1,000s of hours
- Organisation: Exertis
- Industry: Technology Distribution
- Organisation Size: 3,900+ employees
- Headquarters: Burnley, England, UK
Leveraging a wealth of data
When Dr. Adeala Zabair joined Exertis as Head of Data Science & Analytics in February 2023, the company had been integrating SAP as their system for managing daily processes and core capabilities within the business.
A wealth of available data and possibilities to leverage that data, accompanied the adoption of that system.
Dr. Zabair says this presented opportunities to maximise operational efficiency and ultimately generate revenue and profit with minimal effort.
Leading the data science team and guiding that transformation with data has been a main focus of Dr. Zabair’s since joining Exertis. However, before that transformation could begin, the company needed to build a team with the right skill set.
“There are a number of areas in the business that we think could be improved by looking at data-informed decisions: either the data allows us to standardise processes in the business or data brings to the fore a lot of insight, which we can use to make better decisions.”
Finding the right talent solution
Jonathan Wagstaff, Director of Market Intelligence at DCC Technology, works in the divisional layer of the parent company which oversees the various businesses under the umbrella. He was deeply involved in Exertis, in particular, when in early 2021 he started considering where data science could offer the most leverage for distribution and wholesale to his team.
He knew he would need to cultivate a team with the skill set needed to bring data science to Exertis. But he didn’t want to spend a fortune on hiring talent externally for an initial proof of concept. That’s when he was referred to Cambridge Spark and considered apprenticeships funded through the UK Apprenticeship Levy. Levy funding would make it essentially free for Exertis to build that team. And the company could build a graduate scheme with upskilling included as a value-add for new hires.
Jonathan says what especially impressed him about Cambridge Spark was that faculty are actual data practitioners, many of whom hold PhDs from some of the world’s top universities. Also core to his decision to partner with Cambridge Spark was EDUKATE.AI, Cambridge Spark’s online learning platform, which struck him as better than the many other learning tools he’d seen.
Enrolling in Cambridge Spark’s Level 4 Data Analyst programme as Exertis’s first apprentice in April 2021, Jonathan wanted to elevate his existing conceptual knowledge to deploy machine learning for forecasting stock requirements and other practical applications. He says within the first four or five months, the potential for efficiencies driven by the apprenticeship throughout the broader business became clear.
He was now confident enough in the success of the programme to begin recruiting a “crack team” of staff to support their businesses. Participation in the apprenticeships has since grown to include more than a dozen employees from Exertis across both the Level 4 Data Analyst and the Level 3 Data Citizen programmes.
“The stuff that really caught the attention of the management team was a lot of the automation work the team was doing—using Python scripts to automate very complex, large data processing jobs. Automating manual processes and creating ML-powered recommendation engines was where we were freeing up a lot of time for the teams and very quickly making an impact and ROI.”
Levelling up with analytics support
Luke Kay is a Digital Analyst within the marketing team at Exertis who completed the Level 4 programme together with Jonathan. His job is to understand and help plan the team’s activities using business and third-party data from sources like Exertis’s website, Google Analytics, ad platforms, etc.
Half his job focuses on data and half on planning, forecasting and understanding consumer demand to prepare for purchasing.
He saw the apprenticeship as a stepping stone for upskilling into a data analytics role. He says his team has also gone from simply handling data and providing answers to adhoc questions, to telling stories with data and enabling people to take preemptive action. And the apprenticeship has made this transformation possible.
“The diversity and level of the teaching staff was surprising. We were taught by people who’ve run or worked in FTSE 100 companies, others who were biomedical scientists with a background in data science. It wasn’t fuddy-duddy, entrenched lecturers without any real-world experience. It was people who have done this and understand the value of it and want to impress upon you how valuable it is. Huge surprise. Absolutely phenomenal.”
Stock management is a key area where Luke has applied learnings from the programme within his role. In managing reporting around stock status, he’s able to help people who manage stock understand where it is, what it’s doing and which SKUs are at risk.
Website analytics is another area, with disparate tools to manage website and product content.
Eight analysts were spending a full day each week on consolidated reporting, often in Excel. And running queries on these two databases had never been done before. Luke has since built automated reporting through virtual machines running Python. Not only has this saved the analysts 64 hours per week, it’s also improved reporting, generating customised email reports for clients through Power BI dashboards or exported from Excel.
"I was able to make changes to the business almost immediately. Every time I had a meeting with my mentor or coach was a lightbulb moment where I was uncovering more use cases within the business.”
Apart from his tangible contributions to Exertis since his apprenticeship, Luke says the programme has brought him greater job satisfaction.
He aspires to do more advanced data science work, and Dr. Zabair’s team is able to support him in a kind of hub-and-spoke model.
“We’re providing input, but we’re not taking over that workload. He’s able to handle it but knows there’s a central support team available from us to help him make progress if he needs advice on better suited tools or approaches to solve a problem.”
Automating commercial reporting
Guillermo Dominguez is a Purchasing Finance Analyst in Exertis’s Commercial Finance team. He analyses stock and sales data to optimise purchasing decisions.
When he joined the Level 4 programme in September 2022, he’d been using Excel to manage a lot of data in massive spreadsheets that were tedious to maintain. He saw the potential for advanced analytics to help him improve in his job, such as reducing stock holding to free up working capital.
Within the first few months of the apprenticeship, Guillermo found an opportunity to apply learnings to a routine data task.
He had been managing a spreadsheet with tens of thousands of rows of inventory, PO data, sales and backorders, forecasting stock, fill rate and other data. He was using filters to sort and pull insights from the data in Excel, a process that took a few hours every week.
Since learning Python on the apprenticeship, he’s been able to run a script and complete the same task in only a few minutes.
“We do quite a lot of reporting using past data to find trends. But learning tools like predictive modelling will help us use data to improve our forecasting and optimise working capital.”
Bringing transparency to payment data
James Mochrie is an Assistant Accountant at Exertis. His work involves reconciling balance sheets, managing commercial costs with different brands and working with large sets of payment and sales data.
He started the Level 4 apprenticeship in November 2022 in hopes of one day shifting into a data analyst role. One aspect he’s enjoyed most about the programme so far is how he’s been able to implement learnings in his current role.
James’ first portfolio project focused on automating a reporting process from eBay. He used to spend two hours twice a month manipulating payment data in spreadsheets. Now he’s able to automatically process the data in Python and highlight any credit notes in an output format he can easily share with colleagues to address.
He says the apprenticeship has also given him more visibility in the company, as he’s being consulted in discussions about data and automation.
“Doing the portfolio project and applying the skills I learned helped to embed the skills I was learning. It was really cool to be able to use them myself in my job.”
Closing thoughts: How Exertis is clearing a path to operational excellence through data transformation
Exertis’s experience paints a clear picture of how data science, driven by strategic investment in upskilling via Cambridge Spark, yields tangible business outcomes.
From capital improvements of £100s of millions within the supply chain, to saving several full-time equivalents (FTEs) across different teams, apprentices have been driving value at Exertis. And the creation of a hub-and-spoke model—a central data science unit supporting embedded analysts—has amplified the company's data-driven decision-making capabilities.
The company's story is a testament to the transformative potential of data science when thoughtfully deployed and in alignment with an organisation's broader goals.
“I think the apprenticeships really bring about a different way of building skills of individuals in a business. In Exertis they’ve been really good because people have gained so much insight by delving into the vast data that we already have. We've got this new system, which houses the data centrally. By making use of this data, you embark on a journey where the more you discover, the more you are able to convert into actions that benefit the business. And learning skills to navigate large and varied datasets is a key part of it because you have to be able to understand the data that comes through these systems.”
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