TJ spoke to Shaun Dipnall to discover what it takes to make it in data science.
Research indicates there was a global shortage of 250,000 data science professionals in 2020. Why do you think that is?
Data exists in vast quantities in every business. Statista for example, estimates the total amount of data created, captured, copied, and consumed in the world is forecast to increase rapidly, from 59 zettabytes in 2020 to 149 zettabytes in 2024.
This is compared to just two zettabytes in 2010. With this explosion in the amount of data generated, all organisations including financial services, logistics, retailing, healthcare as well as Government, are looking to leverage data to drive efficiencies, deliver customer insights and make better decisions.
By making data science integral to their daily operations, organisations have benefitted from a wave of innovation and growth. Typically, however, an estimated 80% of corporate data is unstructured and needs predictive analytic tools to gain insights from it. With that comes the need to hire people with the right skills to collect, read and analyse data.
How important are data scientists to the future of business?
Data is the new oil in terms of driving business competitiveness and innovation. As organisations increasingly rely on information from data for their decision making, the role of the data scientist will become ever more important. However, the value of the data scientist can only be optimised if businesses gear themselves internally to the data science journey.
Since the pandemic, companies have accelerated the digitisation of their customer and supply-chain interactions and of their internal operations by three to four years
For me this means identifying the specific areas and opportunities in the organisation where data science can add value, then finding the skills and technical resources needed to cope with the technicalities. Usually, existing internal resources are insufficient, so external resources should be called in if required.
It’s also important that company executives understand the process. This includes clearly articulating the particular problem areas in the business, as well as gathering, cleaning, wrangling and preparing the data. Analysing the data comes next, followed by visualising results in interactive and intuitive ways and communicating findings to non-technical stakeholders who need to act on the insights.
Building a data science team with a diverse set of skills is crucial too. This may comprise some or all the following roles: analytics translator, data scientist, data engineer, data analyst, business intelligence developer and software developer.
Once there is a competent data team in the organisation, it needs to fit comfortably into the business’s structure. Ideally, a team should operate as a ‘centre of excellence’ within the organisation, rather than decentralising it across departments or simply placing it with IT.
Do you think the pandemic has placed a greater emphasis on the need for data science skills and how will this impact upskilling staff in the future?
According to McKinsey, since the pandemic, companies have accelerated the digitisation of their customer and supply-chain interactions and of their internal operations by three to four years. In addition, the share of digital or digitally enabled products in their portfolios has accelerated by an estimated seven years.
This acceleration of digitisation has forced businesses to look at upskilling and reskilling their current employees in data science to broaden its value and impact throughout the organisation.
What advice would you give to someone looking to pursue a role in data science and AI?
There are a variety of roles within data science – data engineering, data analyst, data visualisation expert, machine learning expert, and more. This can be confusing especially when the sector is changing as quickly as it is.
My advice would be to talk to various people in the field to get a better idea of where it is you want to fit and how your existing skillsets and qualifications would best suit the role. Once you understand the role and its requirements you are better positioned to find an appropriate course.
When choosing a course, make sure that it offers a combination of self-study, teamwork as well as some real-world problem-solving projects – preferably ones that can be directly applicable to your place of work.
The latter is particularly important to someone wanting to upskill so that they can ensure that what they are being taught can be directly applied to their place of work and so enhance their marketability.
About the interviewee
Shaun Dippnall is CEO of EDSA