Asking Great Questions as a Data Scientist
We outline the importance of asking yourself the questions you need to ask to effectively produce something that the business wants. Once you start asking questions, it’ll become second nature and you’ll immediately see the value and find yourself asking even more questions as you gain more experience.
Asking questions can sometimes seem scary. No one wants to appear “silly.” But I assure you:
- You’re not silly.
- It’s waymore scary if you’re not asking questions.
Data Science is a constant collaboration with the business and a series of questions and answers that allow you to deliver the analysis/model/data product that the business has in their head.
Questions are required to fully understand what the business wants and not find yourself making assumptions about what others are thinking.
Asking the right questions, like those you identified here is what separate Data Scientists that know ‘why’ from folks that only know what (tools and technologies).
We’re going to answer the following questions:
- Where do we ask questions?
- What are great questions?
I had posted on LinkedIn recently about asking great questions in data science and received a ton of thought provoking comments. I will add a couple of my favorite comments/quotes throughout this article.
Where do we ask questions?
Basically every piece of the pipeline can be expressed as a question:
And each of these questions could involve a plethora of follow up questions.
To touch the tip of the iceberg, Kate Strachnyi posted a great assortment of questions that we typically ask (or want to consider) when scoping an analysis:
Few questions to ask yourself:
How will the results be used? (make business decision, invest in product category, work with a vendor, identify risks, etc)
What questions will the audience have about our analysis? (ability to filter on key segments, look at data across time to identify trends, drill-down into details, etc)
How should the questions be prioritized to derive the most value?
Who should be able to access the information? think about confidentiality/ security concerns
Do I have the required permissions or credentials to access the data necessary for analysis?
What are the different data sources, which variables do I need, and how much data will I need to get from each one?
Do I need all the data for more granular analysis, or do I need a subset to ensure faster performance?
Kate’s questions spanned both:
- Questions you’d ask stakeholders/different departments
- Questions you’d ask internally on the data science/analytics team.
Any of the questions above could yield a variety of answers, so it is imperative that you’re asking questions. Just because you have something in your mind that is an awesome idea for approaching the problem, does not mean that other people don’t similarly have awesome ideas that need to be heard an discussed. At the end of the day, data science typically functions as a support function to other areas of the business. Meaning we can’t just go rogue.
In addition to getting clarification and asking questions of stakeholders of the project, you’ll also want to collaborate and ask questions of those on your data science team.
Even the most seasoned data scientist will still find themselves creating a methodology or solution that isn’t in their area of expertise or is a unique use case of an algorithm that would benefit from the thoughts of other data subject matter experts. Often times the person listening to your proposed methodology will just give you the thumbs up, but when you’ve been staring at your computer for hours there is also a chance that you haven’t considered one of the underlying assumptions of your model or you’re introducing bias somewhere. Someone with fresh eyes can give a new perspective and save you from realizing your error AFTER you’ve presented your results.
Keeping your methodology a secret until you deliver the results will not do you any favors. If anything, sharing your thoughts upfront and asking for feedback will help to ensure a successful outcome.
What are great questions?
Great questions are the ones that get asked. However, there is an art and science to asking good questions and also a learning process involved. Especially when you’re starting at a new job, ask everything. Even if it’s something that you believe you should already know, it’s better to ask and course-correct, than to not ask. You could potentially lose hours working on an analysis and then have your boss tell you that you misunderstood the request.
It is helpful to also pose questions in a way that requires more than a “yes/no” response, so you can open up a dialogue and receive more context and information.
How we formulate the questions is also very important. I’ve often found that people feel judged by my questions. I have to reassure them that all I want is to understand how they work and what are their needs and that my intention is not to judge them or criticize them.
I’ve experienced what Karlo mentioned myself. Being direct can sometimes come off as judgement. We definitely need to put on our “business acumen” hats on to the best of our ability to come across as someone who is genuinely trying to understand and deliver to their needs. I’ve found that if I can pose the question as “looking for their valuable feedback”, it’s a win-win for everyone involved.
As you build relationships with your team and stakeholders, this scenario is much less likely to occur. Once everyone realizes your personality and you’ve built a rapport, people will expect your line of questioning.
Follow up questions, in its various forms, are absolutely critical. Probing gives you an opportunity to paraphrase the ask and gain consensus before moving forward.
Follow-up questions feel good. When a question prompts another question you feel like you’re really getting somewhere. Peeling back another layer of the onion if you will. You’re collaborating, you’re listening, you’re in the zone.
The main takeaway here is that there are a TON of questions you need to ask to effectively produce something that the business wants. Once you start asking questions, it’ll become second nature and you’ll immediately see the value and find yourself asking even more questions as you gain more experience.
Questioning has been instrumental to my career. An additional benefit is that I’ve found my ‘voice’ over the years. I feel heard in meetings and my opinion is valued. A lot of this growth has come from getting comfortable asking questions and I’ve also learned a ton about a given business/industry through asking these questions.
I’ve learned a lot about diversity of viewpoints and that people express information in different ways. This falls under the “business acumen” piece of data science that we’re not often taught in school. But I hope you can go forward and fearlessly ask a whole bunch of questions.
Bio: Kristen Kehrer is the founder of DataMovesMe, with the following areas of expertise: Time Series Analysis, Forecasting, Cluster Analysis, Segmentation, Regression Analysis, Neural Network models, Decision Trees, Text Analysis, Full Factorial MVT, Survival Analysis.
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