Over the last decade, the ability to access, leverage, and analyse data has become increasingly important. Data expertise, once largely confined to the tech world, has become vital across every sector and in almost every type of business. 81% of organisations said they were increasing their investment in big data in 2019 according to a study by Whishworks. This means that data professionals are more sought after than ever before. Data analyst and data scientist are two of the key roles at the centre of this thriving data landscape. In this blog, we explore these data-focused roles and discover which specialism is most in-demand today.
What is the difference between a data scientist and a data analyst?
Although both roles are often referred to in the same breath, there are key differences between a data scientist and a data analyst. Data analysts look at data trends, identify patterns, and produce insights that help organisations make data-based decisions. They also help to aggregate, manage, and cleanse data. Data analysts perform an essential job role, helping to provide the commercial business insights that all organisations need to stay competitive in the marketplace.
Data scientists may have a data analyst background but combine this with coding and software engineering skills. They can create new processes for data modelling, advanced programming, predictive models, and machine learning. Data scientists are more heavily involved in the production and creation of products due to their deeper theoretical knowledge and practical expertise. They may not be as essential to every business as data analysts, but they add enormous value and are vital for a wide variety of projects.
What are the differences in pay between these roles?
In the Xcede 2020 Salary Survey, we found that those in data scientists job roles attracted a significantly higher salary than data analysts. A mid-level data scientist commands an average salary of £69,000 compared to £39,000 for a mid-level analyst. This reflects qualification level – many data scientists have advanced degrees – as well as the specialist, targeted expertise that data scientists offer.
Is data scientist a new role?
A decade ago, the role we now refer to as data scientist would have been performed by a senior data analyst or an analyst with a very specific skillset. However, as the importance and value of data has grown, we have begun to see people working towards data science as a specific career path. Often studying maths and coding at university, before pursuing an advanced degree in data science. Data scientist is a role and term that has grown out of our expanding reliance on and engagement with data. The advancement of key tech, such as artificial intelligence, has also made data scientist a more prominent and sought-after role.
Does this mean that data scientists are more in demand?
Not necessarily. All organisations have different data needs, and this is reflected in the types of roles they hire. Feedback from Xcede clients suggests that some organisations are beginning to supplement the work that data scientists do with advanced technology that can perform some of these specialist functions. They are also choosing to increase data analyst numbers as this generalist role can support a wider range of business units.
The need for data scientists varies across industries, but if we look at demand across the board, the number of data analyst roles are much higher. Over the last 12 months, our teams have overseen 453 data analyst roles compared to 300 data scientist roles. This is because the qualification requirements and cost-per-hire are lower for analysts. It’s important for organisations looking to engage data talent to understand specifically what functions they need a candidate to perform. This will help to determine whether they need to engage an analyst or scientist.