Welcome!

Containers Expo Blog Authors: Pat Romanski, Carmen Gonzalez, Elizabeth White, Yeshim Deniz, Liz McMillan

Related Topics: Containers Expo Blog

Containers Expo Blog: Article

Ten Mistakes to Avoid When Virtualizing Data

Meeting the ever-changing information needs of today's enterprises

Data virtualization's ability to overcome hardware and software complexity provides enterprises with an excellent opportunity to improve IT agility and save significantly. As more enterprises seek these benefits, data virtualization is swiftly moving from new idea to the mainstream. This article looks at the 10 most common mistakes made by early adopters as object lessons for helping new implementations accelerate the potential achievement of data virtualization's benefits.

Determining where and when to use data virtualization is the source of five common mistakes that may occur as enterprises adopt data virtualization. Implementing data virtualization, from the design and enabling technology points of view, is the source of three potential mistakes. Failing to determine who implements it and failing to correctly estimate how much value may result are also common. Before we address these, let's begin with a definition of data virtualization.

Data Virtualization at a Glance
Data virtualization brings together (federates) data from multiple, disparate sources - anywhere across the extended enterprise both inside and outside the firewall - into unified, logical, virtualized data stores for consumption by nearly any front-end business solution including portals, reports, and applications (see Figure 1). As a middleware technology, data virtualization is often referred to as virtual data federation, high-performance query, or enterprise information integration (EII).

Data virtualization reduces development time and ongoing maintenance costs by avoiding physical data consolidation with its associated development, storage and support requirements. Further, as data requirements change or expand, modifying the virtual data store can be completed in minutes, typically without requiring IT resources to rebuild consolidated physical stores. Both business users and IT share in these benefits.

Mistake #1 - Trying to Virtualize Too Much
Data virtualization, similar to storage, server, and application virtualization, delivers significant top- and bottom-line benefits. For example, an energy firm uses data virtualization to integrate real-time oil field data with its nightly consolidated warehouse information to increase its production by thousands of barrels per day. A financial services firm reduces its new application development time by 50 percent, while another financial services firm saves nearly $2M annually in business intelligence (BI) and reporting costs.

However, data virtualization is not the solution for every data integration problem. For instance, when the consuming application requires multidimensional analysis, or when the data requires significant transformations before it can be consumed, physical data consolidation is the better approach.

To avoid virtualizing too much data on any particular development project, begin with a good understanding of the characteristics of the business, data sources, and data consumers.

Mistake #2 - Failing to Virtualize Enough
The flipside to Mistake #1 is failing to virtualize enough. Doing things the "way we've always done them," rather than looking for the best way, is something everyone can relate to. During the 1990s, physical data consolidation developed with the advent of separate, consolidated stores and specialized extract, transform and load (ETL) middleware. By the 2000s, ETL had become the default data integration paradigm. But should it be exclusive?

Failing to virtualize enough carries a large opportunity cost because physical data consolidation necessitates longer time to solution, more costly development and operations, and lower business and IT agility due to the extra overhead involved.

Fortunately, the prescription for avoiding this mistake is to carefully analyze and define requirements during the data integration decision-making process to ensure that the best solution to meet these requirements, not tradition, drives the decision.

More Stories By Robert Eve

Robert "Bob" Eve is vice president of marketing at Composite Software. Prior to joining Composite, he held executive-level marketing and business development roles at several other enterprise software companies. At Informatica and Mercury Interactive, he helped penetrate new segments in his role as the vice president of Market Development. Bob ran Marketing and Alliances at Kintana (acquired by Mercury Interactive in 2003) where he defined the IT Governance category. As vice president of Alliances at PeopleSoft, Bob was responsible for more than 300 partners and 100 staff members. Bob has an MS in management from MIT and a BS in business administration with honors from University of California, Berkeley. He is a frequent contributor to publications including SYS-CON's SOA World Magazine and Virtualization Journal.

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
Dynatrace is an application performance management software company with products for the information technology departments and digital business owners of medium and large businesses. Building the Future of Monitoring with Artificial Intelligence. Today we can collect lots and lots of performance data. We build beautiful dashboards and even have fancy query languages to access and transform the data. Still performance data is a secret language only a couple of people understand. The more busine...
Nicolas Fierro is CEO of MIMIR Blockchain Solutions. He is a programmer, technologist, and operations dev who has worked with Ethereum and blockchain since 2014. His knowledge in blockchain dates to when he performed dev ops services to the Ethereum Foundation as one the privileged few developers to work with the original core team in Switzerland.
René Bostic is the Technical VP of the IBM Cloud Unit in North America. Enjoying her career with IBM during the modern millennial technological era, she is an expert in cloud computing, DevOps and emerging cloud technologies such as Blockchain. Her strengths and core competencies include a proven record of accomplishments in consensus building at all levels to assess, plan, and implement enterprise and cloud computing solutions. René is a member of the Society of Women Engineers (SWE) and a m...
Andrew Keys is Co-Founder of ConsenSys Enterprise. He comes to ConsenSys Enterprise with capital markets, technology and entrepreneurial experience. Previously, he worked for UBS investment bank in equities analysis. Later, he was responsible for the creation and distribution of life settlement products to hedge funds and investment banks. After, he co-founded a revenue cycle management company where he learned about Bitcoin and eventually Ethereal. Andrew's role at ConsenSys Enterprise is a mul...
Whenever a new technology hits the high points of hype, everyone starts talking about it like it will solve all their business problems. Blockchain is one of those technologies. According to Gartner's latest report on the hype cycle of emerging technologies, blockchain has just passed the peak of their hype cycle curve. If you read the news articles about it, one would think it has taken over the technology world. No disruptive technology is without its challenges and potential impediments t...
If a machine can invent, does this mean the end of the patent system as we know it? The patent system, both in the US and Europe, allows companies to protect their inventions and helps foster innovation. However, Artificial Intelligence (AI) could be set to disrupt the patent system as we know it. This talk will examine how AI may change the patent landscape in the years to come. Furthermore, ways in which companies can best protect their AI related inventions will be examined from both a US and...
In his general session at 19th Cloud Expo, Manish Dixit, VP of Product and Engineering at Dice, discussed how Dice leverages data insights and tools to help both tech professionals and recruiters better understand how skills relate to each other and which skills are in high demand using interactive visualizations and salary indicator tools to maximize earning potential. Manish Dixit is VP of Product and Engineering at Dice. As the leader of the Product, Engineering and Data Sciences team at D...
Bill Schmarzo, Tech Chair of "Big Data | Analytics" of upcoming CloudEXPO | DXWorldEXPO New York (November 12-13, 2018, New York City) today announced the outline and schedule of the track. "The track has been designed in experience/degree order," said Schmarzo. "So, that folks who attend the entire track can leave the conference with some of the skills necessary to get their work done when they get back to their offices. It actually ties back to some work that I'm doing at the University of San...
When talking IoT we often focus on the devices, the sensors, the hardware itself. The new smart appliances, the new smart or self-driving cars (which are amalgamations of many ‘things'). When we are looking at the world of IoT, we should take a step back, look at the big picture. What value are these devices providing. IoT is not about the devices, its about the data consumed and generated. The devices are tools, mechanisms, conduits. This paper discusses the considerations when dealing with the...
Bill Schmarzo, author of "Big Data: Understanding How Data Powers Big Business" and "Big Data MBA: Driving Business Strategies with Data Science," is responsible for setting the strategy and defining the Big Data service offerings and capabilities for EMC Global Services Big Data Practice. As the CTO for the Big Data Practice, he is responsible for working with organizations to help them identify where and how to start their big data journeys. He's written several white papers, is an avid blogge...