Analytic Sandboxes and Data Virtualization

Analytics – Opportunities and Challenges

I have spent a number of years in the BI space including time with innovators including Business Objects and EMC. In my opinion, the opportunity for business people to perform new types of analysis to gain greater insight into their business and customers has never been greater.

But enterprises are flooded with a deluge of data about their customers, prospects, business processes, suppliers, partners and competitors. Further this data is spread across analyst desktops, data warehouses and marts, transaction systems and the cloud.

How can business analysts cope with this massive data challenge?

Best Practice Solution: Analytic Sandboxes

I think analytic sandboxes are a key piece of the solution.

Rick Sherman, in his white paper Analytics Best Practices: The Analytical Sandbox  describes analytic sandboxes in the following way:

“The goal of an analytical sandbox is to enable business people to conduct discovery and situational analytics. This platform is targeted for business analysts and “power users” who are the go-to people that the entire business group uses when they need reporting help and answers.”

And while the concept is not new, the traditional approach to analytic sandboxes have often resulted in “shadow IT systems” and “spreadmarts” that constrain analytics development, and the business benefits that result.

Key Requirements

Rick complements his sandbox definition with a select set of best-practice design principles. Here is a summary of his list.

  • Data across the enterprise needs to be accessible and timely
  • Time-to-solution must be fast and disposable
  • The business analyst needs to be “in control”
  • Sufficient infrastructure must be available for conducting business analytics
  • Solutions must be cost- and resource-effective

Data Virtualization as an Analytic Sandbox Enabler

Data virtualization addresses the key data challenges business analysts face when creating a new analytic solution, while avoiding many of the downsides of shadow IT systems and spreadmarts.

  • Discover available data sources across and beyond the enterprise.
  • Simply access required data sources, while complying with security and governance policies.
  • Integrate needed data sets in a physical, virtual, or hybrid sandboxes.
  • Allow data in the sandbox to be accessed by any number of analytic tools.
  • Execute and refine all the activities above quickly and easily.
  • Optionally share data sets and sandboxes with other analysts.

What are your Analytic Sandbox Best Practices?

We have seen Rick Sherman’s analytic sandbox best-practice design principles and my list of how Data Virtualization can support them.

What do you think? Are the principles the right list? Is data virtualization the right solution?  Let’s talk!

2 Responses to Analytic Sandboxes and Data Virtualization

  1. Santhosh Nair says:

    I used Analytic Sandboxes using data virtualization for my current company and previous one. Created the workflow to request, deploy and terminate the sandbox as required. This is how our Analysts capture the requirements and bring it back to data integration team for real deployment.

  2. Here is the question for us – The users today are used to querying tables in their native state and structures for analytics and discovery using the SQL Flavor that they are most comfortable with. When we virtualize, there is the need to learn the Composite flavor of SQL and the manner in which they query native tables will have to change as they must not be the best way to interact with data in a virtualized environment. There more times than not a resistance to learning new SL flavors.

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