Learn the business to become a great Data Scientist

The core of every Data Scientist (Sorry, it’s not the coding)


Cornellius Yudha Wijaya

3 years ago | 5 min read

A lot of aspiring Data Scientists think what they need to become a Data Scientist is :

  • Coding
  • Statistic
  • Math
  • Machine Learning
  • Deep Learning

And any other technical skills.

The above list is accurate; most of the Data Scientist qualification you need right now is what I list above. It is unavoidable, as many job listing right now always list these skills as a prerequisite. Just look at the example of Data Scientist job requirements and preferences below.

Taken from

Most of the requirements sound technical; degree, coding, math, and stats. Although, there is an underlying business understanding requirement that you might not realize at first from this job advertisement.

If you look closely, they require someone that had experience in applying the analytical method to solve practical business problems. It implies your everyday task would consisting of solving the business problem, which in turn, you need to understand what kind of business the company runs and how the process itself works.

You might ask, “Why do I need to understand it? Just create the machine learning model and the problem is solved, isn’t it?” Well, that line of thinking is dangerous, and I would explain why.

Just for a reminder, I would argue what makes you great as a Data Scientist is not only how well your coding skill is or how much you understand the statistical theory or even the master of business understanding, but it is a combination of many.

Anybody, of course, could agree or not with my opinion as I believe there are no specific skills that make you a great Data Scientist.

Data Scientist employment is hard. It would not easy to get in this field. With many applicants and people with a similar set of skills, you need to stand out. Business Understanding is the skill that would certainly separate you from all the fish in the ponds.

In my experience as a Data Scientist, there is no skill that I felt underrated as much as the business understanding skill. I even thought that you don’t need to understand the business in my early career. How wrong I was.

I am not ashamed, though, to admit that I did not consider the business aspect essential at first because many data science education and books did not even teach us about this.

So, why is it crucial to learn the business and how it impacts your employment as a Data Scientist?

Just imagine this situation. You work in the data department of the food industry with candy as their main product, and the company plans to release a new sour candy product.

The company then ask the sales department to sell the product. Now, the sales department know that the company had a data department and requesting the data team to give new leads where they can sell sour candy.

Before anybody complains that “This is not our job, we create a machine learning model!” or “I work as a data scientist, not in the sales department.”

No, this is precisely what Data scientists do in the company; many of the projects are to work with another department for solving the company problem.

Back to our scenario, how do you correctly approach this problem then? You might think, “Just create a machine learning model to generate the leads.”

Yes, it is on the right track, but how exactly you create the model? On what basis? Is the business question even viable enough to solved using the machine learning model?

You can’t just suddenly using a machine learning model, right? This is why business understanding is so crucial as a Data Scientist. You need to understand how the candy business in more detail. Keep asking a question like,

  • What kind of business question exactly we want to solve?”
  • “Would we even need a machine learning model?”
  • “What kind of attributes related to candy sales?”
  • “How is the candy selling strategy and practice within and outside of the company?”.

And many more business questions you could think of related to the business.

It is important to know what kind of business your company run and everything related to the business as your work as a data scientist would need you to make sense of the data.

While it is easy to say that business understanding skill is essential, it is not easy to gain one.

Education is one thing; for example, you might have a higher chance to stand out to applying for a data science position in the PR company if your educational background is communication compared to someone with a biology degree.

Although work experience quickly covers this. Working experience with another job title in a similar business industry would provide significant leverage, as you already understand the business process.

For a fresher, it might be a hard industry to break in, but in hindsight, there are many benefits as a fresher as well. I remember Tyler Folkman’s post on his LinkedIn why the industry should consider recent graduates, and I agree. The recent graduate could:

  1. Come with preparation
  2. Hungry to learn about the business
  3. Make an impact

Freshers should a target for companies that have established their data journeys. The company could teach many things about business more easily as fresher have no experience at all in the business world. In my opinion, never count out the freshers.

I also would tell you about my experience, as well. When I first get the data project, I was not thinking about the business at all and just tried to build the machine learning model. And how disastrous it turns out to be.

I present the model to the related parties with hype in my brain. My model result is good, I know everything about the data, and I know the theory of the model I used. Easy peasy, right? So, wrong. It turns out that the user did not care about the model I used.

They are more interested in knowing if I already consider a business approach “A” or why I used the data that should not relate at all to the business. It ends with a discussion that I need more business training.

It is embarrassing, but I am not ashamed at all to admit that it is my fault not to consider business understanding. I could be the best in model creation or statistic, but not knowing the business turns out to be a disaster.

Since that day, I try to learn more about the business process itself, even before considering any of the technical things.


In my opinion, fresher or not, try to learn the business as much as possible.

Focus on one industry you feel interested in; finance, banking, credit, automotive, candy, oil, etc. Every single business has a different approach and strategy; you just need to focus on learning the industry you like.

Data scientist employment is hard. It was not easy to get into this field. With many applicants and many people with a similar set of skills, you need to stand out. Business understanding is the skill that will undoubtedly separate you from all the fish in the pond.

Originally published on medium


Created by

Cornellius Yudha Wijaya

Cornellius Yudha Wijaya is a Data Scientist in Allianz Life Indonesia and often writing about Data Science in his free time. He holds a Biology M.Sc. Degree from Uppsala University and have since managed to teach people how to break into the Data Science industry.







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