According to Reghard van Jaarsveld, Engagement Manager at Decision Inc, 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.
With worldwide revenues for big data and business analytics expected to reach more than US$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 that 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. This results in attention shifting from an individual to getting groups of people working effectively together.
Curiouser and curiouser
Even though many people think that data science revolves around throwing technology at the problem, it is much more nuanced than that. The key to turning data into insights is to do what machines cannot. Data scientists have several character traits that facilitate this.
At the core is a curiosity of gaining a deeper understanding of data and discovering what is relevant to the business. Empathy is another skill that is important as connecting with others provide better insight to what people’s requirements are.
Building from here is having the imagination to come up with ideas that do not yet exist. Having access to so much data means the ideas are out there, they just need to be harnessed by the data scientist (or team of specialists). Creativity is essential to invent and articulate solutions and coming up with unique problem-solving elements.
The final ‘human’ skill required is that of leadership. Data scientists must communicate their ideas in such a way that they can influence people and move them into action.
Science versus analysis
Some still fail to see the difference between analysts and scientists. The former is more focused on being reactive to data while the latter uses insights to be predictive and finding ways to change future behaviour.
For example, the data analyst might look at the difference in inventory levels throughout a global supply chain whereas the data scientist will examine what will affect the global supply chain within the next six months.
Of course, this does not mean a business should prioritise the one over the other. Both sets of skills are important as enterprises chase competitive advantage in an increasingly cluttered market. It is all about how the business can harness those skills and the teams of people contributing to those data areas that will drive efficiencies in the digital organisation.