Analytics that support evidence-based decision making are increasingly present in companies. At this point, Data Science and Communications go together to make the direction of assertive activities. If you work with communication and data science is not yet on your priority list, you should think better. According to Aberje (Brazilian Association of Business Communications), data orientation and context analysis are the main skills required to Communications Managers.
Why is Data Science and Communications so important? In particular, they have the ability to understand data presents in intuitive dashboards. In this way, analyse the landscape and communicate with stakeholders to create strategic actions. Using Data Science, it’s possible to find efficient and structured solutions for decision making. It’s a way to structure and automate actions, such as reporting and planning.
You can have the best modem and the best ideas, but if you don’t know how to analyse, providing feedback and jointly with the team, creating actions and products that track real-time data analytics will not be useful. In other words, just collecting data is not enough to guide the activity.
Nowadays, there is so much to talk about professions that will cease to exist in some years and others that will be unprecedented. Data Science is among the most promising professions by 2030, enabling the increasing use of intelligent systems, which contributes to the advancement of digital transformation.
There are few qualified professionals in this area. We clearly see a migration from technology sector to other core sectors of companies. By combining Data Science and Communications, for example, organizations can manage multiple service channels by mapping out constant information that will be used to expand the business.
In response, many universities in the country are already integrating Data Science and Communications into their curriculum. Knowing and reading how much information is available will benefit future professionals in the fields of Journalism, Marketing, Advertising, Public Relations and Digital Media.
A bit of history
According to the IT Professionals website, data analysis process is not new. David Danoho, author of the article “50 years of Data Science” (in 2005) used the term ‘data science’, which emerged in the early 1960’s with John Tukey, who wrote “The Future of Data Analysis” and began an academic reform.
However, it was in 2001 that William S. Cleveland created the term “Data Science” in his article ‘Data Science: An Action Plan for Expanding the Technical Areas of Statistics Field’. About a year later, the International Council for Science and the Data Committee for Science and Technology published the first CODATA Data Science Journal. Following this publication, further attention was given to the subject and you already know what happened after.
Improve your skills on Data Science and Communications
If you already work with Data Science and Communications, remember that in addition to having the technical knowledge to acquire the information, cleaning it and performing the analysis, it’s also essential to know the reason why you are analysing the data. When you have a project, it’s worth stopping to ask yourself the value of this work for the company and where does it fit into the overall picture.
Knowing the answers to the questions about the first step of the process is as important as the actual analysis. After all, you will submit all the filtered information to C-Level decision makers.
Let’s say you built a model and will present your findings to decision makers in the company. To be clear about the impact this model can have on the business, it is best to start by identifying the problem or challenge you are facing.
Do you know the best way of doing it? Relate the problem to the interests of the public and help them understand the larger context. To out the audience on your side, ask questions before proposing your solution. For example, “Have you experienced this before?” or “Have you observed this in your business?”
This is a way of measuring what information your audience needs to understand the rest of your speech. What seems like an obvious problem to you is often not to your audience. Once you have pointed out the problem, discussed it and shown workarounds for resolution, you can finally reveal your solution.
Finally, your presentation should last a maximum of 15 minutes. Be brief, clear and have a good luck!