As data continues to become more important to businesses, organisations, and society as a whole, the need for data scientists, analysts, engineers, and other data-related roles has increased significantly.

The areas where data and analytics can be applied have expanded rapidly, and new tools and methods are continuously being developed to extract more complex and relevant information.

We look at the current state of data-related jobs, the demand, salaries, specialisations, and why now is the best time to pursue a career in data.

The current demand for data professionals

In 2019 the World Economic Forum (WEF) called data the “new oil” of the global economy and data professionals the “talent that provides the ability to extract, refine, and deploy this new source of value.” Since then, data science has been listed among the best jobs based on salary, career opportunities, and job satisfaction.

A data career also has significant long-term growth and opportunity; the latest Future of Jobs Report 2023 found that the roles of data analysts and data scientists are among the 10 jobs expected to grow the fastest between 2023 and 2027.

Yet, there is a dire data talent shortage across the world. The UK government’s Quantifying the UK Data Skills Gap report found almost half of the businesses were recruiting for roles that require hard data skills (programming, analysis, data visualisation, machine learning (ML), data communication, knowledge of emerging technologies and solutions), but almost just as many struggled to recruit for these roles. At the time of this research in 2021, there were between 178,000 and 234,000 ads for people with hard data skills. 

This industry snapshot reveals multiple reasons why you should pursue a career in data science and analytics. Now, let’s focus on the career itself and what it can offer you – both professionally and personally. 

5 Reasons to become a data scientist or analyst

We’ve listed our top five reasons why data science or analysis is a good career for you in 2024: 

1. Social impact: You can positively impact your company or the planet by solving real-world problems through data.
2. High salary: With a shortage of talent, businesses are willing to pay for the right people with the right skills.
3. Future-proofing: Data is an ever-evolving field, allowing you to stay at the forefront of industries, trends, and innovation.
4. Business diversity: You aren’t siloed to a particular business or sector, almost every business is moving towards data-driven operations. 
5. Global opportunity: Data skills are in demand worldwide, so you could work remotely on international projects or pursue opportunities in different countries.

Unique ways to put your data skills to use

To some, data may seem like a lot of numbers, systems, and processes. But these techniques are being applied to some of the most relevant and urgent challenges we face as a society. These are just some of the solutions you could work on:

• If you’re passionate about fighting climate change, you could build climate models and weather prediction technology, helping to save lives affected by floods, drought, and other extreme weather phenomena. You could also work on cutting-edge public transportation projects that help reduce CO2 emissions.
• If food security interests you, you could analyse crop yields and agricultural practices to help farmers increase their food output. 
• If health is your domain, you could work on fighting cancer by using AI to develop new medicines and medical technologies or to identify patterns from imaging scans not easily detected by humans.
• If good governance and development is your passion, you can use data to combat corruption, increase citizen engagement, or drive budget transparency and decision-making. In the developing world, it can be used to develop solutions to local problems around safety, women’s health, education, and economic empowerment.
• If you’re interested in combating misinformation and preventing cyber hacks, you can use the formidable set of tools and techniques available to predict risks and prevent the spread of fake news.  
• If you’re business-minded, you can help companies drive efficiencies, glean deep operational insights, and generate more revenue.

Untangling the different roles in data

The broad scope and overlap between roles have led to confusion around the tasks and responsibilities of data professionals. Recruiters often use the term ‘data analyst’ loosely, with some jobs advertising for ‘data unicorns’ – those rare people simultaneously skilled in statistics, analysis, data engineering, systems development, people management, interfacing with business and tech stakeholders, and developing and deploying algorithms… 

Looking for unicorns is unrealistic and has led to the Harvard Data Science Review (HDSR) and the Department for Digital, Culture, Media & Sport (DCMS) calling for the standardisation of the roles. Harvard suggested that roles be classified into three key role families: data analyst, data scientist, and data engineer. 

From here, more specialised or domain-specific roles can be established, e.g., ML and AI engineer, big data analyst or engineer, analytics and AI translators, data-oriented product managers, and insights interpreters.

For those who are just starting in their careers or transitioning into data from another field, a data analyst role gives you the core foundation and experience you need, before you decide to specialise. 

We’ve listed the difference between data roles and the best pathway to becoming a data analyst in this article.

Many of our learners on the LSE Data Analytics Career Accelerator started the programme with little-to-no data experience. Our Career Coaches play a key role in preparing our learners for their new careers, like plotting development plans and preparing for interviews.

This is what you can expect while on the programme.

How much can you earn as a data professional? 

Data science is one of the highest-paying jobs in tech right now. Data analysts tend to earn slightly less, and data engineers and specialists a little more. Those working as a consultant or who are paid project-to-project can command considerable fees.

Here are the average salaries across data roles in the UK: 

• Data architect:  £81,000 – £107,000
• Data engineer: £52,000 – £82,000
• Data scientist: £58,000 – £74, 000
• Data analyst: £38,000 – £53,000

For more info on data roles, read our guide on how to become a data analyst and explore our new Career Accelerator in data science from the University of Cambridge Institute of Continuing Education.

Career track and progression

Generally, there are three main data paths to explore. You could choose the business analytics and intelligence route, where you’ll use data to make business decisions and help solve problems. 

Alternatively, you could focus on the technological theory of data science and specialise in algorithms and big data infrastructure. Or, you could go into data engineering and warehousing, designing and managing big data warehouses and optimising data collection, processing, and analysing. 

When it comes to career progression in data, you can start as an analyst and move up to become a senior or principal analyst, data scientist, or engineer, and eventually a chief data officer. Other options are in academic research and teaching or working as a data consultant. 

How to kickstart your career as a data professional

As a data professional, you’ll be navigating technical problems that need strategic thinking and collaborative problem-solving. Because it’s a highly practical job that has the potential to truly impact a business, employers will want proof that you’re capable of doing the work. 

Our data-focused Career Accelerators give you the university recognition and practical experience that employers are looking for. While each programme is unique, they all focus on applying the skills to practical projects. 

In the end, you walk away with a portfolio of work and a globally-recognised university certificate that assures employers you’re ready for a new career. 

If you’re interested in transitioning or advancing your career in data analytics, explore the LSE Data Analytics Career Accelerator. 

To learn advanced data science skills and tools, such as machine learning concepts, deep learning, and NLP, download the brochure for the Data Science Career Accelerator.