A 3-Step Method For Data Scientists to Develop Business Acumen

Many data scientists focus on learning technical skills but spend little time developing the soft skills needed to become successful. Common soft skills often mentioned for success are communication and data storytelling but business acumen is an essential skill that will help you stand out from the crowd. Today I want to discuss how to develop your business acumen as a data scientist.

Introduction

There’s no consensus on the definition of business acumen but from a data scientist perspective, I like to define it as the ability to translate business problems into data solutions and connect to business impact.

1. Determine the Business Problem

The first step for data scientists to develop business acumen is to learn the company’s business model and what issues the company is experiencing. Are there problems getting new customers? Are customers not coming back? Is revenue down? Maybe there are no problems but the company simply wants to increase its revenue growth rate.

Research the KPIs ( key performance indicators ) used to measure company performance and think about how your work relates to these KPIs. Learning the goals and issues the company is facing and how they assess performance will help you evaluate the business impact of your work.

2. Translate Into Data Solutions

Next talk to stakeholders to understand the problem they’re trying to solve and translate that into possible data science solutions. Don’t build a model without asking for context because the stakeholder may believe that’s the only solution when in fact the problem can be solved another way.

Let’s say you work for an e-commerce company and marketing would like suggestions to help optimize their conversion rates because revenue is down.

A couple of options could be:

• Run a funnel analysis on the website visitors to purchase and identify any major drop-offs that can be improved to increase conversion rates.

• Build a segmentation model to help improve targeting to different types of customers. Marketing can customize its messaging for each segment which can translate to higher conversion rates.

3. Connect to Business Impact

As a data scientist, your first inclination may be to suggest building a model as a solution but sometimes a simple analysis will suffice. For example, a funnel analysis can show that a large percentage of visitors abandoned their shopping cart during checkout. Marketing can create an automated e-mail campaign to remind visitors to complete their purchases which will improve conversion rates.

If the funnel analysis only takes 1 week versus taking 1 month to build a model, you can work on the funnel analysis first and then on the segmentation model to further improve the conversion rates. Prioritize your work based on estimated completion time and business impact to decide which one to work on first.

Take the time to learn KPIs important to the business and how they relate to business performance. This will help you figure out how to link data science solutions to company KPIs. In the example above the KPI is conversion rate. A higher conversion rate will translate to an increase in revenue.

What if the problem is customer retention? How does that translate to business impact? An increase in retention means more purchases translating to more revenue from the same customer. The KPI, in this case, would be customer lifetime value.

Final Thoughts

As a data scientist and then a data analyst, it took me years to master all the soft skills necessary to become successful. While there’s no definitive guide on how data scientists can develop business acumen, I hope this starts you off on the right path to mastering this essential skill.

If you found this article helpful, please consider leaving a tip. Thanks for reading!