Do you need a #DataTherapist? 

A few weeks ago, I was  enjoying a lunch with some of my coworkers. We were discussing some of the use cases of Data Science and Business analysis that we are building for our various clients.

Somebody made the comment, that some use-cases don't need a data scientist they need a data therapist.

Many of us laughed, and I even made a Twitter comment about this.  A number of other people on Twitter not only retweeted the comment, but began to make comments about the application of a #DataTherapist to particular use-cases.

Here are a few recent definitions that have evolved in the Twitterverse related to the data hashtag: #Data.

My definition of Data Science: The application of Statistical and Mathematical rigor to Business Data. There should be the proper application of confidence intervals, and p-values to data when making decisions. When doing some type of predictive analysis this becomes even more important.  Data Scientists also do research with all of your business data to include even adding third-party data as a source of enrichment in order to best understand what has happened historically, and what is likely to happen given a set of conditions in the future.

When doing historical reporting, the analyst is reporting on facts that have occurred that the data represents. This is usually your Data Warehouse, Business Intelligence type data product. These things are repeatable, predefined business questions.

A Data Architect designs how an infrastructure should be built from the servers to the data model, and works with other Architects to ensure things are streamlined to meet the goals and objectives of the organization.

#DataOps, are the people that make the Data Architects vision happen. They not only keep the lights on, but also make it easy for the Data Scientist, Business Analysts, Application Developers, and all other manner of people that have a need to work with an enterprises Data Products to do so.

But what is a Data Therapist?

What started out as a "joke" may truly be something important.

So here goes. An initial definition of a #DataTherapist.

A Data Therapist is the person, or group of people that shows the way for what not only Could be done with an enterprises data, but what Should be done with the Enterprises data. They scope out data projects, and work with the executive team to clear the runways for the Data Architect, Data Scientist, Business Analyst and the DataOps team to get their job done.

Not all data problems require a Data Scientist.

Not all Data Science problems are Big Data problems.

Not all Big Data problems are Data Science problems.

Data, regardless of structure, use-case, application, or data product supported will continue to grow. The leaders in this space continue to push the limits on what can be done with Python, R, Spark, SQL, and other tools.

The Data Therapist shines a light on what can be done, and whether your organization should undertake a large scale data project. They also can tell you when you need to stop doing something and simply start anew.

Are you ready to listen to the #DataTherapist?

You may not always like what they have to say.

Please comment below about how you think a Data Therapist can help an organization.


  1. While this started as a humorous discussion over lunch, I am reminded of an instance of being asked to build something that I found unethical. "What" and "how" were Data Architect questions, "why" was what the the client intended to do with the data. Maybe the #DataTherapist answers the "why" questions?

    The #DataTherapist could fill the role of understanding behaviors and intent in building and using large data stores. This role would encompass uncovering insights from the data itself and how the organization acts upon it. A therapist helps a person work through their problems and pain points and helps discover core issues that the person may not even recognize. The goal of therapy is to become a better person. Perhaps a #DataTherapist does the same thing for companies?

  2. Great comment, Glen! I agree that understanding why something should be built is incredibly important. If everyone agrees as to the Why, then the how, when,who, and what should fall into place easily.