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Top 3 data virtualization use cases

Although still gaining momentum, data virtualization is on a fast track to address the challenges with traditional integration solutions, namely faster time-to-market for data and business capabilities, access to a broader range of data across your data ecosystem, and providing an integrated solution for management and governance.

Data virtualization enables access to data without replicating or storing any of the data. It’s essentially a virtual layer of information that allows you to access and integrate data from various sources and systems seamlessly.  

But what are the typical use cases of data virtualization, and what are some of the challenges businesses encounter when trying to put it in place? Here we’ll dive into both questions, along with the potential opportunities data virtualization provides. 

Common uses of data virtualization

Introducing data virtualization into an organization is generally use-case-driven. If your company fits into one of the following three use cases, it may benefit from implementing this type of holistic strategy. 

You have data in numerous locations 

The primary use case for data virtualization occurs when companies have data in multiple locations.  

For instance, if your business has migrated data to the cloud or multiple cloud locations, but still has data on-premises, virtualization can pull all that data into one access point.  

Virtualization is a great candidate to make siloed information look united to the business, even when the data exists in separate environments. But virtualization doesn’t just affect appearances: it also makes data from disparate sources simpler to access, which benefits the users.  

For instance, many companies collect and store customer data in multiple platforms, which can make it difficult for the organization to discern a true 360° view of the customer. Data virtualization can seamlessly integrate the data across platforms to present a single, unified view — saving time over manual analysis and reducing the risk of key data points falling through the cracks.

Learn more about customer data strategy >> 

You’re trying to migrate to the cloud 

At this point, most companies are trying to modernize and move to the cloud to save money and time. But not all companies can move the data that quickly and completely abandon the legacy system. Instead, they migrate data a little bit at a time, which can be a tedious and lengthy process.  

Data migration projects can take months, and during that time, business users could spend significant time finding, reconciling, and analyzing the data manually.  The result is a considerable loss of business opportunity; unable to respond to the needs of the business for data. 

Virtualization bridges the gap during this transition period, streamlining effort and boosting efficiency in the short term, so data can be migrated over time without negatively impacting business users.

Learn more about cloud migration strategy >> 

You want to move from a DWH to a DaaS model 

Some companies prefer to bypass putting data into their traditional data warehouse (DWH) in favor of a data-as-a-service (DaaS) solution. For these organizations, the time-to-value savings of getting data into the hands of users more quickly overrides the case for standing up and maintaining their own DWH. 

Data virtualization enables companies to bypass the need to create ETL processes entirely and serve up unified data views from any combination of DaaS and legacy sources. As long as your organization has thoughtful governance in place and has considered the potential privacy and security impact, data virtualization can quickly harmonize a DaaS strategy.

Learn best practices for evaluating your data storage options >>

Common roadblocks to data virtualization 

If you’re considering data virtualization for your business, there are some potential constraints to consider.

  1. Incomplete MDM framework. If your company has outstanding master data challenges to address, it is best to have a master data strategy defined and solutioning options incorporated into the data architecture before data virtualization can take full shape. Mastering data is often a process and organizational change management problem to solve.   
  2. Disbursed subject matter expertise. Creating a data virtualization solution requires thorough knowledge of your data, the business rules surrounding your data, and a strong understanding of the business needs for using that data. Since data virtualization brings disparate data together, the subject matter expertise on the various data domains can be spread throughout the organization. Identifying these SMEs and ensuring engagement of these individuals is a key enabler to achieving success with data virtualization.
  3. Governance issues. You never want to overlook governance in a rush to meet business requirements. Accountability and ownership of data are essential tenants of a successful data management framework. Before implementing a data virtualization project, be sure you have a solid governance operating model in place to ensure security, compliance, and data quality.  

Although data virtualization can be a transformative solution for many companies, it’s not your only option. Sometimes the use case isn’t quite there, or privacy and governance concerns outweigh the potential value of a data virtualization effort. Fortunately, there are multiple ways to realize the value of your data.

Explore your data integration & architecture options >>

Setting yourself up for data virtualization success 

Data virtualization can be an excellent solution for businesses struggling with integration challenges that are preventing the speed and scale of business growth. Collecting data from multiple platforms and presenting it in one unified view for business users streamlines workflows and makes data easier to use and digest across the organization.  

It’s not a one-size-fits-all solution, but for certain use cases, data virtualization offers significant value. How do you determine if data virtualization is a good fit for your business? How can you define and evaluate potential use cases to understand the potential pitfalls and weight them against an accurate projection of benefits? 

In our Data Virtualization Discovery Workshops, expert teams walk you and your key stakeholders through your unique constraints and opportunities, identifying the right next steps to advance your data management strategy. Starting with your current state architecture and building toward a true 360 view of your data, we’ll work with you to determine if data virtualization is a good fit, and which use cases will help you realize its value for solving some core business problems.  

Have a question about data virtualization? Ask us anything >> 

Ready to get started? Explore our Data Virtualization Discovery Workshops >> 

About the author

Mike Mappes

Mike Mappes is a Senior Strategic Data Management Consultant in the Data Practice at Fusion. Mike helps lead teams that assess a company's data management portfolio. He then leads strategy engagements to help companies advance their programs across the disciplines of analytics and governance.

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