Embracing the science of data

The importance of data in the digital society cannot be underestimated. Companies need to constantly review, analyse and interrogate the data they have at their disposal to enable better decision-making as well as improved solution development, says Reghard van Jaarsveld, Engagement Manager at Decision Inc.

With worldwide revenue for big data and business analytics expected to reach more than $210 billion by 2020, it is no wonder that getting to grips with the data at hand is fast becoming an organisational priority. Even though data analytical skills have been touted over the past few years, there is a new breed of specialist emerging to take this to the next level, the data scientist.

This person forms part of a trinity of people within the data management field: data engineers, data analysts, and data scientists. While the engineer is focused on design, infrastructure and flow of data, the analyst uses data to understand the business and provides valuable insights for specific departments. A data scientist can be viewed as a polymath, an individual with a working understanding of various complex bodies of knowledge within science, technology, engineering, mathematics and the arts.

Given this, one of the biggest hurdles for organisations is to get value from those claiming to be data scientists. Considering the average age of these employees to be 26, it is hardly reasonable to think they are experienced enough to be considered data scientists in the true sense of the word.

A data scientist requires skills across several disciplines. These include maths and statistics, programming and databases, domain knowledge and soft skills, as well as communication and visualisation. Given how diverse these skills are, they are rarely found in a single individual.

Instead, the likelihood is greater that businesses will pool a variety of skills in multi-talented teams that constitute what a data scientist is all about.