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7 Questions Every Data Scientist Should Be Answering for Business

 

 

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. 

Summary

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.  

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Comment by Wes Sauder on October 6, 2015 at 5:27am

Six = 6 :)   

Great Stuff.  Sometimes it feels like you're adding too many steps when you are starting a project, but discussions like this confirm that we're not crazy when you pull back to the fundamentals - something as simple as 'stating the problem you are trying solve' is so often missed.  Good Post!

Comment by Daimon Brown on September 21, 2015 at 6:43am

I like the post very interesting. Would be good if the writer took heed of is own advise though, he quotes "Grammar, spelling and graphics matter; your audience will lose confidence in your analysis if your results look sloppy". There are 2 spelling mistakes and poor grammar in his opening paragraph? 

Comment by Savita Kirpalani on September 18, 2015 at 4:47am

Very well said Damian.

Does the analysis address the original intent? Is a very important question a data scientist should be answering, it is also a very tricky one to address.

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