The shortage of analytical (i.e. data science) talent is well documented. Organizations face an uphill struggle to recruit (and retain) Data Scientists and, for now, we just have to deal with this fact.
But there are changes afoot. Coding is now a popular classroom activity as our children are told that to succeed in life, they have to take a course in R or Python.
This is sound advice. Data Science is certainly on the rise not only as a profession but also, most of all, as the latest buzzword in the world of technology. These two words, Data Science, are also associated with the enormous growth in data, technology and our inherent need for knowledge around Artificial Intelligence.
But what does it mean to be a Data Scientist?
Looking at their day-to-day lives, almost 70% spend their time cleaning, preparing and organizing data. This is a crucial part of the job, but you could argue that some of these tasks don’t require a phd in maths or statistics.
What’s more, many talented, young data scientists are not particularly happy in their job. Studies reveal that they are prone to moving between companies, with an average tenure of less than two years per job.
These individuals often need intense support and mentoring in order to find their feet and connect with the key business challenges they must address in their work. Often, in a real world setting, there is no time for that. Data needs cleaning, reports must be made, and so on. Soon disillusion can kick in when they don’t have the right support, technical environment or the opportunity to apply latest Machine Learning techniques.
What needs to happen?
You must fill the gap in their knowledge regarding general business functions and their links to AI and Data Science. In the end, all business professionals are applying analytical thinking. Everyone gathers data, performs analyses, pivots data in excel, models problems, interprets results, extrapolates and optimizes. But that’s what a Data Scientist is supposed to do as well. So, what next?
We must make fundamental changes both from the business and Data Science side of things.
Everyone who is not considered a Data Scientist must get into data, whether they like it or not. Everyone must understand how that data is organized and why – after all, data is the new corporate currency. Everyone must understand the basics of the technologies involved, the inner workings of AI and traditional statistical modeling and optimization methods.
Today’s Data Scientists must regard themselves more as business professionals, and obtain a better understanding of how real-world innovation really works. While they are experts when working with data and algorithms (and, maybe, build solutions), that doesn’t mean that their efforts will be successfully embraced by ‘the business’ (either internally or by the customer).
Here, I present an overview of the competencies that could help your business address these challenges. This integral competence framework has two benefits: it highlights that the quality of a Data Scientist is not judged on the basis of their R or Python skills alone. Yes, it’s important to have these skills, but they represent just one of the many areas of knowledge now required by today’s Data Scientists.
For the business professionals, this competency overview helps them to understand what competencies and skills are required to run a successful AI / Data Science initiative.
(NB if you want to get the detailed list, drop a note to firstname.lastname@example.org)