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Did you know that over 2.5 quintillion bytes of data are generated worldwide every day? By 2021, it's predicted that businesses using data-driven insights will take an estimated £1.4 trillion annually from their less-informed competitors. This is because data-driven organisations are 23 times more likely to acquire customers and they’ve done this by spending about £143 billion on big data and analytics in 2019 alone.
At the pace at which data is growing it's becoming critical for a business to adopt a data strategy. Currently, organisations only analyse about 12% of the data they have, which means that they are losing huge amounts of valuable data-driven insights, primarily because they don't understand what valuable data looks like.
The whole data field used to be a blur to me, just a load of fancy buzzwords that I never really made sense of. That is until I decided to work and study in data. Since I started studying for a Data Analytics Apprenticeship, I have been exposed to the meaning of those buzzwords and gained an understanding of how to collate, present and use data.
Although some data analysts may look at large and complex data, also known as ‘big data’, a lot of work also involves smaller data, such as internal data sets and company records, which is what I work with on a day-to-day basis. Even with smaller data sets, analytics can be used to predict future events, for example, it can be used in sales forecasting, fraud prevention, marketing segmentation and operational efficiency to name a few areas. After all the hard work of finding key insights, it's equally as important to be able to communicate those insights effectively, so that decision-makers can understand and act upon it. Mastering the art of data storytelling is key to ensuring people across the organisation take actions in response to the data you collate. It's so much more than just crunching numbers day in day out. At its core, studying data science allows people to become data-driven thinkers – in all aspects, not just the job. It helps people make educated decisions in every part of their role.
To become a data analyst and to develop technical attributes, you’ll have to learn analytics, data visualisation, predictive modelling, coding, communication and more. Robust contextual understanding, statistics and probability are some other fields you'll gain knowledge of. All these skills can lead to you becoming a great data analyst. But mastering data science on your own is really hard and this is why I chose to do an apprenticeship. A lot of the knowledge and skills I’ve attained, I’ve gained on the job, not just through study.
There is a huge opportunity to progress if you choose a career in data, as it’s a growing industry and relevant to so many different businesses. For example, it is widely used in health-care, banking, consultancy services and e-commerce industries. All of which are very different. Data Science is very versatile, or in other words, it provides 'horizontal mobility'.
Data-led apprenticeships are rather new. I’d never heard of a data science apprenticeship until a year after finishing my A-Levels and many months of Googling for courses in tech. I knew I wanted to do an apprenticeship in tech for a while, but it took time to find the perfect opportunity for me.
It all came into place when I came across WhiteHat. A tech startup, who match young people to the best career-focused apprenticeships on offer. They helped me find a course that is right for me and assisted me in every single step, all the way until I secured my role at Concentre Consulting. Not only do they help people find the best apprenticeships, but they also provide an outstanding alternative to university, with incredible content and a huge support network.
Prior to starting my new job in data, I was working for a company that focused on mobile app marketing. I was looking at ad performance on a daily basis, analysing what worked, what didn’t, what ads needed more of a push and so on. I was carrying out analytics every day without even realising it. Alongside that, I was teaching myself code such as HTML, SQL and Python. I had an interest in data before I even came across the field or the job role.
All I had to do is look outside the box, and the right career appeared right in front of me. In the past, I was searching for specific roles that I thought I would enjoy, without having the knowledge of all the roles available to me. I realised that instead of searching for roles, I should be working out what my key skills were, and work it out from there. That’s what led to me choosing a career in data analytics.
Data is a multidisciplinary field that has its roots in statistics, maths and computer science. So if you're good at maths, approach problems methodically and you have an interest in learning computer languages then data analytics could be for you.