Data Scientist Soft Skills: How to Develop Business Acumen | by Vicky Yu | February 2022
There is no consensus on the definition of business acumen, but from the perspective of a data scientist, I like to define it as the ability to translate business problems into data solutions and connect to business impact.
The first step to developing business acumen is knowing the company’s business model and the problems it faces. Are there problems attracting new customers? Customers not coming back? Are revenues down? Maybe there are no problems, but the company just wants to increase its revenue growth rate.
Research the KPIs used to measure business performance and think about how your work relates to these KPIs. Knowing the goals and issues facing the business and how it measures performance will help you assess the business impact of your work.
Talk to stakeholders to understand the problem they are trying to solve and translate it into possible data science solutions. Don’t build a model without asking for context because the stakeholder may believe it’s the only solution when in fact the problem can be solved another way.
Say you work for an e-commerce company and the marketing department wants suggestions on how to optimize their conversion rates because revenue is down.
Two options could be:
As a data scientist, your first inclination may be to suggest building a model as a solution, but sometimes analysis will suffice as well. For example, a funnel analysis might show that a large percentage of visitors abandoned their cart during checkout. Marketing can create an automated email campaign to remind visitors to complete their purchase, which will improve conversion rates.
If the funnel analysis only takes a week instead of 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 rate. Prioritize your work based on estimated turnaround time and business impact to decide which one to work on first.
To understand business impact, try linking your data science solution to business KPIs. In the example above, the KPI is the conversion rate. A higher conversion rate will result in increased revenue.
What if the problem was customer retention? How does this translate into business impact? An increase in retention means more purchases resulting in more revenue from the same customer. The KPI, in this case, would be the customer lifetime value. Practice connecting your work to company KPIs to show business impact.
As a data scientist and then data analyst, it took years to master all the soft skills needed to succeed. While there is no definitive guide on how to develop business acumen, hopefully this will put you on the right path to mastering this essential skill.