Historically, businesses have turned to data analysis to understand more about their business operations and determine what practical actions they can take to improve performance. Organizations have often found that such analysis raises as many questions as it answers, fuelling more analysis but leading to little change that delivers true business value. The inability to make this analysis truly actionable creates the challenge of analysis paralysis.

This phenomenon has discouraged many businesses from becoming more data-driven and has slowed the optimization of business processes as a result. The barriers to making analysis actionable have included the following factors:

A lack of quality, relevant data to generate insights that look credible
The skills needed to interpret the data and translate the analysis into meaningful outputs
When data models are built, the overhead associated with converting the analytical code so it can run automatically on a database and adding database fields to hold the model outputs and modifying the UI to accommodate them