If you want to enhance your chances of success in today’s corporate world, you must be ready to continually overcome obstacles as they can appear at any time and from anywhere. So, having business acumen, a skill you can master with the appropriate methods, is essential if you want to be successful. While expanding their technical knowledge, many data scientists spend little time working on the soft skills necessary for success. Communication and data storytelling are two common soft skills that are frequently mentioned for success, but business acumen is an essential trait that will set you apart from the competition. Keep reading to find out what exactly business acumen is and how data scientists can build it.

What is business acumen?

To begin with, it’s important to define what business acumen exactly is. Business acumen, also known as business sense or business savvy, can be defined as a person’s ability to understand various business environments and successfully navigate them. Strong business acumen skills enable people to comprehend business processes, assess company problems, and offer valuable insight on how to achieve objectives and, in that way, ensure business success. In times of change, they may also adapt and remain flexible. To put it another way, someone with excellent business acumen is knowledgeable about many areas of a company and how it runs and can use this knowledge to make wise judgments. When choosing applicants for leadership roles, employers place high importance on the business acumen skills of candidates.

Business acumen in data science

Although there’s no universally accepted definition of business acumen, from the standpoint of a data scientist, it can be defined as the capacity to convert business problems into data solutions and link those solutions to business effect. Any young data scientist can benefit from having business domain knowledge to stay competitive in the field of data science. The industry is so dynamic and developing at such a rapid rate that data scientists are being forced to tune their models and comprehend customer behavioral patterns. This turns into a crucial feature that distinguishes a data scientist from a regular researcher or software engineer. Keep in mind that you can build real world business acumen in the world of data science not only on an individual level but also on the organizational level. That’s why outsourcing this planning to companies that focus on business growth is always the best option for startups and larger organizations.

Identify the business problem

Understanding the business model and problems of an organization is the first step in gaining business acumen. You might come face to face with issues like attracting new clients, sales going down, and customers abandoning your brand. On the other hand, there may not be any issues and the company’s goal is to simply speed up the growth of its revenue. Consider how your work relates to the key performance indicators (KPIs) used to measure corporate performance after doing some research on them. It’ll be easier for you to measure the business impact of your work if you’re aware of the company’s objectives, problems, and performance evaluation methods.

Data translation solutions

Stakeholders should be consulted in order to comprehend the issue at hand and transform it into potential data science solutions. Don’t develop a model without a context since the stakeholder might think that’s the only way to deal with the issue when there are alternative options. Imagine that you’re employed by an online retailer whose marketing strategy is looking for recommendations to improve conversion rates because sales are declining. One of the possible solutions could be to analyze the website traffic that’s intended for purchases to find any significant drop-offs that can be improved to boost conversion rates. Another solution could be to create a segmentation model to aid in better targeting of various consumer groups. For each segment, marketing can tailor its messaging, which may result in improved conversion rates.

Connect to business impact

Your first instinct as a data scientist might be to propose creating a model as a solution, but there are instances when analysis will work just as well. For instance, a funnel study may reveal that many visitors abandoned their shopping carts during the checkout process. To encourage customers to complete their purchases, a company can set up an automated email campaign, which can increase conversion rates. You can work on the funnel analysis first and then on the segmentation model if it just takes a week instead of a month to construct a model, which will help you increase conversion rates. Determine which of your tasks to work on first by prioritizing them based on their expected completion time and business impact.

Although it can take years to acquire all the soft skills required for success as a data scientist and later a data analyst, hopefully, this article can assist you on the path of building your business acumen.