
On-premises
NEW YORK
DB
DB

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Traditional data integration is failing to meet your current
business requirements …
DB
ERP/CRM
Web Apps
Analytics
Insights
DB
eCommerce
Cloud - AWS
Big data Apps
Hadoop
Data
warehouse
Cloud - Azure
Cloud
apps
SaaS
Workday
SF
Cloud - Google
On-premises
On-premises
On-premises
On-premises
DB
DB
ETL
ETL
Batch oriented
Only structured data
Static data sources
Not Agile Not real-time
Duplication of data
Time to value issue
Performance issues
Lack of SaaS sources
ETL
Challenges

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Positive
Slowing down insights can impact decisions and can
even by harmful to the business ….
Business
value
Time to action
Data
originated
Analytics performed
Insights gleaned
Action
taken
Outdated
insights
Impotent or harmful
actions
Negative
Decision made
Poor decision
Traditional ETL/DI approaches

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178% more likely
to grow revenue
2.5 times more
likely to grow
revenue at 20% or
more.
Base: 1,376 global data and analytics decision
Source: Forrester Analytics Global Business Technographics® Data And Analytics Survey, 2019
In fact, Insights-driven firms that take accurate and
quicker decisions are …

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Data
warehouse
Database
Source: Forrester Research
What businesses need is a new approach to
connecting data silos dynamically to support new and
emerging business needs…
On-premises Cloud

DEFINITION
FORRESTER
“Dynamically orchestrating disparate data sources intelligently
and securely in a self-service manner and leveraging various
data platforms to deliver integrated and trusted data to support
various applications, analytics, and use cases”
Data fabric

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Data fabric architecture
Data security
Data governance
Metadata/catalog
Data processing
Data quality
Data lineage
Data management
Global distributed platform, in-memory, embedded,
self-service, and APIs
Global data access
Data modeling, preparation, curation, and graph engine Data discovery
Transformation, integration, and cleansing Data orchestration
Ingestion, streaming, and data movement Data ingestion/streaming/
Data Virtualization
Data platform —
processing
Data processing/
persistence
Hadoop
NoSQL
Spark
Data lake
EDW/BDW
On-premises
Cloud Data sources
Policies
AI/ML
AI/ML
AI/ML
AI/ML
AI/ML
AI/ML
Source: Data Virtualization Or Data Fabric: Which Is Right For You? Forrester report
Data virtualization

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Data virtualization is one of the top technologies being
leveraged to support data management …
“Which of the following are included in your plans for data management over the next 12 months?”
Base: 2154 data and analytics technology decision makers; Source: Forrester Analytics Global Business Technographics® Data And Analytics Survey, 2019

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• Automating various data management functions – integration, transformation,
preparation, curation, security, governance and orchestration.
• Integrate across Hybrid and multi-clouds – offering rapid and on-demand
integration capabilities to accelerate insights and analytics.
• Broad set of use cases – Quickly responds to new business needs whether for
customer 360 or fraud analysis.
• Real-time data initiatives – Including supporting real-time analytics, critical
business applications, and insights.
• Centralizes data security and governance – Controlling who can access data
based on security policies.
Key capabilities of data fabric addresses the gaps in
legacy solutions….

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• Customer 360 — personalization
• Customer intelligence — for upsell/cross-sell,
targeted marketing
• Fraud detection
• Risk assessment analytics
• Healthcare-related analytics and insights
• Financial-services related analytics
• Insurance-related analytics
• Retail-related analytics
• Telco-related analytics
• Others
Typical use cases for data fabric continue to expand …

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• Agility: enables quick response to access from multiple sources
• Real-time data: access to information in real time
• No coding: No developers are required to start using it.
• Centralized: All data can be accessed at one location.
• Dynamic integration: brings together diverse sources
• Trusted data: provides consistent data for applications and insights
• Secure: leverages underlying security access controls to protect data
• Easy to use: does not require learning any new technology
• Self-service: can be leveraged by any business user
Key benefits of data virtualization are many …

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Recommendations
❖ Create a team to succeed comprising of EA, data
engineers, security & analysts.
❖ Start with a few data sources to integrate, don’t
boil the lake.
❖ Leverage ML and AI to automate various layer of
data fabric
❖ Keep security in mind from start – ensuring
access controls, audits and monitoring.
❖ Leverage solutions that can help to accelerate
data fabric initiative.

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