Our brand new career spotlight series provides industry insights from people working in sectors that are looking for new faces.
Do you know what you want to do after college or university? Maybe you’ve had your career path mapped out since you started primary school, and have been working towards your dream job ever since. But for most of us, deciding what career to pursue isn’t easy. With so many options to choose from, how do you know what your ideal job is really like?
In this new series, we’re asking a range of graduates what their career is really like, to help you find that elusive dream job. First up, it’s data analytics. And we’re surprised to find it’s not just for geeks…
Dream job: Data analytics
Skills required: Numeracy, analysis, coding, creativity, problem-solving
£££: Starting salary c.£24K, senior roles £60k+
Meet Peter Jones, a data analytics manager in financial services
Tell us about a typical day in your job
My day typically involves three types of work:
- Strategic work, setting out how to make my department more data-centric - this could be organising training on new software or in statistical techniques, or writing guidance notes on how to approach a fraud detection analysis.
- Building tools and dashboards to visualise trends.
- Work on specific projects - planning the analysis, requesting and cleansing data, conducting analytical tests and producing insightful visualisations to tell the story.
What made you want to get into data analytics?
I’ve always been analytical, and my degree (in English) had a big focus on selecting the right evidence to support my argument. Increasingly, analysis and problem-solving are expected to reference data so the move into analytics was quite a natural one. I’ve always been comfortable with maths but gave it up after GCSE so I’ve had to do a fair bit of post-university training in maths and statistics to be competent, but it wasn’t a barrier.
How did you get into data analytics?
I was working in the strategy team at the Home Office and I realised that data was essential for planning and making decisions so I decided to develop my skills in data. That turned out to be one of my better decisions as there is now a huge demand for data scientists and people who can use data. I’ve largely developed all of my data skills and experience on the job, with training on key pieces of software along the way, especially data visualisation tools which help ‘non-data people’ to understand the story and act on the findings.
What’s the biggest misconception people have about working in data analytics?
People assume that ‘data people’ spend their spare time indoors geeking out over maths and sci-fi. While this isn’t an entirely unfair representation, people are often pleasantly surprised that I can be creative and think big-picture.
What was your dream job when you were 16?
Apart from rugby lock forward for England (I’m tall, but a dwarf compared to the national team), I wanted to be a lawyer. However, an unbelievably dull work experience in a solicitor's office when I was 17 led to a dramatic rethink towards the civil service, and then into data.
What courses or training did you do?
Most crucially, data visualisation - I particularly like Tableau - as well as statistical packages and code like R and Python. I’ve also done leadership and management programmes at CASS Business School, which is really useful as an analyst because you reach a point where you either have to specialise or go ‘hands off’ and move into leadership. I chose the latter.
What was your university life like?
Great. I studied English at Sussex. Not many teaching hours (six per week), so I spent lots of hours reading books on the sunny campus in Brighton.
What advice would you give to someone who wants to work in data analytics?
The obvious requirement for new entrants is to evidence their skills in maths and/or code. But as a hiring manager, there are lots of applicants like that. If you’re a good analyst, a good thinker or a problem solver then actually communication skills (written and verbal) are the biggest thing to work on as very good analysts who can also interact with management and make an impact with their findings are rare. So my advice is to train yourself to ask ‘so what?’ and ‘what should we do now that we’ve learned this?’, because analytical findings are worthless unless they are acted upon.
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