Everyone is a Data Scientist

The shortage of Data Science talent is well documented. Organizations face an uphill struggle to recruit (and retain) Data Scientists and, for now, we just must deal with this fact.

But this situation is changing. Coding is regularly touted as an essential skill and is now a popular classroom activity with many children now taking courses in R or Python.

This seems like sound advice. Data Science is on the rise as a profession and the volume of data is skyrocketing. This has a knock-on effect as our technologies continue to change our inherent need for AI-fueled knowledge.

However, data exploration is not the exclusive domain of the Data Scientist.

Many jobs are similar in nature to the role of a Data Scientist. This fact is often overlooked. But everyone regularly gathers data, cleans data, performs analyses, pivots in Excel, models problems, interprets results, extrapolates, and optimizes.

Data Science is now a diverse domain and no one Data Scientist can cover all of the available domains.


Looking at their day-to-day lives, Data Scientists spend almost 70% of their time cleaning, preparing and organizing data. This is a crucial part of the job, but you could argue that some of these tasks do not require a PhD in mathematics or statistics.

What’s more, many young and talented Data Scientists are not particularly happy in their job. They regularly move between companies, with an average tenure of less than two years per job.

This is because these individuals need support and mentoring to find their feet and connect with the key business challenges of their work. But, in a real-world setting, there is often no time to focus on their professional development. Data is demanding. It needs cleaning, reports must be made, and so on. When Data Scientists do not get the right support, technical tools or the opportunity to apply latest Machine Learning techniques, disillusionment soon kicks in.

To overcome this, organizations must help Data Scientists understand their business functions and how they link to the world of AI and Data Science.

In the end, all business professionals must apply analytical thinking. So, what next?

First, we must make fundamental changes both from a business and Data Science perspective.

Everyone who is not considered a Data Scientist must start to embrace data, whether they like it or not, understanding how data is organized and why. After all, data is the new corporate currency, the new corporate oil. Everyone must understand the basics of the technologies involved, and the inner workings of AI, traditional statistical modeling and optimization methods.

Today’s Data Scientists must also embrace the world of business and gain a better understanding of how real-world innovation really works. When working with data and algorithms, building solutions, they are the domain experts. But that does not mean that their efforts will be thoroughly embraced by ‘the business’ (either internally or by the customer).

An integral competence framework highlights how the quality of a Data Scientist should not be judged purely on the quality of their R or Python skills. Yes, it is important to have these skills, but they represent just one of the many areas of knowledge now required by today’s Data Scientists.

So, how can you make sure your Data Scientists have the right mix of skills and support to thrive?

Our integral competence framework brings the necessary clarity, helping business professionals understand what competencies and skills are required to run a successful AI / Data Science initiative.

Competences framework

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