As far back as 2012, a data scientist was referred to as the sexiest job of the 21st century. Even though these skills remain in great demand, a global shortage of the in-demand resources has seen companies across industry sectors still struggle to unlock the full potential of their internal data.
Fortunately, momentum has started to shift. Not only is there an increased awareness of the need for data scientists, but higher education institutions are now also providing for more courses focused on the skills required of the position. Added to this, the availability of more advanced technology is also contributing to a more conducive environment for data scientists.
Like most specialised sectors, however, nothing will replace experience. As a result, the IT industry needs to urgently build-on, and support, experientially driven courses, internships, and processes to facilitate the data scientist journey from ‘sexy and sole purpose’, to critical and team reliant.
“Of course, the influx of data science courses and degrees are a great help on this journey, but they are only one piece of the puzzle,” says Jessie Rudd, technical business analyst at PBT Group. “A data scientist must have experience regarding how to work with volumes of disparate data, as well as an understanding of what the business truly needs from its data.
“This can only come from time and understanding. Of course, theory is important, but the skills to perform the job correctly can only come from being at the ‘coal face’ and experiencing data in action.”
Andreas Bartsch, head of innovation and services at PBT Group, believes that part of the challenge in fulfilling the role of data scientist lies in its definition, and its misguided perception within organisations.
“Within the industry, there are too many broad definitions of what the job entails. This is a real concern as many higher education institutions refer to their graduates as data scientists after they have completed their courses, which this not actually the case. Even within the corporate environment, data science has many different definitions, which further exacerbates the challenge,” says Bartsch.
Thanks to the availability of Artificial Intelligence (AI) and Machine Learning (ML) technologies, analysing data, at scale, has become easier than ever. However, this does not mean it is something that can be left for the machines to do, instead, it requires a balance between form and function.
“Data engineering enables AI. And many data science functions rely on data readiness, data accuracy, data completeness, and so on. This therefore requires an integrated approach to data science, which might not come necessarily from only having theoretical knowledge,” says Bartsch.
In fact, many of the skill sets and experience essential to undertaking data science do not come from an individual, but the team around them.
“Building and maintaining a strong data science team has become a critical function at most organisations. From data collection and management to analysis and data engineering, all this requires a combination of skilled people that work in unison to help accomplish the strategic objectives of the organisation,” says Rudd.
“If companies are to truly embrace data science, they must understand all the intricacies involved and not just rely on plugging graduates into the growing data real estate,” concludes Bartsch.