This is the first in a series of blog posts aimed at anyone wanting to learn more about analytics practises and development at OVO. If you’re interested to work here it’ll help you understand our terminologies (they all differ don’t they?!) and learning culture. It may even help you become a better analyst or data scientist by framing and structuring your development.
Data & Analytics at OVO
So, first off an introduction to data & analytics at OVO. We’re a data driven bunch. I’ll give you a few examples as I’m hoping if you’re reading this posting you’re analytically minded and therefore won’t just take my word for it! So, we run A/B tests and do customer research to drive our product development; we forecast energy usage to ensure we buy enough energy to meet customer demands; we monitor our team’s wellbeing and text mine the feedback we get to spot trends so we can actively be a top employer - again!.
In terms of set up at OVO, we have a core Data & Analytics team comprising analysts and data scientists, a core team of data engineers, specialist analysts and data scientists for Energy Trading and related analytics, and even more analysts and data engineers across the business in specialised roles.
Supporting great analytics
One of the questions that came up in our retrospectives with the core Data & Analytics team, was - what could we do to better support analysts across OVO?
The team used to run a SQL buddy scheme to improve access to data, but quickly recognised that being a great analyst is about a lot more than proficiency in querying a database. It's about: results focus and the 80/20 rule; using business knowledge to frame a problem we can use analytics to solve; building reusable models, so that other analysts can build on repeatable work and we have consistency. It’s about storytelling with data, helping our ‘audience’ interpret the insights for best outcome, and increasingly, it’s about enabling the business to gather good data and then deploying machine learning skills to go further than any ad hoc question would have taken us. It’s also about being accountable - having statistical rigour, and ethical standards.
We’ve also been asking analysts across OVO what they wanted, and there was resounding feedback that they’d value a structured development path, and also a clear way towards data science.
So, in this short series of posts I’m going to keep you up to date with what we’re doing to support analytics and data science across our company and get the absolute best out of it. I’m thinking we’ll cover at least:
- What is the difference between an analyst and a data scientist? - Part 2
- How and why we’ve built a consolidated skills matrix for both analysts and data scientists
- Something about our recently launched ‘Analyst Best Practice’ guide
- Other opportunities we offer to learn new skills - our thoughts on hosting events such as pydata and Data Science festival, lunch and learns, and more.
If you’re reading from outside OVO I’m curious to get your thoughts on the above, to complement the external research I’ve been doing, so please comment away! And shout if there’s anything you’d like to hear more about.
Special thanks to Kate Maxwell for taking the time to review this blog post.