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.

7 Questions Every Data Scientist Should Be Answering for Businesses


Business professionals of all levels have asked me over the years what it is that they should know that their Data Science departments may not be telling them. To be candid, many Data Scientists operate in fear wondering what they should be doing as it relates to the business. In my judgment, the questions below address both parties with the common goal of a win-win for the organization: Data Scientists support their organization as they should while business professionals become more informed with each analysis.

What problem are we trying to solve?


It is important to be able to answer this question in the form of a sentence. Remember that the business end-user most likely does not use common terms like CV, logistic regression, or error-based learning in their everyday business routine. It does not help anyone when a Data Scientist hides behind fancy terms instead of providing actionable insight that moves the organization along. I can assure you that translating the Data Science jargon into something digestible for the business professional will create many allies. After all, a Data Scientist should have the primary skill of being able to transform complex ideas and make them readily understood.

Does the approach make sense?


In truth, this may be the single best question that benefits the Data Scientist even though it is asked primarily of the business professional. Learning to write out an effective analytic plan can have profound meaning. Writing is a discipline that should be embraced by the Data Scientist. It allows the Data Scientist to synthesize his or her thoughts. Although we live in a day and time where technology is at the center of everything we do, we should remember that technology, Data Science, and statistical computing are not replacements for critical thinking.

Does the answer make sense?


Can you make sense out of what you have found? Do you know how to explain the answer you have received? Your organization is counting on you to be the translation piece between the computer output and their business needs. Remember: computers simply do what they are told. As Data Scientists, we need to be sure we directed it to do the right thing. Validate that the instructions you gave it were the ones you intended. Be scientific in your approach, document your assumptions, and be sure you have not introduced bias into your work.

Is it a finding or a mistake?


Not everything is a Eureka! moment. So, make skepticism a discipline as a Data Scientist. One should always be skeptical of surprise findings. Experience should tell you that if it seems wrong, then it probably is wrong. Do not blindly accept the conclusion your data presents to you. Again, there is no substitute for critical thinking. Make absolutely sure you understand, and can clearly explain, why things are the way they are – whether a finding or a mistake.

Does the analysis address the original intent?


Unless you are surrounded by other Data Scientists in your organization, this question requires accountability to one’s self. You should be honest with yourself, always ensuring that you are not aligning the outcome with the expectations of the organization. It may be obvious to note, but it is critical to speak the truth of the data, realizing sometimes that the outcome does not align with the question the business is seeking to answer. However, if your analysis is essentially something unflattering to the organization, be sure you are 100% confident in your findings. In this situation, additional analysis is more important than less. Giving an analysis that does not reflect well on the business – and that is not well substantiated – may very well be your last.

Is the story complete?


We would agree that the best speakers, writers, and leaders are all good storytellers; it is no different for the Data Scientist. While storytelling is not the only way to engage people with your ideas, it is certainly a critical part of the Data Science recipe. Do your best to tell an actionable story. Resist the urge to rely on your business audience to stitch the pieces of your data story together. After all, your analysis is too important to leave up to wild interpretations. Take time to identify potential holes in your story and fill them appropriately to avoid surprises. Grammar, spelling, and graphics matter; your audience will lose confidence in your analysis if your results look sloppy.

Where would we head next?


As Data Scientists we should realize that no analysis is truly ever finished – we simply run out of resources. It is worth the effort for a Data Scientist to understand and be able to explain what additional measures could be taken if the business was able to provide additional resources. In simple terms, the business professionals you work with, at the very least, will need to have that information so they can decide if it makes sense to move forward with the supplemental analysis.


It is key to remember that Data Science techniques are tools that we can use to help make better decisions for an organization and that the predictive models are not an end in themselves. It is paramount that, when tasked with creating a predictive model, we fully understand the business problem that this model is being constructed to address – and then ensure that it does just that. These seven questions begin to form the bond of a stronger partnership between the data science department and the organization.