What is Data Virtualization?

With more data under their control than ever before—and with that volume of data increasing significantly every day—many leading organizations have outgrown traditional data warehouses. In the age of agility, businesses need a data management solution that can keep pace with rapid growth, enabling them to make sense of their data quickly and logically.

For an increasing number of organizations, data virtualization is that solution.

Data virtualization is an approach to data management where an organization’s data—including structured and unstructured data sets (e.g., social media posts, images, videos, rich media, and audio files)—is accessible via a centralized data layer interface or dashboard. Regardless of how it’s formatted or where it lives (e.g., a database, a CRM, or Dropbox), data virtualization enables organizations to retrieve, manipulate, and analyze all of their data at any time.

Here’s how it works: Data virtualization platforms leverage the metadata of each piece of data, no matter how or where it’s stored. This enables employees to make sense of all data, whether it lives on-premises in a data center or a data lake or data warehouse in the cloud—without having to move or copy any files or worry about changing formats. The technology essentially bridges the gap between disparate data sources and types, shattering data silos and giving organizations a clear view of their data’s totality while eliminating duplicate files and documents. Since employees no longer have to hop from repository to repository looking for information, data virtualization delivers much faster access to data, which helps organizations make better decisions in less time.

Due to the benefits data virtualization provides (more on that in a bit), it comes as no surprise that one recent study found that the global data virtualization market, which brought in $1.68 billion in 2017, will grow to $8.36 billion by 2026. What’s more, according to Gartner, 60 percent of organizations will invest in data virtualization tools by 2022.

When you start to understand just how beneficial data virtualization can be, these numbers start to make a lot of sense.

What are the benefits of data virtualization?

Because data virtualization enables organizations to manage all of their data, regardless of format or location, from one centralized interface, the technology delivers many advantages to organizations across all industries.

Here are some of the benefits that encourage an increasing number of enterprises to invest in data virtualization.

  • 1 Unlocking the full value of all data

    Enterprises have more information under their control than ever before. And much of that data is unstructured, meaning it doesn’t fit neatly into spreadsheet columns.

    Managing unstructured data is a major challenge for most businesses. In fact, a recent study found that 95 percent of organizations have some level of unstructured data under their control. What’s more, 90 percent of organizations have data silos that prevent them from realizing the full potential of all their data or seeing relationships between different data sets, particularly when stored in different repositories.

    Data virtualization solves these problems by helping organizations shatter silos and make sense of all their data—including unstructured data—no matter where it lives. This is a big deal because research suggests that companies that use big data can increase profitability by 10 percent.

  • 2 Higher profitability

    Leveraging big data increases profitability. But the cost-savings implications of data virtualization extend beyond that.

    Data virtualization helps organizations bolster their bottom lines by reducing infrastructure costs, streamlining data management to let employees reclaim time, and helping them identify new revenue-generating opportunities by analyzing data they otherwise likely wouldn’t be able to.

    With the right data virtualization solution, organizations can process data much faster, helping them rapidly create and test new business models—a benefit that takes on increasing importance in today’s fast-paced business world.

  • 3 Stronger data governance, security, and compliance

    The implementation of the GDPR has had a profound impact on companies that do business in Europe. The privacy law puts strict regulations on where companies can store data, what kind of data they can store, and how long. Organizations found in violation of the GDPR can face fines as high as €20 million or 4% annual global turnover, whichever is larger.

    One of the biggest changes that came with the GDPR is the so-called right to be forgotten, which requires companies to delete personally identifiable information of a user upon request. This can be particularly challenging for companies with their data spread out across several repositories and in several different environments.

    By creating a single source of truth accessible and readily available, data virtualization can help organizations avoid running afoul of regulations like the GDPR.

    As a bonus, avoiding potential non-compliance costs increases profitability further.

  • 4 Increased productivity

    Since data virtualization means all data is always available, employees become more productive because they have to spend less time looking for information.

    This benefit can have profound implications. Leading data virtualization solutions empower data scientists and data analysts by eliminating the bulk of their data management busywork, enabling them to utilize their skills more effectively and innovate faster.

    Developers also benefit from virtualization. Instead of tracking down data services, developers can focus on more important aspects of operations—like improving the user experience and running analytics to figure out what to work on next.

  • 5 Accelerated decision-making

    Historically, organizations would integrate data via traditional extract, transform, and load methods. This process was slow and inefficient, to say the least.

    Faster access to data—and the peace of mind that comes with knowing that access includes all data—add up to help businesses make better decisions in less time.

    As a result, businesses that use data virtualization tools can operate with agility and drive competitive advantage by moving faster than their less modern peers.

  • 6 Simplified IT management

    Data virtualization tools help organizations modernize their IT infrastructure, eliminating legacy systems, and moving to the cloud. Instead of working with several disparate systems and repositories or having access to many servers, data virtualization enables organizations to manage, analyze, and process all data from one location—regardless of format.

    Taken together, the technology helps companies simplify IT management, reducing costs while freeing up technicians to focus on other mission-critical areas of infrastructure.

  • 7 Real-time data delivery

    Virtualization eliminates the need for organizations to replicate data, which speeds up delivery considerably.

    As a result of real-time data delivery, companies that use data virtualization tools can make decisions with up-to-the-minute information. This positions them to meet customer needs faster and respond to shifts in the market quicker. Instead of spending several hours putting together reports, employees can do the same in minutes instead.

    By now, you’re aware of the benefits of data virtualization and why companies are increasingly moving to these modern platforms.

    Now, it’s time to look at some of the challenges organizations encounter when moving to a data virtualization platform.

What are the challenges of data virtualization?

Like any other piece of technology, data virtualization has its pros and cons. Here are a few of the common challenges companies encounter as they begin transforming their organizations with data virtualization.

  • 1 The technology is not a panacea by itself

    You can’t just invest in a data virtualization tool and expect your organization to evolve rapidly overnight.

    With the right approach, data virtualization can certainly help your business unlock the above benefits. But just pouring money into a new technology won’t solve all of your big data problems by itself.

  • 2 Deploying without a plan

    For data virtualization initiatives to work, you need to have a plan. While data virtualization, when successfully implemented, will make your employees’ jobs easier, they still need to be trained on the technology and understand how it will change their workflows.

    Remember, it’s not uncommon for employees to be hesitant to use new tools. So make the business case for them and tell them exactly how data virtualization will help them work more effectively.

    As you begin designing a data virtualization plan, create policies and processes that your team should follow. Hence, they know how to use the new tools productively and efficiently and know what’s expected of them.

  • 3 Implementation can cost some money

    Since you’re reading this post, chances are you’re trying to figure out how your organization can make the most sense of its data. That’s great!

    At this point, you probably think that data virtualization sounds like the solution to your problems. But you might be concerned with the price tag that comes with it.

    Unlike many modern software solutions, data virtualization tools aren’t always available on a subscription basis. As such, end-users might not find them too appealing due to the perceived high costs associated with servers and software licenses.

    However, with the right solution, organizations will generate an impressive return on their data virtualization investment—negating this challenge altogether.

  • 4 Operating at scale

    When data virtualization solutions first emerged, many of them suffered from performance issues at scale.

    However, data virtualization has matured considerably over the last few years. Modern solutions can operate at high-performance levels at scale, regardless of network capacity and query complexities thanks, in large part, to caching functionality.

    Now that you’re familiar with the benefits and challenges of the technology let’s shift our attention to how, specifically, you can use data virtualization tools to move past your competitors, delight your customers, engage your team, and take your organization to the next level.

Data virtualization use cases

There are several ways you can put data virtualization tools to use at your organization. Here are some of the more common ones.

  • 1 Data integration

    One of the main reasons organizations invest in data virtualization is integrating all of their data in one place.
    From a customer’s perspective, this means that all relevant data can be leveraged to serve up personalized experiences and recommendations (more on this later).

    From an employee’s perspective, this means that all the data they need is always at their fingertips, accelerating workflows considerably.

  • 2 Big data and IoT analytics

    Of course, organizations also invest in data virtualization because it enables them to manage and analyze all of their data in one location, getting rid of data silos and duplicative information simultaneously.

    As a result, it’s much easier for all employees—including business analysts, developers, data scientists, and business intelligence professionals—to run big data and IoT analytics to make better decisions in less time.

    Companies can also use this functionality to implement agile BI workflows across their organizations.

  • 3 Data abstraction

    Data virtualization platforms can serve as a data abstraction layer that increases information access across the organization while ensuring accurate and secure data.

    In turn, the abstraction layer means that data is protected from any changes to IT infrastructure or architecture. Even if data is relocated, companies can rest comfortably knowing that their single source of truth persists.

  • 4 Logical data warehouses

    Thanks to data virtualization, employees can query data across several disparate sources—including traditional RDBMS, Hadoop, Tableau, NoSQL, SaaS services, and online storage platforms—in a manner that still feels “logical” since they only have to deal with a single interface to do so.

    Compare that to the old way of doing things, where employees had to relocate and even reformat data to perform similar tasks.

  • 5 Virtual data lakes

    Data virtualization enables organizations to unlock more benefits from their physical data lakes by adding a virtual layer on top of them to apply more context to all data easily. Since the virtualization layer can access and process data where it lives, duplicate copies of the same files and documents are not required.

    With virtual data lakes, employees don’t have to interact directly with back-end data, making all an organization’s data available to more employees—even those who might not be as technically proficient as the rest of the team.

  • 6 Data governance

    With more visibility into all of your data, it’s easier to enact governance and compliance policies—and ensure your team sticks to them.

    This reduces your exposure to penalties for non-compliance (e.g., GDPR and HIPAA) while increasing the security of your data and reducing duplicate and unnecessary data, bringing more efficiency to your network.

  • 7 Customer experience

    In an age where competitors are always just a few clicks away, customer experience is quickly becoming a key differentiator for businesses across all industries.

     

    Data virtualization helps organizations consistently improve the customer experience by ensuring companies can leverage all customer-specific information thanks to a 360-degree view of the customer. This makes it much easier to deliver personalized recommendations and experiences that today’s customers demand.