Register to this webinar: http://goo.gl/9xNv8I This presentation is from Denodo’s "Data Virtualization: Advancements Driving Broad Adoption" webinar, led by our guest speaker Noel Yuhanna, Principal Analyst at Forrester Research. Noel's presentation is complemented by real examples of large and complex deployments of Data Virtualization presented by Paul Moxon, Senior Director of Product Management at Denodo. Topics covered: - Innovative features and the flexible architecture of Data Virtualization are driving broader adoption of this technology. - Large scale customer use cases deploying Data Virtualization for Self-Service BI, Big Data Analytics, Cloud Integration and Enterprise Data Services. - Forrester's recommended best practices for adoption of Data Virtualization.

1. Data Virtualization: Advancements
Driving Broad Adoption

2. Today’s Speakers
■ Paul Moxon
Senior Director, Product Management
Denodo Technologies
■ Noel Yuhanna
Principal Analyst
Forrester Research

3. Data Virtualization: Advancements
Driving Broad Adoption
Noel Yuhanna, Principal Analyst
Denodo Webinar

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Agenda
› Changing business demands for data
› What is data virtualization?
› Data virtualization use cases
› Forrester wave on data virtualization
› Recommendations

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› Age of the customer – consumer personalization
› Real-time business insights from all connected data
› Internet of Things (IOT) Applications
› Data-driven mobile Applications
› 360-degree view of the business focused insights
› Self-service data driven insights
New business use cases have emerged

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Data management challenges have grown
Silos
ScaleMulti-format
Duplication
Governance
Security
Performance

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The new data management requirements
› Self-service data platform
› Delivers integrated data (on-premise and cloud)
› Right-time (near real-time, not batch)
› Scales to support more data, new sources
› Support broader use cases
› Simplified from modeling, quality and development
› Agile to support new business demands
› Secure to meet compliance

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What is data virtualization?
Forrester’s definition:
An integration agile platform that orchestrates data in real
time or near real time from disparate data sources,
whether on-premises or cloud, into coherent self-service
data services to support various use cases and
workloads including extreme transactions, analytics, and
predictive analytics

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High-level data virtualization architecture
Data virtualization
Applications Transactions
Analytics
Operational
Other workloads
ETL
Persistent
repository
EDW
OLTP DB
Cloud
sourcesDatabases
1 2
Hadoop
Direct access
Traditional EDW Stack
ODS

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Data virtualization is new and innovative
Source: August 8, 2013, “Information Fabric 3.0” Forrester report

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How is data virtualization is different?
• Delivers a real-time/near real-time data platform
• Supports all kinds of workloads including transactions
• Integrates with traditional ETL, EDW and directly into
various source systems
• Its an agile platform that delivers faster time-to-value
you don’t have to wait months
• Delivers self-service data platform that even end-users
can use directly

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Data virtualization market adoption
› More than 15,000 customers worldwide mostly using
hand coded custom approach
› 65% of Fortune 500 companies are doing data
virtualization today
› Broad acceptance across verticals – financial services,
retail, Telco, manufacturing, High-tech and others..
› More use cases – transactional, operational and
analytical
› Besides established vendors like IBM, Informatica,
Denodo, Cisco, new vendors have joined the bandwagon
including Red Hat, SAS, SAP…

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Data virtualization growing use cases
› Customer analytics - integrating many sources
› Fraud detection – collecting all data events
› Recommendation engines – customer analytics
› IOT Apps – machine failure detection, predictive
› Mobile apps – mobile mind shift
› Vertical-specific – retail, insurance, banks
› And others . .

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Case study: Retailer uses DV to deliver
real-time customer analytics
› Background
• Data growth issues — few TBs to 10s of TBs — clickstream, social media,
blogs — spread across several databases, logs, and repositories.
• Wanted real-time access to billing, revenue, and other customer data to
better understand its customers and their usage patterns
• Retailer wanted real-time insights, immediate access to billed and unbilled
revenue, and ability to upsell and cross-sell new products.
› Solution
• Retailer chose Data Virtualization to deliver a virtualized data layer.
• Was able to access disparate data in near real time
• Retailer now has more detailed insights about the customer than ever before.
• Plans to add more data sources — geolocation, customer preferences . . .
• Retailer claims to have saved $1.5 Million with DV over three years

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Case study: Data Virtualization helps financial
services firm to support right-time data platform
› Background
• Firm provides investment products and services to various consumers — helping
find the right mix of products and services across their portfolio
• Investment management and research team deliver research data extracted from
dozens of sources and in several formats.
• Developers were previously using complex SQL views — although functional,
adding new services, source system, compliance mandates and new business
policies were extremely challenging
› Solution
• With data virtualization it found the platform easy to use and high-performance.
Architects created consolidated views with the help of the business, enabled
better collaboration and drove further innovation.
• As a result, financial services company grew its customer base by 20% in 2014,
and further claims saving more than $2.5 million yearly on technology and people
sources.

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› Background
• The investment firm has hundreds of various sources, subject-area data
stores, and finds it challenging to manage its growing data volumes and
provide business users all the data they required in a consistent way.
• While the company leaned toward data virtualization, business users wanted
to know how accurate the data was, especially having gone through several
rounds of transformation and integration.
› Solution
• DV allowed the firm to combine disparate data sources easily, creating a
real-time data fabric. Because the data is not copied, it reduces redundancy
and ensures timeliness.
• The overall project at the firm was well-received, delivering a centralized
location for data that’s linked and actionable.
Case study: Investment management firm
turn data into information

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Implementation approaches with data
virtualization
› Top-down
• Business users driven – focusing on self-service platform
• Delivers virtualized integrated access layer to support agility
across specific data sources to support those use cases
• Example: Integrated view of data for LOBs, specific use case
› Bottom-up
• More project specific – faster-to-value
• Integrated virtualized layer across most data sources across
on-premise and cloud
• Example: Integrated view of business across LOBs

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Top data virtualization vendors

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The Forrester Wave™: Enterprise Data
Virtualization, Q1 2015
Source: March 11, 2015, “The Forrester Wave™: Enterprise Data Virtualization, Q1 2015” Forrester report

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What to look for in a vendor solution?
› What sources can it integrate with?
› What features are available for scale and performance?
› What kind of security features exists?
› What support exists for data quality and governance?
› Is the product integration of products or a single
integrated product?
› Does it integrate with Big Data – Hadoop, NoSQL and
IOT applications?
› How much effort is involved to setup and deploy?
› How much budget do I have to allocate?

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Recommendations
› Data virtualization delivers competitive edge and drives
innovation and growth.
› Data virtualization should be part of your data DM
strategy to support various new and emerging use cases
› Start out with few sources and grow, adding more
sources and users
› Keep security, governance and data quality in mind.
› Data virtualization does not require huge investment, the
ROI is typically in 3-4 months

22. Thank you
forrester.com
Noel Yuhanna
nyuhanna@forrester.com
Twitter: @nyuhanna

23. DV in a Modern Data Architecture…
“Invest In The Building Blocks To Speed Data
Enterprise architects must revise their data architecture
to meet the demand for fast data. In-memory data
grids, data virtualization, and NoSQL data sources are
must-have technologies to effect the transition from
data museums to a world of contextual data services.”
Forrester: Create A Road Map For A Real-Time, Agile, Self-Service
Data Platform (February 2015)

24. DV in a Modern Data Architecture…
“Integrating with data virtualization platforms to
simplify ingestion. Data virtualization integrates disparate
data sources in real time or near real time to support
analytics, predictive analytics, customer personalization,
and real-time integrated analytics. It integrates with EDW
solutions, ingesting derived data sources after they have
been transformed, cleansed, and integrated.”
Forrester: The Forrester Wave™: Enterprise Data Warehouse, Q4 2013
(December 2013)

25. DV in a Modern Data Architecture…
“Denodo’s key strengths are delivering unified and
centralized data services with security and real-time
integration across multiple traditional and big data
sources including Hadoop, NoSQL, cloud, and SaaS.”
Forrester: Market Overview: Big Data Integration (December 2014)

26. DV in a Modern Data Architecture…
“Data, content, and the cloud. Data and content repositories
as well as a cloud-based infrastructure form the foundation in
this conceptual view of the APP. Components will access
data and information, for example, using APIs, dedicated
services, and different security mechanisms, especially where
composite apps combine on-premises and off-premises
(cloud-based) elements. Consequently, data virtualization will
also find an anchor here.”
Forrester: Increase Flexibility By Embracing Future Business And Technology
Trends (September 2013)

27. DV in a Modern Data Architecture…

28. Large Stock Exchange…
Trading Systems
Clearing Systems
Broker Dealer
Accounting
Data Vendors
Other
EnterpriseDataAccessLayer
News/
Announcements
Source Systems
BI/Reporting/
Analytics
Applications
requiring
Enterprise Data
Data on
Demand
Connect to
heterogeneous
sources
Data Profiling
Data Quality
Data Modeling/
Standardisation
Data Integration
Publish data to a
‘cloud’
Plug into BI/
Reporting tools
Support Applications
requiring ‘integrated’
data on demand
Cloud
Public Data
Reference Data Systems CRM
Master Data
Historical Data
Historical Data Stores
Listings

29. Leading Cable Operator…
Trading Systems
Clearing Systems
Broker Dealer
Accounting
Data Vendors
Other
EnterpriseDataAccessLayer
News/
Announcements
Source Systems
BI/Reporting/
Analytics
Applications
requiring
Enterprise Data
Data on
Demand
Connect to
heterogeneous
sources
Data Profiling
Data Quality
Data Modeling/
Standardisation
Data Integration
Publish data to a
‘cloud’
Plug into BI/
Reporting tools
Support Applications
requiring ‘integrated’
data on demand
Cloud
Public Data
Reference Data Systems CRM
Master Data
Historical Data
Historical Data Stores
Listings

30. Global Eletronics Manufacturer…
Trading Systems
Clearing Systems
Broker Dealer
Accounting
Data Vendors
Other
EnterpriseDataAccessLayer
News/
Announcements
Source Systems
BI/Reporting/
Analytics
Applications
requiring
Enterprise Data
Data on
Demand
Connect to
heterogeneous
sources
Data Profiling
Data Quality
Data Modeling/
Standardisation
Data Integration
Publish data to a
‘cloud’
Plug into BI/
Reporting tools
Support Applications
requiring ‘integrated’
data on demand
Cloud
Public Data
Reference Data Systems CRM
Master Data
Historical Data
Historical Data Stores
Listings

31. Leading Mutual Fund…
Trading Systems
Clearing Systems
Broker Dealer
Accounting
Data Vendors
Other
EnterpriseDataAccessLayer
News/
Announcements
Source Systems
BI/Reporting/
Analytics
Applications
requiring
Enterprise Data
Data on
Demand
Connect to
heterogeneous
sources
Data Profiling
Data Quality
Data Modeling/
Standardisation
Data Integration
Publish data to a
‘cloud’
Plug into BI/
Reporting tools
Support Applications
requiring ‘integrated’
data on demand
Cloud
Public Data
Reference Data Systems CRM
Master Data
Historical Data
Historical Data Stores
Listings

32. Look for a DV Platform that meets
your needs…
■ Can do more…
■ Broad functionality (source, publish, model/execute)
■ Works at scale in the real world
■ Proven scale in actual customer deployments for Data
Virtualization
■ Not just for prototyping and then switch to something else
■ Look at both product and "expertise/education“
■ Fast time to success
■ Integrated solution with ease-of-use to speed up development
and deployment
■ (And, yes, Cost!)

33. Denodo Platform meets your needs
■ Can do more…
“Customers appreciate Denodo’s easy-to-use, tight integration with
non-structured data, simple yet sophisticated data modeling
capabilities, search, data life-cycle management, and support for
comprehensive lists of data sources including big data, IoT, and
cloud sources.”
■ Works at scale in the real world
“…enterprises across financial services, insurance, retail, life
sciences, and government rely on Denodo to support their mission-
critical data virtualization deployments.”
■ Fast Time to success
“…specialized vendors, such as Denodo Technologies, offer more
integrated solutions and greater automation, ease of use, and
simplicity to speed up development and deployment of data
virtualization.”
■ Cost
“…customers often mention lower cost and faster time-to-value
are top reasons for selecting Denodo.”

34. Supporting your DV Journey
“your way”
■ Bottom Up
■ Start with skunkworks projects, proof-of-technology
■ Download ‘Denodo Express’
■ Learn from online tutorials, knowledge base
■ Top Down
■ Modern data architecture
■ Create Data Virtualization CoE
■ Denodo Data Virtualization Architecture training
■ Whichever approach you choose…
■ Get the Wave Report…do your homework
■ Call us to discuss your scenario and solutions

35. Denodo – Upward Momentum

36. Q&A

37. www.denodo.com info@denodo.com