Data and analytics: the most in-demand skills
What are data and analytic skills and why are they crucial today?
The global demand for data analysts and data science roles is rapidly increasing, with the number of applications for data science in accountancy, law and actuarial science multiplying every year.
Data and analytics will be integral to almost every role across every sector.
The Royal Society’s report ‘Dynamics of data science skills’ found that between 2013 and 2018:
The demand for data scientists increased by 1827%
The demand for data engineers increased by 452%
The demand for data analysts increased by 43%
This type of demand cannot be met through Higher Education alone. The analysis of Higher Education Statistics Agency data prepared for the UK Government’s Data Skills Taskforce estimated that the ‘potential supply of data scientists from UK universities is unlikely to be more than 10,000 per year’* - meaning that HR leaders and L&D specialists will need to look at upskilling their current talent in order to plug the data skills gaps within their organisation.
Download our Skills Revolution Report or APAC Skills Revolution Report for APAC regional insights.
Let’s explore these three roles and how they can add value to organisations:
Data scientist
A data scientist is able to take data in its raw form and break it down into useful insights that can help organisations to drive their business forward.
Data engineer
A data engineer will support in building or maintain systems and algorithms that will manage and explore data, while looking for key trends that will have a significant business impact.
Data analyst
Data analysts are responsible for interpreting data and providing insights through clear visual, written, and verbal communication.
What does ‘analytics’ mean?
Analytics is the process of using data to do one of four tasks:
Descriptive analytics: using data to describe a situation
Diagnostic analytics: using data to make inferences and draw conclusions as to why something has happened
Predictive analytics: using the data we already have to predict what is likely to happen in the future
Prescriptive analytics: using the data we already have, and the predictions that have been made, to prescribe what should be done in order to achieve a specific outcome in the future.
What type of specialist roles and skills are organisation’s recruiting?
According to research by the UK Government, 48% of businesses that were surveyed were recruiting for roles that required data skills, the most common of which was data analyst.
BPP and professional services firm Grant Thornton came together to find out which skills organisations needed in order to get the most out of their data, and identified three key areas:
1) Technical (hard) data skills
2) Generalist (softer) skills relating to good work practice in the context of data
3) Data literacy and confidence in using data
Download our Skills Revolution Report or APAC Skills Revolution Report for APAC regional insights.
Technical skills
Many organisations are unable to capitalise on the data within their business due to a shortage of specialists with technical data skills, such as data science, scripting languages, big data, SQL databases and machine learning.
Developing your data specialists ensures that you have teams with the technical knowledge and skillsets to work with complex data architectures, including programming for data analytics, big data architectures and big data analytics, in addition to AI, machine learning and robotic process automation.
Generalist skills
Data does not sit in isolation, and neither should the data scientists who use it. Those working with data need to understand how these skills are applied at a more strategic level, understanding the types of decisions and problems that data can help solve. They will need a strong commercial mindset and collaboration skills to gather meaningful data and be able to transform this into insight that drives change.
There is a great desire for data ‘storytellers’ – those with the critical thinking and communication skills to present findings through stories. They can then link these insights to the real world, in a way that resonates with senior stakeholders, informing business decisions and adding tangible value to an organisation.
Data literacy and data confidence
We’re not just lacking in data scientists, but also in ‘data citizens’ – people in a non-digital profession who have sufficient levels of confidence in how to use data in their everyday role. What is needed is an uplift across organisations to build fundamental data skills and overall confidence in using technology to benefit individual roles.
