Implementing Data Virtualization for the First Time

Implementing Data Virtualization for the First Time

Data virtualization is a powerful data integration and data management approach, but if you are implementing data virtualization for the first time, it helps to know how to overcome some typical challenges. In this post, I’ll outline some of the challenges I experienced when we implemented data virtualization for the first time, along with some tips for navigating such issues.

Taking Inventory Before You Begin

Data virtualization might seem daunting from a security, resource, and data governance perspective.

It helps, at the requirements phase, to perform a simple maturity assessment outlining where your organization is right now, where you want to get to, and how you want to get there. Be sure to include a technology horizon scanning exercise, which will help to clarify all the options available, so you can determine exactly what is right for your organization, with regard for all of your areas of concern.

Identifying the Necessary Skills

Marcus Blosch, research vice president at Gartner, says that a talent gap is one of the top barriers to digital transformation, and the same might be said about implementing data virtualization for the first time. According to Blosch, “There are two approaches to breach the talent gap — upskill and bimodal…In smaller or more innovative organizations, it is possible to redefine individuals’ roles to include more skills and competencies needed to support digital. In other organizations, using a bimodal approach makes sense by creating a separate group to handle innovation with the requisite skill set.”

Your maturity assessment will help to identify the skills you have and those you need. In general, if your organization is moving from a traditional data warehouse to a cloud-based technology, you will most likely need to invest in re-training. This is also your opportunity to build in self-service, powered by emerging artificial intelligence/machine learning (AI/ML) technologies, as this is a way to enable more output with existing teams.

Meeting stakeholder Needs

Before you begin the implementation, take a step back and consider all of your potential stakeholders and sponsors. The benefits of a long list are twofold: Extended rewards, and reduced risk of excluding individuals who could stop a project in its tracks.

Gartner identifies three critical steps in this process:

  1. Identify the Most Senior Person You Can Engage With
  2. Create a Common Language Based on Your Organization’s Key Business Goals
  3. Be Realistic About Your Organization’s Data-Driven Ambitions

Moving Forward

Implementing data virtualization is considerably easier and more straightforward than many other implementations, especially those that involve new hardware, which is not often the case with data virtualization. Still, a data virtualization project will go much more smoothly if you overcome each of the three challenges above.

I’ll close with my short-list of 5 best practices for implementing data virtualization:

  1. Before you begin, identify where you are, where you want to get to, and you think you’re going to get there. If you don’t know the answers, talk to others who have done this before.
  2. Don’t be afraid of the future. Chances are, the technology and approaches have evolved to address your exact situation.
  3. Hearts and minds are ultimately more important than technology, so engaging with stakeholders should have precedence over choosing the technology. Data management, data cleansing, and data classification are all important aspects of the implementation, and they will require ownership from key business stakeholders, regardless of your chosen technology.
  4. Sponsorship from the top makes all the difference. Don’t underestimate the power of a top executive walking into a sales meeting using business intelligence gathered from your data virtualization project.
  5. Data governance and security are key; data is gold, so it should be treated with respect.
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