Soft skills key differentiator of good data analytics professionals


The technical skills required to be a data analytics professional form the cornerstone of training approaches. If these individuals are not proficient in things like data wrangling, analytical approaches, modelling techniques, data exploration, or data visualisation, then they will likely not qualify. But that is just one part of what the job requires. I believe the difference between a good and an average data analytics professional can be found in the soft skills they possess.

Forbes recently published an article that explored this topic in greater detail and it is a fantastic read. Of course, every individual is different and will need to strengthen various aspects of their skill sets in this regard. Often, acquiring and improving these skills happen either through experience or additional training, or both. Personally, I knew I had to improve my presentation and data storytelling skills and so I enrolled in additional courses to strengthen these aspects. It is about having the self-awareness to identity opportunities for self-improvement and growth.

Below are my views on the key soft skills the Forbes article I mentioned identifies.


This is the most important soft skill in my mind. The article highlights the importance of listening as an active form of communication. How can you provide decision-makers with the insights they need, if you are not prepared to listen to their requirements, strategies, needs, and so on?

There are other aspects to consider as well. Presentation is crucial – whether it is written, on screen, or verbal. If I have to listen to a data analytics specialist say ‘like, you know’ 50 times in a presentation session to executives, I would send them to grammar or finishing school 😉 While workplaces and office environments may not be as formal as they once were, business acumen is still essential. You must adapt your language, the level of detail, and the length of the message to the audience you are working with. Considering that we are increasingly presenting remotely, even aspects like dress code and manners online affect the delivery of a message.

Secondly, you have to be able to tell the story that the data is showing. It is particularly important to be able to translate a graph and its implications to a business audience. You must express it in the business terms that they relate to.

This requires two aspects – you need to understand what the graph is saying, and then to translate that into business language. For example, business users do not want to hear there is a .7 correlation between the propensity to churn and the likelihood to adapt a second service. Instead, they want to hear that if people are unhappy with their service, they are more likely to stay if you offer them a secondary package, maybe at a reduced rate. You may know all the statistics and data trends on the tips of your fingers, but you need to know the “language” for example that mobile customers churn, insurance policy holders lapse their policies, people terminate a lease, manufacturers cease production, and the list goes on; even though it all means “stop using your service” in some way or another.


Collaboration is another important soft skill to have. As the article highlights, this can be working in a team where there may be specialists who better understand certain technologies or outcomes.

But there is another form of collaboration that is crucial in my mind – collaboration with the business. This is a natural extension of communication. One of the best ways that you can learn to understand a business is through close cooperation with its people, subject matter experts, and other stakeholders.

We once found this strong indicator between an ethnic group and some medical outcome. When we discussed it with the businesspeople, they were quick to point out that there was no correlation as the primary catchment area of that particular hospital was an area where a lot of people of that nationality lived. Our data did not indicate the overall population statistics. Validating information with business experts before broadcasting it widely can often save you a lot of blushes.

Critical thinking

Critical thinking is essential to seriously analyse and investigate every outcome. Some insights can be very misleading, especially if you may have omitted a key variable. It is therefore always a good approach to critically validate every outcome.


If a data analytics professional is only ever going to follow orders and process one service ticket after the other to create reports, dashboards, and graphs, any breakthrough insights would be highly unlikely to achieve. Instead, they must ‘play’ with the data and explore it. One of the key questions to ask is ‘what else is the data telling us?’ You have to be interested and curious to follow some of those more obscure pathways through the data patterns to really find some interesting and valuable insights.


Creativity is something that can take two forms.

The first is intricately linked to curiosity – where you have to be creative in your searching and solution crafting. The second lies in visual data art – where we use graphical art constructs like icons, colours, and line thicknesses to display the story that the data is telling.

It is one thing talking through a ‘live’ data visualisation or exploration, but it is another story altogether if you need to get the message across on a Web page or a printed report that is going to be read in isolation. This requires you to be creative with the presentation and the narrative to captivate the reader and get the message across.

These soft skills are an integral part of becoming a better data professional. If you are willing to put the effort in to enhance them, you will be able to differentiate in no time.

Leave a Reply

hope howell has twice the fun. Learn More Here anybunny videos