What Does It Mean to Be “Creative” as a Data Scientist?
By: Jesse Blocher
Tags: Problem Solving, Data Science, Measurement, Creativity, Philosophy

What Does It Mean to Be “Creative” as a Data Scientist?

Left brain, meet your right brain.

Photo by Jr Korpa on Unsplash

As a faculty member in a data science program, I interact a lot with students who want to build their skills, learn the profession, and succeed. To that end, they are great at building their professional networks and reaching out to data science professionals to better understand all the different ways in which someone can be a data scientist (a post for another day).

I also teach a course called Survey of Data Science Applications, and one of my goals for this class is to expose students to as many varied applications of data science to real world problems as possible. As such, we have frequent guest speakers from academia and industry talking about how they use data science in their field.

A topic that often comes up is, of course, some variation on “what does it take to succeed in data science?” and quite often the guest speaker or industry professional will say something like “creativity is really important.”

But what does this mean? Data science is a pretty left-brain kind of field, right? What do we mean by creativity? Finger painting? Well, I’m hopeless when it comes to art, but I think I’m pretty creative in data analysis and problem-solving. So let’s unpack this.

I see creativity showing up in at least three ways in data science: metrics, methods, and problem framing.

Metrics: Measuring the unmeasurable

Creativity in data science is so necessary because the data that you have is rarely the data that you want. Creativity is the skill required to bridge that gap.

For example, most firms would say that they want to improve customer satisfaction. What does that mean? If you could put the variable customer_satisfaction into your optimization algorithm, you’d be set…but there is no such thing.

Could you use sales data? Absolutely. If a customer purchases your product, that is a good indicator of satisfaction. However, a single purchase may not be all that helpful. What if you are a car dealer (or other durable good, like a sofa)? If someone buys a car or refrigerator or watch and then you never hear from them again, are they satisfied? Perhaps. Is that what you are trying to measure? Maybe.

It requires creativity and continual improvement to understand how to measure the unmeasurable.

Let’s say you are a consumer goods company, one that sells laundry detergent. Is a satisfied customer one who buys your product once every month? How do you know? Maybe it is just inertia and they are one clever ad from a competitor away from switching.

What if you work at a nonprofit, one that helps the homeless? Should your main metric be “number of neighbors served”? Perhaps not — more homeless is not better, right? The goal should be zero homelessness. But you can’t aim for zero either.

This is just scratching the surface. How to you measure employee satisfaction? How do you measure “potential customers” who may soon engage with your product but have not yet even come to your website or entered your stores? You cannot avoid these questions and yet few of them have obvious answers.

Methodology: using your whole toolbox

When discussing this in class, one of my students made an excellent point. He noted that understanding the mathematics behind the algorithms and statistics was very important when deciding what approach to use when solving a problem. This is absolutely true. Creativity is not just using the right tool for the job, but knowing how to adjust a problem just a little bit to make your approach deliver much better results.

To be creative and successful in data science is more than simply learning all of the scikit-learn methods and tuning parameters. Why do those algorithms work? What problems do they work best on? Why might a decision-tree approach work better than a linear regression approach? When is boosting useful and when is it a waste of time? Even if you are more inclined to be a machine learning engineer (one who applies algorithms, not designs them), you should spend time learning algorithms, optimization methods, and the math behind all of it. It will make you much better at your job in the long run.

If you are more of a stats-oriented data scientist, what do you do when your data is non-normal? When should you use the level (users) versus a rate (user growth) versus a ratio (active users/total users)? What do you do if you data isn’t randomly distributed? Not everything is a simple A/B test — indeed I suggest to you that most of the important questions are hard to place in a simple control vs treatment experimental setup.

Further, setting up the statistical test correctly is often the hardest part — what is the right null hypothesis? What, then, is the right alternative hypothesis? In stats classes (in person, online, all of them) these are typically given to you. In real life, you are the one coming up with them and this may be the hardest part of the job. Remember: switching the null and alternative hypothesis can yield very different outcomes, so knowing which is which is vital.

(Side note: I was struct by this article in Fortune magazine (possible paywall) on the backstory behind DeepMind’s new protein folding AI breakthrough. First, they talk about how they had to customize algorithms — off the shelf was not sufficient. Second, they talk about alternating between pushes for optimal performance and emphasizing creativity, where alternative approaches (that may reduce performance, sometimes by a lot) are thoroughly explored. It reminded me of how genetic algorithms do optimization)

Being creative means understanding what the overall goal of the analysis and choosing the right test, algorithm, or methodology to solve the problem at hand.

Problem framing: asking the right question

If you work in a very large company with a mature data science function, this third concept of creativity is less important, at least early in your career. If you work at Netflix doing A/B testing on movie artwork, for instance, the question you are answering has been given to you.

However, an awful lot of firms are still building out their data science function and many, many business folk (non-data science) have no idea what data science can do. Thus, they are inclined to ask irrelevant or unanswerable questions. In this case, being creative is working with your business partners to identify what their real goal is.

For example, perhaps a product manager wants to assess the success of a marketing campaign. They suggest that they launch the campaign in two weeks and that you, the data people, should see if product sales rise afterwards.

There are a lot of problems with this scenario. What is the goal here? Is it to increase sales? There are possibly many ways to do that besides marketing. Is it to assess the efficacy of a specific marketing technique? If so, is looking at sales data the most precise way to do that? Does the business care mostly about outcomes? If so, then a more machine-learning approach may work, because it is best at making accurate predictions. Or, does the business care mostly about decisions — i.e. what marketing campaigns work best in driving future revenue? This is more of a statistical inference question around cause and effect that requires a statistical approach.

You may not be able to answer all of these questions, but you can move the conversation in the right direction and help answer them collaboratively with your team.

Creativity is often probing your business partners to understand their real problem, which is often hard to put in data science terms. If you can regularly bridge that gap, you will have a long a prosperous career.

How do you develop creativity?

This is perhaps the hardest part. Developing creativity essentially means growing your imagination. The hardest part of all of these problems I’ve described is simply imagining what else could be done besides what is right in front of you. We, as humans, all have an anchoring problem — once we see something, be it a metric, or an algorithm, or a problem statement — it is hard to see alternatives.

The best way I’ve found to develop creativity is simply experience.

Experience? For many of you, I bet this answer is a letdown — you need creativity to get experience and also experience to develop creativity. So, other than getting a job and working for a decade, here are some suggestions of what you can do now:

1. Read

I don’t mean read API documents or a post on tuning XGBoost. I mean read case studies. Read about how firms are implementing analytics, statistics, and AI. Read the newspaper about firm initiatives and experiences with data. Read about problems they are having. This is easier if you are at a school that has subscriptions — our business school has a lot of industry publications with high quality research on latest trends, issues, and challenges. A lot of this requires subscriptions because you often need to pay for high quality information.

2. Take theory classes

If you are methodologically inclined, be sure you understand the philosophy of statistics, and not just the code. What exactly can you do with statistics? What problems does it solve? When does it fail? Take more theoretical math classes that explain why algorithms work and how/why they might fail. What are they best at? Worst at? Why?

3. Volunteer

And I don’t mean just volunteer to write code. Volunteer for something that is outside your comfort zone. There are a myriad of social problems in our world today that involve very diverse groups. Practice listening and empathizing with people who are different than you. Practice seeing the world through someone else’s eyes. A big part of creativity is simply seeing differently.

4. Do projects

Yes, of course. You’ve heard this already so I won’t say much more. Here is my twist: start by thinking about a problem you are interested in, don’t start with a data set. Then, once you are focused on a problem, think through (and write down) what you need to measure and how you’d like to solve it. Then, it is time to start looking for data, and you’ll soon find that you can’t get what you need. Now what do you do? How do you get close to what you want with what you can get? This way, you’ll be practicing a more ‘real-world’ project. This is about much more than just practicing writing code and getting a result. Keep a journal along the way so you can recall your thought process. Maybe do it with a partner so you can talk through it with each other. Data science is (should be) a team sport.