data virtualization increases its adoption: webinar by noel yuhanna (forrester research)

Data Virtualization: Advancements Driving Broad Adoption

Today’s Speakers

■ Paul Moxon

Senior Director, Product Management

Denodo Technologies

■ Noel Yuhanna

Principal Analyst

Forrester Research

Data Virtualization: Advancements

Driving Broad AdoptionNoel Yuhanna, Principal Analyst

Denodo Webinar

© 2015 Forrester Research, Inc. Reproduction Prohibited 4

Agenda

› Changing business demands for data

› What is data virtualization?

› Data virtualization use cases

› Forrester wave on data virtualization

› Recommendations


› 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


Data management challenges have grown

Silos

ScaleMulti-format

Duplication

Governance

Security

Performance


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


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


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


Data virtualization is new and innovative

Source: August 8, 2013, “Information Fabric 3.0” Forrester report


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


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…


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 . .


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


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.


› 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


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


Top data virtualization vendors


The Forrester Wave™: Enterprise Data Virtualization, Q1 2015

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


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?


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

Thank you

forrester.com

Noel Yuhanna

[email protected]

Twitter: @nyuhanna

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)


“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)


“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)


“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)

Large Stock Exchange…

Trading Systems

Clearing Systems

Broker Dealer

Accounting

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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

Leading Cable Operator…

Trading Systems

Clearing Systems

Broker Dealer

Accounting

Data Vendors

Other

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ayerNews/

Announcements

Source Systems

BI/Reporting/

Analytics

Applications

requiring

Enterprise Data

Data on

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Connect to

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sources

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Data Quality

Data Modeling/

Standardisation

Data Integration

Publish data to a

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Plug into BI/

Reporting tools



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Cloud

Public Data


Master Data

Historical Data


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Global Eletronics Manufacturer…

Trading Systems

Clearing Systems

Broker Dealer

Accounting

Data Vendors

Other

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prise

Data

Acc

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ayerNews/

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



data on demand

Cloud

Public Data


Master Data

Historical Data


Listings

Leading Mutual Fund…

Trading Systems

Clearing Systems

Broker Dealer

Accounting

Data Vendors

Other

Enter

prise

Data

Acc

ess L

ayerNews/

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



data on demand

Cloud

Public Data


Master Data

Historical Data


Listings

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!)

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 modelingcapabilities, 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-valueare top reasons for selecting Denodo.”

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

Denodo – Upward Momentum

www.denodo.com [email protected]

http://www.denodo.com/

mailto:[email protected]

data virtualization increases its adoption: webinar by noel yuhanna (forrester research)

Data & Analytics

data management challenges

disparate data sources

integrated data onpremise

forrester report

various use cases

broader use cases

use cases transactional

business demands