How Is Knowing the Business Important to Data Science?



Businesses around the world are involved in a multitude of projects at any given time. As Data Scientists come into the business fold, it becomes more important with each passing day to have both parties – “the business” and “the Data Scientist” – begin to define successful strategies of working together. Businesses are having to become aware of the techniques and methods of a Data Scientist in order to maximize their analytic investments; and, simultaneously, Data Scientists are having to learn how to be relevant to an organization that is in a constant state of change. From a business perspective, knowing what to expect of a Data Scientist and having that Data Scientist develop a reasonable Data Science workflow can create huge competitive advantage over other companies who are lost at the “Data Science Sea.”

Our Business Conditions, Today


Performing a bit of journalistic investigation into the organization’s business situation will help provide a Data Scientist with the necessary context for their Data Science project right off the top. Getting background facts on the business will help the Data Scientist know what he or she is getting involved in – in the truest sense. This may not be obvious to the Data Scientist at first, but learning background facts about the business helps to uncover details that will round out one’s understanding of what the business has determined it needs as it relates to the Data Science project. Through this process, information on identifying resources most certainly bubbles to the surface. The takeaway: even if a Data Scientist has worked at the organization for years, this critical step should not be skipped. The business background is a dynamic concept that speaks to the circumstances or situation prevailing at a particular time – it should not be looked at as part of a one-and-done process. Data Scientists should be careful not to fall into the trap of believing that nothing has changed since the last Data Science project.

 It Doesn’t Matter What the Business Wants – I Can Model Anyway!

Many Data Scientists forget the essential step of learning about the business from the business’s perspective. Since the business is the customer of the Data Scientist, this can be easily boiled down to “What does the customer truly want to accomplish?” This simple but straightforward question may seem frivolous to an inexperienced Data Scientist, but getting at what the business objectives are for any Data Science project will create a necessary roadmap for moving forward. The fact of the matter is that most businesses have many competing objectives and constraints that have to be properly balanced in order to be successful on a day-to-day basis. As the Data Scientist, one of your primary aims in ensuring a successful Data Science project is uncovering important, possibly derailing factors that can impact outcomes. Data Scientists should not advance the project workflow on the basis of their analytic talent alone, but rather take the time and necessary steps to learn the business objectives; otherwise, a Data Scientist runs the risk of being seen as a rogue employee with irrelevant results. At the end of a Data Science project, everybody can see clearly when a Data Scientist has come up with the right answer to the wrong problem. A Data Scientist with half the analytic skill can be more effective to an organization than a Data Scientist who squeezes every last bit of information gain from a dataset, but does not know how to frame the business problem.

 What Do You Mean I Missed The Target?

As a Data Scientist who operates in business, you should want to know what it takes for your Data Science project to be successful. However, this cannot be only about the evaluation of predictive models or how a Data Scientist designs experiments, but in addition to how the business will judge success. Learning how to frame up the business success criteria in the form of a question – and whether the criteria will be judged subjectively or objectively – will help a Data Scientist pinpoint the true target. An example of a business criterion that might be specific and measurable objectively would be “reduction of patient readmissions to below 19%.” An example of a business success criterion that is more subjective would be something like “gives actionable insights into the relationship of the data we have.” However, in this later case, it only makes sense for the Data Scientist to ask who is making the call on what is useful and how “useful” is defined. Bottom-line: if Data Scientists do not know what the business success criteria is for a Data Science project, they have already failed before the project has begun.


Having a solid business understanding about a Data Science project will prove to be valuable for both the Data Scientist and the business. Real-world Data Scientists should not operate as an island. In reality they need to learn to speak many languages beyond Python, R, and Julia; they should also learn to speak “business.” The better a Data Scientist can understand the business milieu, the business objectives, and how to measure the success of a Data Science project in the eyes of the business, the more effective a Data Science will be for an organization.