What do kids do when they play in a sandbox? They use mini loaders to fill toy dump trucks with sand. They make mud cakes and build castles. All the while, they are experimenting and learning. What happens when you put heavy rocks in the loader's bucket? How tall can you make your castle before it topples over?

The same is true for BI professionals using analysis sandboxes.

I recently spoke with QlikTech technical advisor Elif Tutuk about analysis sandboxes and have some of her insights to share with you. Elif is a consultant who spends her days with QlikView customers, helping them get the most they can out of their QlikView deployments.

An analysis sandbox is an environment in which power BI users-data-savvy users with strong query and database skills and a solid understanding of the business and business processes-experiment with data, explore analysis, and conduct ad hoc questioning. They can also create prototype BI applications without negatively affecting performance of back-end data sources or the production BI environment. They use these environments to explore enterprise data, combine it with local and external data, and then massage and package the resulting data sets.

Analysis sandboxes can be used to answer urgent, ad hoc questions

Depending on the degree to which the decision makers are tech-savvy, they may even be able to use the sandbox to answer questions themselves. Consider these scenarios:

  • Revenue variance analysis. A general manager receives a monthly revenue statement and sees that revenue is less than expected. She assigns a business analyst to go away and figure out the factors contributing to the variance. Because the question is not a routine business question, the analyst uses an ad hoc information source and does his or her best to find the root cause of the revenue variance with limited time and information available.
  • Quick-reaction decision-making. In a competitive, dynamic business environment, it's not uncommon for an executive to have a 4-hour window in which to make a decision. He may need help from a business analyst to support a quick decision with evidences in the form of data. Without an analysis sandbox, the business analyst may have a hard time finding answers-especially if finding answers requires melding data from multiple sources, and required crunching through a huge volume of data quickly.
  • Customer buying trend analysis. Let's say a management team is talking about why some customers stop buying a particular product after a few months. Perhaps it's not possible to answer this question using higher-level, readily-available data. So the power user has to go into demographic data and purchasing data, and maybe analyze the competition's promotions during a particular period of time. The power user would go to an analysis sandbox to do some exploratory analysis in search of answers.

What these scenarios all have in common is a burning need for fast business answers, in an ad hoc manner. An analysis sandbox is important because the longer it takes to find the root cause of a problem, or the answer to a burning business question, the greater the cost to the organization.

We've identified several ways organizations can set up analysis sandboxes. We'll explore these, and discuss a good practice for using QlikView for analysis sandboxes, in upcoming posts. Stay tuned!