Data Analytics As Your Trusted Advisor: Trust, But Verify


In a recent article on the Performance Ideas blog, which focuses on best practices for the business analytics field, the author presented an article describing the typical trajectory and cycle of an organization adapting analytics into business decisions for the first time.

The introduction of analytics to the making of business decisions is compared to bringing a new advisor into the decision process. The trajectory they identify is for the new data consuming client to move from rejection to being embraced, similar to how a consultant can go from interloper to trusted advisor:

Reject: Can I trust the data? What am I supposed to do with it?

Accept: I can see the value but I can’t identify the stories

Embrace: This is cool! What else can I do with this?”

While that general path is accurate of the steps required to confidently act on the results generated by an analytics solution, it is important to note the danger of considering the Embrace stage to be the end of the story.

Although organizations that consume data must trust their models in order to incorporate the results into business decisions, there are a few reasons why they should maintain a degree of skepticism that leads to continued vigilance.

On The Lookout

Once the analytics solution is in place and has been embraced as a key portion of decision processes, the business needs to continue testing the assumptions and parameters. They should constantly be poking and prodding to ensure that their solution either:


  • Performs according to their model of their business environment, or
  • Proves to them that their model of the business environment itself needs to be adjusted.


It takes time and good context for the usefulness of data to be revealed. While the analytics solution must be embraced and applied with confidence, that confidence must be supported by continual observation and occasional adjustment.

Testing The Waters

Perhaps the best approach for the introduction of an analytics solution is to make sure it is approachable and usable. If the output is overwhelming and difficult to process, the trajectory moving from Reject to Accept will be elongated and carry greater inertia.

If the data makes the decision makers’ job easier and less complicated, their reluctance will be more easily overcome. If instead they are presented with hundreds of charts each morning to examine, the path to accepting and embracing the analytics will be far more cumbersome.

By having the analytics start with taking a small chunk of information and improving the way it is used, decision makers can test the waters and ease themselves into incorporating the data into their processes.


It is natural for organizations to show some reluctance to change how decisions are made when introducing a new analytics solution. The Reject -> Accept -> Embrace trajectory is an accurate depiction, but the full picture of a successful introduction includes some degree of testing the waters in the early stages, and ongoing verification and observation after the analytics have been embraced.


Data Analytics As Your Trusted Advisor: Trust, But VerifyJAN. 24, 2013