Article

5 Top Data Virtualization Trends in 2022

August 4, 2022 | Enterprise Storage Forum.com | Drew Robb

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The Overview

The virtualization journey encompasses everything from storage to networking to data. This EnterpriseStorageForum.com’s trends article details how top companies are approaching data virtualization (take a peek at trend #2!).

 
The ARTICLE

Below article originally published by Enterprise Storage Forum | Authored by Drew Robb

 

5 Top Data Virtualization Trends in 2022

Many believe that an evolution of virtualization is ongoing. We had storage virtualization, server virtualization, containerization, desktop virtualization, network virtualization and now data virtualization is the next wave. 

See below some of the top trends companies and IT teams are seeing in the data virtualization market: 

1. Cloud-Native Infrastructure

Much has been said about the potential for cloud-native infrastructure. Some organizations have announced cloud-first or cloud-only initiatives. And developers are rising to the challenge.

You can foresee the future by what the developers are working on. In the nineties, they bought into the Windows ecosystem. So many developers worked on Windows that it became an enterprise behemoth.

Now it is the turn of cloud-native applications. Most developers are working on them and that is where IT is headed.  

“Data plays a vital role in enterprise development, but users are concerned about how to utilize that data more efficiently and concisely,’ said Edward Qin, chief product officer, Algoblu.

“Data virtualization fixes this problem.” 

2. Data Versus Apps 

How does data virtualization solve the problem? By changing the paradigm.

IT architecture traditionally sought to move data to the applications and databases where it is processed or analyzed.

But there is now so much data, and it is being created so rapidly that the traditional model is breaking. Factor in, too, the demands of real-time processing, and something had to change. That change is manifesting in a shift toward taking applications to the data rather than vice versa. 

“You can now bring your applications to your data,” said Harry Carr, chairman and CEO, Vcinity. 

“That frees up more possibilities of where and how you optimize your organization’s storage and infrastructure.”

3. Self-Service Data Insights

Bolstered by the growth and momentum of the data management industry, increasing self-service access to insights is a developing trend, according to Thor Olof Philogene, co-founder and CEO, Stravito. 

“This is no surprise, given that consumer insights teams are busier than ever, and consumer centricity is of growing importance,” Philogene said. 

“Insights teams at large enterprises are already advocating for self-service access to market research and insights, moving them from the role of gatekeepers to that of facilitators.”  

4. Insight Democratization

Self-service access means far greater access to analytics by far more people. Hence, the rise of insights democratization, whereby technology is making insights broadly accessible to a wider sphere of stakeholders across the organization.  

This trend has been ongoing for some time. But now it is accelerating. 

“Self-service access enables and empowers employees to access and use data independently,” said Philogene with Stravito. 

“Giving employees the tools to access, process, and apply insights in a way that can be sustained over the long-term encourages autonomous and efficient work, ultimately helping to catalyze a company’s ability to quickly adapt to shifting consumer needs.” 

5. Organizational Shift 

Data virtualization and accessibility, in turn, mean that organizations need to reevaluate how they present data and insight across the enterprise.

Yes, the past 10 years have enabled sales managers and line of business heads to do far more and have access to greater amounts of data. But this has largely been through dashboards with limited self-service analytics capabilities. 

The next phase is unleashing the inherent know-how of the business and letting people do whatever they want with analytics capabilities. After all, the sales manager typically knows their area better than an analyst. By putting real self-service analytics into their hands, they can be counted on to find data nuances that were never before available. 

As a somewhat crude analogy, consider a postal delivery route. The U.S. post office plans routes centrally. The algorithms are good, but their limitations include not taking terrain fully into account or mailbox accessibility. If those delivering the mail were given more of a voice, they could help the entire post office network develop better software and route planning, by introducing factors, such as steep hills, houses with lots of stairways, security gates where you have to wait to get inside, and more. 

Thus, any organization must look to achieve a balance between broad data accessibility and central planning. 

“On a business level, it’s clear that leveraging insights has proven to be more important than ever, so it’s time companies put data insights into the hands of knowledgeable stakeholders, who are able to make decisions based on accessible information,” said Philogene with Stravito. 

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