Avoiding Analytics Shelfware

Shelfware is software that goes unused or underused after purchase.

This blog is about sharing ideas and experiences that you can use to ensure that your Business Intelligence software doesn’t turn into shelfware.

Having made countless software sales, I have seen my share of clients who ended up not using the software they purchased. Or, more commonly, where the benefits they were expecting did not materialize.

On the other hand, I have seen clients achieve spectacular results from their Business Intelligence investment. They have created a facts-driven culture, and have introduced new products, increased sales, and made dramatic improvements in service quality that wouldn’t have been possible if they were simply crunching numbers in Excel.

Avoiding analytics shelfware requires due diligence before the software is purchased, and a commitment after the purchase has been made.

If the investment amount is considered significant by your organization, then the points below are very applicable.

At a high level, here are a few recommendations. These will be covered in more detailed in other blog posts on buying Tableau and other analytics software:

Before Purchase:

  1. Identify an executive sponsor (if you want to go beyond a handful of users of Tableau Desktop)
  2. Write your software selection criteria
  3. Look at at least one other data visualization product (there are over 100!)
  4. Get the input of those who will be doing the data analysis
  5. Get the input of those who will be consuming the data analysis results
  6. Allocate time and money for training, prototyping, and learning by doing
  7. Start small, and create momentum by demonstrating compelling results
  8. If your data is not already accessible and organized, involve the IT team as early as practical

After Purchase:

  1. Look for evidence that the dashboard consumers are getting more value
  2. Look for evidence that the data analysts have achieved new capabilities
  3. Find out what roadblocks stand in the way (More training? Cleaner data? More user involvement? Not enough time to learn and build? …)
  4. See if the backlog of requests for the analysts is meaningful (if the analysts don’t have much on their plate, you might be looking at shelfware)
analytics shelfware

Analytics Shelfware