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No, Everyone Doesn't Need to Understand Analytics

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The breadth of analytics has certainly increased in recent years. So, too, has the pool of people who dip their toe into creating analytics of one sort or the other. The trends toward democratization of data and self-service analytical capabilities are powerful and both have driven a lot of value for organizations in recent years. At the same time, it is possible to go too far. I get concerned when I hear the suggestion that everyone in the organization needs to create, use, and understand analytics. Many people don’t (and shouldn’t!) understand analytics at all.


There are absolutely people within an organization who must understand how analytics work. Many of those people already have the need and the skills to create their own analytics. In most companies today, the number of people who have analytical toolsets of some kind and data access beyond standardized reports is growing rapidly. However, in most cases, it is still a relatively small number as a percentage of all employees. This is how it should be.

The fact is that many people have no training in, understanding of, or interest in analytics. It makes no sense to try to get them deeply involved. A few examples of the types of employees who shouldn’t be expected to understand analytics include:

  • Front line employees such as servers, cashiers, or call center agents

  • Administrative functions such as executive assistants or travel agents

  • Behind the scenes support teams such as mechanics, maintenance, and security

  • And more

People in the types of roles above often have no relevant background to enable the successful creation of analytics processes or even the understanding of how processes that others have created actually work. They have other things to focus upon if they are to be successful in their jobs.


To say that many people in an organization don’t need to create or understand analytics is not the same as saying that they should not make use of analytics. For example, a very sophisticated algorithm might feed a call center agent offers to give to callers. But, the agent does not need to understand how the offers are created. The agent must simply deliver the offers. A travel agent need not understand all the optimization that goes on in the system in order to prioritize flight options. The agent must simply book the right choices for each client. A mechanic doesn’t need to understand how IoT data feeds predictive maintenance models to flag a needed repair. The mechanic simply needs to make the repair.

The point is that the goal should be to permeate analytics throughout an organization and to allow it to impact virtually every employee and business process. But, it is perfectly fine to have many people unaware of the effort, complexity, and theory behind those analytics. As long as they change their behaviors and take action as the analytics suggest, success can be achieved. The secret is to enable such people to take advantage without having deeper understanding of the underlying analytics.


At a very high level, this issue can be broken down to the distinction between analytics creators and analytics consumers. There are degrees of sophistication within those high-level segments, but one of the biggest sub-segments will usually be highly unsophisticated analytics consumers. For those employees, the goal should be to provide simple, prescriptive actions based on analytics. The goal should not be for them to understand what is under the hood or to have them creating their own analytics. Pushing people too far up the sophistication scale will not only waste a lot of resources, but will lead to a range of issues from employee frustration to bad decisions.

In the end, focus on making sure that analytics are disseminated to employees who are unsophisticated consumers in a way that they can make use of the results and take action. Let them focus on what they do best in their day to day job and let your analytics creators take care of the heavy lifting.