The days when a business data analyst only needed to be a spreadsheet ninja are long gone. Modern-day business analysis requires robust data analysis skills and knowledge in data science methodologies like predictive analytics or causal inference. The familiarity enables you to support non-technical teams and bridge the gap with IT-based departments. In other words, you become an analytics translator.
Nevertheless, you need to have more than the ability to correlate data to identify problems for the business. Your advanced hard skills simply get your foot in the door. It’s the soft skills that keep it open. Highly proficient abilities in communication, stakeholder management, and business acumen are keys to becoming a critical resolution key for senior management.
Here are the hard and soft skills needed when starting as a business data analyst.
Improve your hard data analyst skills
In my current profession, all interns and junior colleagues are incredibly skilled. However, since they do not have years of business analytics experience, we look at what else they bring to the team. Either it’s self-taught or learned at school through data and computer science classes. These are the same principles considered during my hiring process.
To put it another way, focus your post-education energies on improving your skills as a data analyst. By doing so, you help stakeholders commit to positive business decisions.
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Have a solid statistical foundation
Statistics is the root of business data analytics. This science empowers you to comprehend a company’s data better. Additionally, it gives you a chance to see the most common biases in business. For instance, you comprehend the definitions behind omitted variables, mutual casualties, or selection bias.
You need to completely understand concepts like the mean, correlation, or t-test and how they’re applied to an organization’s data. Not only does a solid statistical foundation enable you to be more secure in your data analysis, but it also helps you move to the next level in your journey.
The main branches of business data analytics
Econometrics, segmentation, and predictive analytics are the main branches of data analytics. While it’s both tremendously underrated and unpopular, this application of statistical methods used in econometrics is a powerful science. The algorithms within this science focus on the decision-making process.
What is also insightful is segmentation and the associated techniques. Here, you separate markets into groups of prospects and customers with similar characteristics. This includes what they purchase.
Finally, I recommended predictive analytics as the third priority to study. This form of statistical science is currently popular among businesses. Its goal is to predict possible future outcomes from a series of variables.
To illustrate these areas of study, here are some examples from my professional career. The first uses the matching algorithm for econometrics. The second applies the k-means algorithm for segmentation.
Matching is an econometrics algorithm to find causal incrementality with non-comparable groups.
I work for the German fashion company Zalando. There are two language possibilities for their website: English and German. The English version was introduced in 2018. However, a few months after launch, the question of whether it was worth it was put on the table.
The English website is dedicated to immigrants and ex-pats. This profile is entirely different from the average German. Expats are younger, live almost exclusively in city centers, and have a higher income, just for the people’s characteristics. Thus their buying behavior is different.
As the English and German consumers aren’t comparable, matching was necessary to determine whether the separate website was worth the investment. Thus, I tried to determine every characteristic that makes a Zalando customer. I opted to go with 15 of them.
For the robustness check, I used the repeated experiment approach and created a 1,000-fold repetition. After the correlation of the data, I showcased the methodology and approach to senior management and colleagues.
