Cambridge Spark is a fantastic partner for us. They help us to accelerate our AI journey. So if you're a Chief Data Officer or Head of Data Science, it's a no-brainer. I think the combination of Cambridge Spark and your organisation will be a very efficient and winning team.
Didier Vila, Chief Data and AI Officer, Utility Warehouse
Utility Warehouse (UW) is the UK’s only genuine multi-service utility provider in an industry undergoing unprecedented change and paradigm shift as a result of both the pandemic and the ongoing energy crisis. These two factors are forcing utility companies to accelerate and complete their respective digital transformation efforts and capture and understand changing consumer needs and behaviours.UW is a unique business, with unique technological challenges, because of its multi-service nature. UW customers can access and benefit from energy, telecommunications, insurance, and a cashback card, all from one account and one bill.
A new partnership to accelerate digital transformation
Late last year we launched a partnership with Utility Warehouse to help bridge the data science and AI skills gap in the multi-service utility space. The partnership brings Cambridge Spark’s tailored, cutting-edge apprenticeships to UW’s expert tech team, upskilling them to deal with new challenges across the industry.
In the short video below, we hear from Didier Vila, Utility Warehouse's Chief Data and AI Officer on UW's vision for data science within the organisation. We also hear briefly from Max Adcock (who is interviewed in more detail further down), a Senior Data Analyst and one of our Level 4 Data Analyst apprentices on his success on the programme so far.
By working together, Cambridge Spark provides Utility Warehouse tailored data apprenticeship programmes to meet the specific demands of data analysts, data engineers, and software developers. These new skills are critical to navigating the richness and diversity of the UW data ecosystem but also implementing robust AI solutions. In particular, our programmes combine theory and practitioner know-how to increase the impact and accelerate the delivery of UW's "Scaling AI" vision.
Hear from Didier Vila, Chief Data and AI Officer at UW
An interview with Max Adcock, Level 4 Data Analyst apprentice
Max Adcock has worked at Utility Warehouse for the last decade, pivoting his career from customer service into data analytics during that time. In the full interview below, we find out all about his experiences on the Level 4 Data Analyst programme, and how it's helped him in achieving a recent promotion from Data Analyst to Senior Data Analyst, increasing his confidence and taking on significantly more responsibility for UW's data vision.
Hi Max, can you tell me a little bit about yourself and what you do at Utility Warehouse?
Sure, I've worked at UW for ten years in a variety of roles ranging from customer service, managing complaints, a product manager role and then I've been in our data team for about three years. We're the UK's largest multi utility provider and my role in the business is working with our technology and customer product teams centred around how we can improve the customer experience. My role touches everything from how our customers interact with us, when they call us, when they email us, when they speak to us on chat, how our customers interact with our help centres, and everything in between.
So how exactly would data be involved in your day to day role?
So, we work as part of a cross functional team, so it would be anything from helping our product managers define what they're working on, for example, discovery on certain initiatives, reflecting back on other things that we've changed and introduced, i.e. measuring success. Asking questions like 'Did that thing we did have the desired impact? Should we do more of it?' Helping our business understand change.
So, changing market conditions, customer acquisition, it's really, really vast and varied and the customer experience team touches all parts of the business. We support all of the contact centres, helping them understand why there might have been a peak in contacts on a particular day, was there an incident? It's vast. Every day is very different. It's super reactive.
What were your key motivations for doing the apprenticeship?
I was very lucky. It was something that the data leadership team came and offered to everyone, and it wasn't something that I'd heard of beforehand and there were some elements of it that really stood out to me. It touched on software engineering, foundations of programming and that's something that I hadn't really had much exposure to before but it interested me.
What did you hope to gain at the end of the apprenticeship?
I was always quite clear that I wanted to build up a portfolio of evidence that showed that I was stretching to becoming a Senior Data Analyst. We had recently gone through a process across the data function on defining a career framework and at the time I was employed as a data analyst and I had aspirations of becoming a senior analyst, and ultimately I want to go up to principal data analyst. I was keen to use this course as an opportunity to demonstrate the value-add that I bring to the business, and to leverage that in a conversation for review at the end of the year.
Can you give us some examples of how you've applied some of the new skills you've learned on the apprenticeship?
We are reliant on lots of third party tools and systems to interact with customers and a large part of my role before the course was downloading datasets from these tools manually, and then performing some analytics and cleaning it to present to the business. With what I've learned, we've been able to build some software that scrapes this data automatically, so I've saved a considerable number of hours every single month that I can use to support the business in other ways. So essentially, we've now got an automated pipeline for most of our tools that collect data.
Aside from that project, the showcase project that we've done is that I've been able to collaborate with our data scientists to launch a natural language processing model in production. We send out a range of different surveys to customers where we ask them to review an interaction with us or would they recommend us to a friend or family member and within these surveys, customers can leave a comment, which obviously we get hundreds of thousands of comments returned to us every year and it's very difficult to analyse them.
So we've been able to collaborate with running the comments through a machine learning model, basically every comment we return a topic and I've been able to get involved in that in the analytics of discovery, which has been a great project end-to-end. There was lots and lots of automation which would have taken hours of monthly admin. I've been able to automate that and build software that takes that pain away and does all of the hard work for me.
It's freed me up to add value to the business in other means and I think one of the other projects in my portfolio was understanding what communications we send could trigger customers to call through. We send millions of communications to our customers every year. We receive millions of calls every year. It's an ongoing business interest to try and understand what drives calls and how we can better the experience.
My first project was looking at what communications prompt customers to call through and I was able to use Python to process data that I wouldn't have been able to before because of the size of it. We were able to tailor some of the communications that were causing problems and we're really starting to see a difference in how many calls customers make to us, especially when they're onboarding and joining us, which is a big success. It's one of the business wide KPIs now to reduce calls per customer and I think that project has gone a small way to chipping away at some of those calls which has been really, really nice to see.
Can you tell us a little bit about your recent promotion and how you think that the course has affected that?
Yes, I was recently promoted from Data Analyst to Senior Data Analyst and part of that criteria for the promotion was feedback coming from stakeholders, people that I've worked with, and people that I work for.
A lot of the praise that they gave me in that 360 feedback was from some of the projects that I've worked on through Cambridge Spark and a lot before that too but it was clear that I've been able to up the amount of value that I've been adding to the people that I work with since starting the apprenticeship.
Looking towards the future, how do you think that this apprenticeship will help you with your professional career development?
I think it's actually changed the direction that I want to go down. So, I didn't realise how much I enjoyed getting involved in the data engineering and the data modelling side of things and that wasn't something that I typically got involved in before the course. Through working closely with our Data Scientists & Data Engineers, I've definitely realised that I'll probably pick up a programming language and maybe branch into software engineering but with a specialisation in data.
Is your company ready to upskill in data analytics?
Aside from the Level 4 Data Analyst and Level 7 AI and Data Science apprenticeships mentioned above, Cambridge Spark offers a range of data science and AI apprenticeships funded by the government's Apprenticeship Levy, as well as a number of commercial training options for organisations who don't qualify for Levy funding.
Fill out the form at the bottom of the page and one of our consultants will contact you directly to answer any questions you may have about our full range of training options.
Read more Level 4 Data Analyst apprentice stories
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