Radiantadvisors research cisco data virtualization adoption

OVERCOMING BARRIERS TO DATA VIRTUALIZATION ADOPTION

Research

A Cross-Talk Research PaperLindy Ryan, Research Director

Research

Overcoming Barriers to Data Virtualization Adoption

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This edition published September 2014.

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Introduction

Cross-Talk Research Methodology

Barrier #1: Choosing Data Virtualization

Barrier #2: identifying the Lynchpin Use Case

Barrier #3: Assigning Roles and Responsibilities

Barrier #4: Selecting a Vendor

Conclusion

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Table of Contents

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The challenges of data management are getting exponentially harder. With

the ever-increasing quantities, sources, and structures of data – as well as the

insurgence of new tools and techniques for storing, analyzing, and deriving deeper

insights from this information – data-driven companies continue to evaluate and

explore data management technologies that better integrate, consolidate, and

unify data in a way that offers tangible business value.

Data virtualization is so compelling because it addresses business demands for

data unification and supports high iteration and fast response times, all while

enabling self-service user access and data navigability. However, adopting data

virtualization is not without its set of barriers. Primarily, these relate to building a

business case that can articulate the value of data virtualization in terms of speed

of integration alongside the ability to manage ever-growing amounts of data in a

timely, cost-efficient way.

Supported by Cisco Data Virtualization, Radiant Advisors had the opportunity

to further explore and understand the barriers experienced by companies

considering data virtualization adoption, and then to pose these questions to

companies that have already adopted data virtualization to glean their insights,

best practices, and lessons learned. Together, the two halves of this research

facilitate a practicable, independent, and unscripted “cross-talk” to fill information

gaps and assist companies in overcoming barriers to data virtualization adoption.

Introduction

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Cross-Talk Research Methodology

Enabling Competitive Advantage with Modern Data Platforms 22

The first portion of the research independently solicited the participation of

many companies within the Radiant Advisory Network to properly harness a

wide perspective across many industries and organizations to identify common

concerns and questions regarding barriers to data virtualization from a broad, pre-

adopter community. The companies included were very interested in adopting

data virtualization and either currently not using data virtualization or evaluating

the technology for potential adoption. Gathering this perspective ranged from

structured discussions at industry events to formal interviews with BI team leads.

As a result, we identified areas of concern and isolated critical questions in need of

response from the post-adoption community.

These top-of-mind concerns were then posited to companies that have already

adopted and implemented data virtualization within their organization. To facilitate

this exchange of information, current data virtualization-successful customers

were solicited from Cisco’s existing customer network. Representatives from

each company were invited to independently and anonymously share insights,

best practices, and lessons learned in overcoming barriers to data virtualization

adoption.

Following the completion of all interviews, detailed research notes from both

consorts of companies were synthesized with Radiant Advisors’ expertise and

thought-leadership to distill key insights that filled the information gaps identified

by pre-adopter companies.

Ultimately, this research and the following report are intended to guide potential

adopters to overcome perceived barriers to data virtualization adoption by

facilitating an independent, unscripted “cross-talk” between potential adopters

and those who have already moved forward.

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With so many available technologies and approaches to data management, the first

question that potential adopters consider is the rationale for the decision to adopt

data virtualization opposed to other approaches (i.e. data federation, master data

management (or, MDM), the data lake, extract-transform-load (or, ETL), etc.). Some

companies have expressed that MDM provides a more manageable and intuitive

approach, rather than consolidating multiple copies of data. Others wonder

if data virtualization will eventually take a back burner in data management as

the concept of the data lake grows in popularity and acceptance. Finally, there

is lingering ambiguity about how much data virtualization is needed for batch

analytics, real-time insight, and interactive queries – and if data virtualization can

become oversaturated with too much data from the same source.

Then, after deciding on data virtualization has been nominated as the preferred

approach, the next “biggest hurdle” has been expressed as how to effectively

communicate what data virtualization is – both as a data management construct

and as a value proposition – to C-level executives who are critical for buy-in and

championship at the business level. It has been noted by IT teams that business

executives tend to “get lost in the sauce” and dwell on trying to navigate the

differences between different data integration technologies, and that distinctions

aren’t meaningful at the C-level. Frankly, this makes the ability to gain executive

buy-in and support difficult. Companies evaluating data virtualization would like to

better understand how to take data virtualization “from a concept to a framework”

in a way that is communicable and meaningful to executive stakeholders.

Overcoming the Barrier

Current data virtualization users note that sometimes you can’t be traditional –

you have to do something different. That said, the value proposition is simple: data

virtualization avoids longer data integration development lifecycles that are riskier

to meet fast paced business needs, is faster to market and deploy, and supports

the changes that happen on-the-fly in the organization.

Companies that use data virtualization have described it as a seamless and quick

way to “insulate and adapt,” by using an abstraction layer to “snap together” data,


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“When you move really fast,

customers are really happy

– every bit of slowness

directly impacts customer

happiness.”

- A commercial, web-based

services company

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change views, present the same data from multiple places, and change the logic

behind it. The reality is that (business) users want unfettered access to unaltered

data, and they want it fast. Using data virtualization avoids depleting already-

scarce IT time to build a model that ends up not meeting expectations – inevitably

frustrating business – and instead offers a quick scanning tool to build in a way

that responds immediately to business changes and needs.

Data virtualization has, too, been seen as a way to expand IT’s potential – described

as a “capacity increasing initiative,” if you will. As a layer of abstraction, data

virtualization lives between data sources to establish unified semantic context

and data access without actually impacting the data source structure. It provides a

mechanism to bring IT closer to the business, too, moving away from a very tech-

heavy and IT-driven function to one that can be translated into business language

and moved closer to the business user for richer enterprise-wide collaboration.

Finally, data virtualization has been implemented as a vehicle to polyglot

persistence (persisting data in the data store best suited for its purposes). With

the influx of new databases, the ability to store data in more performant ways

than traditional databases becomes increasingly more imperative. A move to an

abstraction layer has proven both empowering and agile to understand what data

exists where and what the access patterns are around that data, as well as enables

the ingestion of more on-premise and cloud data. Ultimately, from an executive

management perspective, the value of data virtualization aligns with the ability to

provide enterprise data back out for analytics and visualization, and moves away

from the use of “black box” enterprise systems that can make it difficult to acquire

data in real-time.

One interesting note is that the reasons for choosing data virtualization as a

core technology and the reasons for keeping it are not always one in the same.

For example, one Colorado-based non-profit explained that they choose data

virtualization to provide a layer of abstraction between data stores and users of

those stores with the idea that IT would be easily able to switch out the back end

with no changes required to the front end. However, while the use case remains

valid, at this particular organization it turned out to be an internal fallacy as the


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company’s user groups all had power users who demanded access to unaltered

source system data and resisted the approach. In the end, this company chose to

keep data virtualization while other investments were canned (including MDM,

ETL, and rapid DW development) because of its success in using data virtualization

to enable both logical data warehousing and real-time data provisioning for

services.


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Potential data virtualization adopters are eager to understand (and learn from)

the experiences of post-adopters regarding how to “get started” using a data

virtualization layer – including what elements informed that decision, the resulting

lessons learned, and how that initial experience has been applied to an overall

data virtualization growth strategy.

With so many possible “lynchpin” use cases, many potential data virtualization

adopters have expressed concern that data virtualization can seem “too

conceptual” of a concept to hone in one a single opportunity that simultaneously

takes advantage of agility, is speedy to start, and illustrates a real business value

justification opportunity. Specific questions from potential adopters asked for

further exploration into whether there have even been use cases that have been

proven to be impossible to do without data virtualization – as opposed to more

difficult – and if there were any specific implementation challenges (or “gotchas”)

that were uncovered during a data virtualization implementation, and how these

were addressed.

Moving forward, once the initial hurdle of launching data virtualization has been

overcome, potential adopter thoughts turn to how data virtualization is expanded

within the enterprise as part of a larger growth strategy and seek insights to guide

phased adoption and implementation. This path of thought also includes which

implementation strategies to avoid with data virtualization, to ensure that it is

deployed efficiently, effectively, and properly throughout the organization.


Post-adoption companies validate the importance of the proper lynchpin use case

to prove the value of data virtualization to the organization and earn executive

buy-in. The following represent a sampling of data virtualization lynchpin use

cases that successfully introduced data virtualization to the organization:

• One media company had invested nearly $500k in the development of a physical

customer data mart that was, in the end, unable to be built according to established

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Barrier #2: Identifying the Lynchpin Use Case

“You can’t rebuild data every

day, so you need the ability

to get what you want on the

fly. In a [traditional] data

warehouse you build all or

you build nothing because

the data is persisted there –

but with data virtualization

you can build and rebuild on

the fly with data as needed.”

- A cable telecommunications

company

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timeline parameters and money allocated. Because the business had already

invested significant resources in the project, IT was unable to ask for additional

funding and, instead, had to deliver on the requirements document without

additional cost or time. With data virtualization, they were able to build virtually

and deploy within three months with only supplemental professional services

assistance. For this company, their lynchpin case was initiated as an opportunity

for creative problem solving.

• A commercial, web-based services company approached data virtualization

adoption as a way to overcoming a broken data environment. Previously at this

company, some databases weren’t well designed and were “bulled together” with

brute force, without consideration for architecture and business rules. Because of

this, additional BI tools couldn’t be added on top of the existing infrastructure and

technologies already in place couldn’t be used as intended. The initial hurdle was

trying to run poorly performing queries with handwritten SQL code, and initiatives

in ETL helped to properly structure for analytics but still left other operational and

real-time data unable to be seamlessly interlinked. Then, when the company’s

contact management system began a redesign of ownership hierarchies that

rendered hundreds of reports obsolete, IT was left with no time to rebuild a

fragmented data warehouse. Instead, they turned to data virtualization to insulate

and adapt the business as the data environment changed from underneath too

fast to approach in traditional ways.

• Another multinational agrochemical and biotechnology corporation tucked

data virtualization adoption discreetly within a $1 million business transformation

project aimed at enabling a “new start” in its business information platform. As

the project rolled out, data virtualization was simultaneously deployed across the

organization. The reasoning was straightforward: within any organization there is

a relatively small set of data (say, 15% by this company’s estimate) that is used over

and over again. This company wanted to take this highly-reusable data and make it

self-serving, thereby partnering business and IT to collaborate directly to address

the business’ desire to see their own data in their own language, vet definitions,

and then translate that into myriad data virtualization-enabled reporting tools as

a single source of extraction and value.


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• Lastly, a children’s non-profit organization used data virtualization to spur a

project that had been languishing for over a year to due the fact that modeling

it in a traditional EDW process (staging, ETL, business model data warehousing,

data view – “or worse yet, a cube” --, report) had proven far too cumbersome and

interdependent. After hearing other industry success stores on data virtualization,

this company was emboldened to dust off the old project, and set about to retiring

its traditional data warehouse. This was replaced with a logical data warehouses

that could meet organizational real-time data needs, however even this approach

was still unfamiliar territory fraught with struggle. Management stepped in and

helped foster organizational agreement on key terms and definitions. Ultimately,

the inclusion of a data virtualization layer allowed this company to provide both

operational and analytical reporting out of the same set of views, maximizing the

company’s operations and business value to its charitable endeavors.

Enabling Competitive Advantage with Modern Data Platforms 11


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As potential adopters begin to roadmap data virtualization as an integral part of

data and organizational architectures, many see the identification and designation

of resources and roles within the BI team as an obstacle due to their inherently

competitive natures (i.e., who has the “rights” to work within the data virtualization

tool and is this person a data modeler or an ETL developer? Moreover, how do they

interact with the DBA?). There is the perception that creating a data virtualization-

specific team drives agility by consolidating work in a tight area with limited

resources, however there is a lack of clarity as to whether these roles should be

isolated to IT, or whether they should be isolated within the business, as an IT

brokerage team, or as part of a cross-functional team.

Separately from ownership, under the helm of roles and responsibilities, there is

additional concern regarding the handling of security and governance within a

data virtualization environment. With the need to drive access and visibility for

discovery, many companies wonder how to enable a governed data discovery

environment that leverages data virtualization while still controlling individual

access and database security.


Adopted companies consistently noted that they retain small, interdisciplinary,

and agile teams to fully deploy and manage data virtualization. Many companies

noted a team of between five to seven people that is centralized and consists of

both business and IT roles (two to three IT FTE with others part of the organizational

matrix). One investment-banking firm noted that responsibility shared among

multiple departments has enabled the capacity to virtually grow in a well-

governed consistent fashion across the organization. Another non-profit noted

that these roles should, to some extent, be overlapping with each member having

some capacity to assist in other areas while maintaining specializations that align

to the core functions of the team.


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“Even with those basic (and

significant) flaws, we saw

tremendous success and

acceptance [from using

data virtualization] and

soon started to suffer from

the semi-mythological

‘perils of success.”

- A non-profit organization

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Specific core roles and responsibility

Application Administer

main responsibility is to ensure the server remains available and accessible with no downtime to correctly meet incoming demands, honor business service-level agreements, and anticipate future demands

Governance Architect

takes ownership of decide what should or should not be built as virtual objects – and then, how to build it – within the abstracted environment to sustain a sharable, consistent model that provides proper business semantics across the organization

Business Information

Owner

work in tandems with the governance architect to avoid a purely technical approach and retain the business value of data virtualization

Developer

acts as the single point of authority to vet and implement proposed changes within the data virtualization environment as well as builds, optimizes, and tunes the virtual objects

Most data virtualization teams also have a non-technical data virtualization

Champion that actively communicates with executive management to champion

data virtualization’s value and inclusion as a core technology within other areas

of the business. As a measuring point for data virtualization adoption within the

organization, some companies have also launched an Integration Competency

Center, too, to leverage the small data virtualization team for development

and training across the larger organization. Communication, community, and

awareness are essential, especially in areas of governing the abstraction layer

itself, which remains a moving target as both the role of data virtualization and the

business process of discovery continue to mature in the organization.


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Finally, potential data virtualization adopters seek to cut through the marketing of

vendors and understand key differentiators between data virtualization vendors

in the space today. Additionally, potential adopters hope to navigate beyond

the vendor marketplace and benefit from lessons learned and best practices of

adopters that have already had the opportunity to evaluate competing vendors

and have had the opportunity to learn from hands-on, already in-use experience

with chosen tools. Further, they would like to insight on how the evaluation and

selection of a data virtualization vendor applies relative to the total tool and vendor

landscape and technology architecture, how it has performed since its adoption,

and how this affects the ongoing roadmap of the organization.

As a vendor differentiator, beyond product capabilities has been a distinct interest

in how vendors support ongoing training and learning events for users, including

the performance and responsiveness of vendor technical support. Additionally,

because potential adopters recognize that there will be a learning curve inherent

in adopting a new technology, they are interested in the level of training and

support services available, what has proven to be the most useful and invaluable

for companies – and its technical and non-technical users – that are new to data

virtualization.


Key differentiators when evaluating data virtualization vendors, according to

those companies who have already undertaken this journey and made a buying

decision, can be distilled into three areas: vendor/product maturity, industry

impact, and roadmap; product technical capabilities; and level of vendor support

and services.

Vendor/Product Maturity and Impact Across Industries

Time and time again, adopting customers remarked the importance of seeing a

vendor client-base that resonates with their respective industries as a measuring

stick by which to weigh the vendor’s strength and permeation amongst its

competitors. Another indicator of vendor/product maturity is its time in the market


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“We went with the

[data virtualization]

approach because the

environment was changing

out from underneath way

too fast to approach in

traditional ways.”

- A commercial, web-based

services company

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and longevity with proven customer use cases. Further, adopting customers

looked for synergies in use cases that showcase product maturity and illustrate

how vendors have handled challenges trending in the market.

Ongoing transparency in where the vendor has been and where they are going

proves just as important to adopting customers, noting that the vendor roadmap

is critically important to the ongoing success of data virtualization. Adopting

customers also desired a vendor that allows users a voice into the product

development roadmap, and one that actively looks to incorporate emerging

technologies (such as the Cloud) to expand and refine existing capabilities.

Product Technical Capabilities

Of course, how the technology works and what it is capable of is a huge

differentiator during data virtualization vendor comparisons. In addition to the

distilled list of key differentiators below, price and total cost of ownership is, of

course, always an important factor in the buying decision.

• Ease of use (User interface; allows development using SQL)

• Availability of source system connectors (Databases, Web APIs, RESTful APIs)

• Ease of ingesting new types of data sources

• Ease of accessing data output (ODBC, JDBC)

• Support for complex caching needs

• Availability of clustering for redundancy

• Ease of deploying changes to production environment

• Visibility into execution plans

• Scalability (a big concern in large enterprises -- DV strategic architecture)

• Extensibility (ease of calling into and out of the DVL)

• Impact analysis, or ease of identifying lineage both backward and forward

for any entity or attribute at any level (i.e. sources, source abstraction layer,

middle/conforming layers, demand layer, and publications)

• Performance monitoring

• Object level security

• Optimization engine -- ability to create high performance execution plans

• Pass-down optimizations

• Audit logs for reviewing user access for governance and compliance


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Level of Vendor Support and Services

Lastly, adopting customers chose a vendor that not only has proven successful

and has the right capabilities and roadmap to help customers continue to be

successful, but for one that takes the initiative to help their customers actively

succeed. This is measured in terms of both responsive technical support in helping

to investigate and resolve issues, as well as the level of vendor training provided

to the customers that minimize the learning curve and helped to leverage existing

internal knowledge.

Finally, customers noted that professional services offered by the vendor would

be helpful as they work to develop the right data virtualization environment,

particularly at the onset of implementation. This is primarily because customers

noted that they still struggle with what goes into a classic layer and what goes into

a data virtualization layer, and how these complement each other. One customer

called this balance between access and ownership a “political turf war” and said

that governed data virtualization requires a large cultural change, as it is difficult

to get true ownership, especially pertaining to semantic definitions.


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Conclusion

Data virtualization is a mature technology that has evolved with the data

management industry. However, while nearly every major research and consulting

firm delivers architectural blueprints that include a logical business semantic layer,

only some companies have implemented that component -- and even fewer

have enterprise-wide – leaving data virtualization the often-missed piece of an

overall unification strategy that includes incumbent, traditional ETL and data

replication. As a technology, data virtualization in itself isn’t a silver bullet, but

when implemented with governance and a rich methodology, it solves the bigger

problem of managing data volatility and addresses many pain points of business

and IT.

The reality today is that, without a robust consolidation strategy, business and

IT simply cannot keep up with the volatile landscape of surging data. More and

more, integration is becoming a lackluster blanket integration strategy: even if you

manage to do it, it invariably won’t last. Instead, a data unification strategy that

adopts data abstraction through data virtualization as a key integration strategy

solves data volatility and offers very immediate benefits to alleviate the pain points

of the organization, and maximizes the value of integrations whether physical or

abstracted.

To achieve the benefits of data virtualization, companies need to take the leap.

So, while barriers exist, they can be easily overcome -- many have done so already

and are achieving these benefits today. This research is a guidebook, providing a

living conversation from both sides of the adoption table, and intended to assist

potential adopters in overcoming barriers by leveraging the insights and lessons

learned of those who have already moved forward.

After overcoming barriers to data virtualization adoption, the next step is to plan

this technology as part of a long-term data integration strategy. Therefore, the

same discipline and diligence that is put into physical data integration should be

applied into the abstracted environment. Seek to quantify the reduction in both

development time and time to deployment leveraging data virtualization, and,

once these are quantified, double the benefit quotient by moving beyond the

immediate, localized benefit and looking farther to drive larger, enterprise-wide

strategic benefits.

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About Radiant Advisors

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© 2014 Radiant Advisors. All Rights Reserved.

Cisco (NASDAQ: CSCO) is the worldwide leader in IT that helps companies seize the opportunities

of tomorrow by proving that amazing things can happen when you connect the previously

unconnected. Cisco Information Server is agile data virtualization software that makes it easy

for companies to access business data across the network as if it were in a single place.

To learn more about Cisco Data Virtualization

visit www.cisco.com/go/datavirtualization

About the Author

Lindy Ryan, Research Director, Data Discovery and Visualization, Radiant Advisors

As Research Director for Radiant Advisors’ Data Discovery and Visualization practice, Lindy leads research and analyst activities in the confluence of data discovery, visualization, and data science from a business needs perspective.

http://www.radiantadvisors.com

http://www.cisco.com/go/datavirtualization

http://www.cisco.com

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