Apprenticeship Levy, Case studies, Data Analytics

Moving beyond Excel: Audrey's story

21 September 2021

Audrey Guy, Senior Development Engineer at Jonhson Matthey, tells us how the Level 4 Data Analytics diploma is positively impacting her work.

Here at Johnson Matthey, we generate a huge amount of data daily. I started the Data Analytics diploma so that I could analyse and use this vast quantity of data more effectively while moving beyond my usual go-to tools of Excel. I wanted new ways to explore, visualise and make sense of our information and the Level 4 Data Analytics Diploma covered all of these topics and more.

In particular, I use Python to extract and analyse data much faster than VBA macro would have allowed me to.
This has allowed me to quickly dive deep into the data and find discrepancies and anomalies, as well as analytical insight. From this, I’m able to give strong, data-driven feedback to management so that we can take action.

Secondly, since starting my journey with the Corndel Data Analytics Diploma I have been able to consolidate how I interact with data on a day to day basis. I have improved my fundamental understanding of the different types of data and its lifecycle, as well as data stewardship and governance.

It made me reflect on where we are as an organisation with regards to our journey with data; what are the gaps, the strengths, and where can I make a difference to support our target of an integrated data-driven culture within thelabs of the future.

Thirdly, and perhaps most importantly, I have learned that it is not necessarily just the hard skills (statistics or coding) that matter, but the methodology or mindset of a data analyst. Most of the diploma is about being curious, asking the right questions, making hypotheses, and understanding where you can have an impact on the business.

I am free to explore challenging data projects, knowing that I have a coach to turn to at Corndel when I am stuck or other members of the cohort that I can reach out to. This adds to my growing confidence. In terms of how much time I dedicate to studying for the diploma, on a weekly basis the official agreement is 20% of the time. What this meant for me in practice was a great deal more fluid. The distinction between the diploma and 'work' is more blurry than just setting 2 half days aside per week - it has become a part of my day-to-day job. The projects drafted for the diploma were discussed with my line manager and incorporated into my MATS and DPD.

Overall, the diploma is well structured, you do not need much statistics or coding knowledge to start with, as you are taught the essentials as you go. The most important part is your mindset, wanting to make an impact and be part of the journey to improve the data landscape and culture.

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