Welcome!

Containers Expo Blog Authors: Yeshim Deniz, Pat Romanski, Elizabeth White, Liz McMillan, Zakia Bouachraoui

Related Topics: Containers Expo Blog, Microservices Expo

Containers Expo Blog: Article

How Data Virtualization Improves Business Agility – Part 3

Optimize staff, infrastructure and integration approach for maximum ROI

While the benefits derived from greater business agility are significant, costs are also an important factor to consider. This is especially true in today's extremely competitive business environment and difficult economic times.

This article, the last in a series of three articles on how data virtualization delivers business agility, focuses on resource agility.

In Parts 1 and 2, business decision agility and time-to-solution agility were addressed.

Resource Agility Is a Key Enabler of Business Agility
In the recently published Data Virtualization: Going Beyond Traditional Data Integration to Achieve Business Agility, resource agility was identified as the third key element in an enterprise's business agility strategy, along with business decision agility and time-to-solution agility.

Data virtualization directly enables greater resource agility through superior developer productivity, lower infrastructure costs and better optimization of data integration solutions.

These factors combine to provide significant cost savings that can be applied flexibly to fund additional data integration activities and/or other business and IT projects.

Superior Developer Productivity Saves Personnel Costs
At 41% of the typical enterprise IT budget, personnel staffing expenses, including salaries, benefits and occupancy, represent the largest category of IT spending according to recently published analyst research. This spending is double that of both software and outsourcing, and two-and-a-half times that of hardware.

Not only are these staffing costs high in absolute terms, with data integration efforts often representing half the work in a typical IT development project, data integration developer productivity is critically important on a relative basis as well.

As described in Part 2 of this series, data virtualization uses a streamlined architecture and development approach. Not only does this improve time-to-solution agility, it also improves developer productivity in several ways.

  • First, data virtualization allows rapid, iterative development of views and data services. The development and deployment time savings associated with this development approach directly translate into lower staffing costs.
  • Second, the typically SQL-based views used in data virtualization are a well-understood IT paradigm. And the IDEs for building these views share common terminology and techniques with the IDEs for the most popular relational databases. The same can be said for data services and popular SOA IDEs. These factors make data virtualization easy for developers to learn and reduce training costs typically required when adopting new tools.
  • Third, graphically oriented IDEs simplify data virtualization solution development with significant built-in code generation and automatic query optimization. This enables less senior and lower cost development staff to build data integration solutions.
  • Fourth, the views and services built for one application can easily be reused across other applications. This further increases productivity and reduces staffing resource costs.

Better Asset Leverage Lowers Infrastructure Costs
Large enterprises typically have hundreds, if not thousands, of data sources. While these data assets can be leveraged to provide business decision agility, these returns come at a cost. Each source needs to be efficiently operated and managed and the data effectively governed. These ongoing infrastructure costs typically dwarf initial hardware and software implementation costs.

Traditional data integration approaches, where data is consolidated in data warehouses or marts, add to the overall number of data sources. This necessitates not only greater up-front capital expenditures, but also increased spending for ongoing operations and management. In addition, every new copy of the data introduces an opportunity for inconsistency and lower data quality.

Protecting against these inevitable issues is a non-value-added activity that further diverts critical resources. Finally, more sources equal more complexity. This means large, ongoing investments in coordination and synchronization activities.

These demands consume valuable resources that can be significantly reduced through the use of data virtualization. Because data virtualization requires fewer physical data repositories than traditional data integration approaches, enterprises that use data virtualization lower their capital expenditures as well as their operating, management and governance costs. In fact, many data virtualization users find these infrastructure savings alone can justify their entire investment in data virtualization technology.

Add Data Virtualization to Optimize Your Data Integration Portfolio
As a component of a broad data integration portfolio, data virtualization joins traditional data integration approaches such as data consolidation in the form of data warehouses and marts enabled by ETL as well as messaging and replication-based approaches that move data from one location to another.

Each of these approaches has strengths and limitations when addressing various business information needs, data source and consumer technologies, time-to-solution and resource agility requirements.

For example, a data warehouse approach to integration is often deployed when analyzing historical time-series data across multiple dimensions. Data virtualization is typically adopted to support one or more of the five popular data virtualization usage patterns:

  • BI data federation
  • Data warehouse extension
  • Enterprise data virtualization layer
  • Big data integration
  • Cloud data integration

Given the many information needs, integration challenges, and business agility objectives organizations have to juggle, each data integration approach added to the portfolio improves the organization's data integration flexibility and thus optimizes the ability to deliver effective data integration solutions.

With data virtualization in the integration portfolio, the organization can optimally mix and match physical and virtual integration methods based on the distinct requirements of a specific application's information needs, source data characteristics and other critical factors such as time-to-solution, data latency and total cost of ownership.

In addition, data virtualization provides the opportunity to refactor and optimize data models that are distributed across multiple applications and consolidated stores. For example, many enterprises use their BI tool's semantic layer and/or data warehouse schema to manage data definitions and models. Data virtualization provides the option to centralize this key functionality in the data virtualization layer. This can be especially useful in cases where the enterprise has several BI tools and/or multiple warehouses and marts, each with their own schemas and governance.

Conclusion
Data virtualization's streamlined architecture and development approach significantly improves developer productivity. Further, data virtualization requires fewer physical data repositories than traditional data integration approaches. This means that data virtualization users lower their capital expenditures as well as their operating, management and governance costs. Finally, adding data virtualization to the integration portfolio enables the optimization of physical and virtual integration methods.

These factors combine to provide significant cost savings that can be applied flexibly to fund additional data integration activities and/or other business and IT projects in the pursuit of business agility.

•   •   •

Editor's Note: Robert Eve is the co-author, along with Judith R. Davis, of Data Virtualization: Going Beyond Traditional Data Integration to Achieve Business Agility, the first book published on the topic of data virtualization. This series of three articles on How Data Virtualization Delivers Business Agility includes excerpts from the book.

More Stories By Robert Eve

Robert Eve is the EVP of Marketing at Composite Software, the data virtualization gold standard and co-author of Data Virtualization: Going Beyond Traditional Data Integration to Achieve Business Agility. Bob's experience includes executive level roles at leading enterprise software companies such as Mercury Interactive, PeopleSoft, and Oracle. Bob holds a Masters of Science from the Massachusetts Institute of Technology and a Bachelor of Science from the University of California at Berkeley.

Comments (0)

Share your thoughts on this story.

Add your comment
You must be signed in to add a comment. Sign-in | Register

In accordance with our Comment Policy, we encourage comments that are on topic, relevant and to-the-point. We will remove comments that include profanity, personal attacks, racial slurs, threats of violence, or other inappropriate material that violates our Terms and Conditions, and will block users who make repeated violations. We ask all readers to expect diversity of opinion and to treat one another with dignity and respect.


IoT & Smart Cities Stories
At CloudEXPO Silicon Valley, June 24-26, 2019, Digital Transformation (DX) is a major focus with expanded DevOpsSUMMIT and FinTechEXPO programs within the DXWorldEXPO agenda. Successful transformation requires a laser focus on being data-driven and on using all the tools available that enable transformation if they plan to survive over the long term. A total of 88% of Fortune 500 companies from a generation ago are now out of business. Only 12% still survive. Similar percentages are found throug...
Never mind that we might not know what the future holds for cryptocurrencies and how much values will fluctuate or even how the process of mining a coin could cost as much as the value of the coin itself - cryptocurrency mining is a hot industry and shows no signs of slowing down. However, energy consumption to mine cryptocurrency is one of the biggest issues facing this industry. Burning huge amounts of electricity isn't incidental to cryptocurrency, it's basically embedded in the core of "mini...
The term "digital transformation" (DX) is being used by everyone for just about any company initiative that involves technology, the web, ecommerce, software, or even customer experience. While the term has certainly turned into a buzzword with a lot of hype, the transition to a more connected, digital world is real and comes with real challenges. In his opening keynote, Four Essentials To Become DX Hero Status Now, Jonathan Hoppe, Co-Founder and CTO of Total Uptime Technologies, shared that ...
Every organization is facing their own Digital Transformation as they attempt to stay ahead of the competition, or worse, just keep up. Each new opportunity, whether embracing machine learning, IoT, or a cloud migration, seems to bring new development, deployment, and management models. The results are more diverse and federated computing models than any time in our history.
At CloudEXPO Silicon Valley, June 24-26, 2019, Digital Transformation (DX) is a major focus with expanded DevOpsSUMMIT and FinTechEXPO programs within the DXWorldEXPO agenda. Successful transformation requires a laser focus on being data-driven and on using all the tools available that enable transformation if they plan to survive over the long term. A total of 88% of Fortune 500 companies from a generation ago are now out of business. Only 12% still survive. Similar percentages are found throug...
Dion Hinchcliffe is an internationally recognized digital expert, bestselling book author, frequent keynote speaker, analyst, futurist, and transformation expert based in Washington, DC. He is currently Chief Strategy Officer at the industry-leading digital strategy and online community solutions firm, 7Summits.
Digital Transformation is much more than a buzzword. The radical shift to digital mechanisms for almost every process is evident across all industries and verticals. This is often especially true in financial services, where the legacy environment is many times unable to keep up with the rapidly shifting demands of the consumer. The constant pressure to provide complete, omnichannel delivery of customer-facing solutions to meet both regulatory and customer demands is putting enormous pressure on...
IoT is rapidly becoming mainstream as more and more investments are made into the platforms and technology. As this movement continues to expand and gain momentum it creates a massive wall of noise that can be difficult to sift through. Unfortunately, this inevitably makes IoT less approachable for people to get started with and can hamper efforts to integrate this key technology into your own portfolio. There are so many connected products already in place today with many hundreds more on the h...
The standardization of container runtimes and images has sparked the creation of an almost overwhelming number of new open source projects that build on and otherwise work with these specifications. Of course, there's Kubernetes, which orchestrates and manages collections of containers. It was one of the first and best-known examples of projects that make containers truly useful for production use. However, more recently, the container ecosystem has truly exploded. A service mesh like Istio addr...
Digital Transformation: Preparing Cloud & IoT Security for the Age of Artificial Intelligence. As automation and artificial intelligence (AI) power solution development and delivery, many businesses need to build backend cloud capabilities. Well-poised organizations, marketing smart devices with AI and BlockChain capabilities prepare to refine compliance and regulatory capabilities in 2018. Volumes of health, financial, technical and privacy data, along with tightening compliance requirements by...