Harnessing a Wealth of Data

When Dr Adeala Zabair joined Exertis as Director of Data Science and Analytics in February 2023, the company had been integrating SAP as its system for managing daily processes and core capabilities within the business.

A wealth of available data and possibilities for leveraging 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 data transformation has been a primary focus of Dr Zabair since joining Exertis. However, before that transformation could begin, the company needed to build a team with the right skills.

"There are several areas in the business that we think could be improved by looking at data-driven decisions: whether data allows us to standardise processes in the business, or data uncovers lot of insights that we can use to make better decisions."

Dr Adeala Zabair, Director of Data Science and Analytics at Exertis

Finding the Right Talent Solution

Jonathan Wagstaff, Director of Market Intelligence at DCC Technology, works at the divisional layer of the parent company that oversees the various businesses under its umbrella. He was deeply involved in Exertis, in particular, when in early 2021 he began to consider where data science could offer the greatest leverage for his team's distribution and wholesaling.

He knew he would need to cultivate a team with the skills necessary to bring data science to Exertis. But he didn't want to spend a fortune hiring talent externally for an initial proof of concept. That's when Cambridge Spark was recommended to him and he 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 create a graduate scheme with upskilling included as an added value for new hires.

Jonathan says what especially impressed him about Cambridge Spark was that the lecturers are real data practitioners, many of whom have PhDs from some of the best universities in the world. Also central to his decision to partner with Cambridge Spark was EDUKATE.AI, Cambridge Spark's online learning platform, which he found to be better than many other learning tools he had seen.

Enrolling on Cambridge Spark's Level 4 Data Analyst programme as Exertis' first apprentice in April 2021, Jonathan wanted to elevate his existing conceptual knowledge to implement machine learning in predicting stock requirements and other practical applications. He says within the first four or five months, the potential for apprenticeship-driven efficiencies across the business became clear.

He now had enough confidence in the programme's success to start recruiting an "elite team" of staff to support their businesses. Participation in apprenticeships has since grown to include more than a dozen Exertis employees on the Level 4 Data Analyst and Level 3 Data Citizen programmes.

"What really caught the management team's eye 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 building ML-driven recommendation engines was where we were freeing up a lot of time for teams and very quickly driving impact and ROI."

Jonathan Wagstaff, Director of Market Intelligence at DCC Technology

Levelling Up with Analytics Support

Luke Kay is a Digital Analyst within the marketing team at Exertis who completed the Level 4 programme alongside Jonathan. His job is to understand and help plan the team's activities using business and third-party data from sources like the Exertis website, Google Analytics, ad platforms, etc.

Half of his job focuses on data and the other half on planning, forecasting and understanding consumer demand to prepare for purchases.

He saw the apprenticeship as a stepping stone to upskill into a data analytics role. He says his team has also moved from simply handling data and providing answers to ad hoc questions, to storytelling with data and enabling people to take preventative actions. And the apprenticeship has made this transformation possible.

"The diversity and standard of teaching staff was amazing. We were taught by people who have run or worked in FTSE 100 companies, others who were raw biomedical scientists with a data science background. They weren't fusty, entrenched academics with no real-world experience. They were people who've done this and understand the value of it and want to pass on to you how valuable it is. A massive surprise. Absolutely phenomenal."

Luke Kay, Data Analyst at Exertis

Inventory management is a key area where Luke has applied learnings from the programme within his role. Managing inventory health reporting, he is able to help people managing stock understand where it is, what it's doing and what SKUs are at risk.

Web analytics is another area, with disparate tools to manage website content and products.

Eight analysts were spending a full day each week on consolidated reporting, often in Excel. And running queries across these two databases had never been done before. Luke has since built automated reporting via virtual machines running Python. This has not only saved analysts 64 hours per week, it has also improved reporting, generating personalised email reports for clients via Power BI dashboards or exported from Excel.

"I was able to make changes in the business almost straight away. Every milestone that I had a meeting with my mentor or coach was a lightbulb moment where I was discovering more use cases within the business."

Luke Kay, Data Analyst at Exertis

Aside from his tangible contributions to Exertis from 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 can support him in a sort of hub-and-spoke model.

"We are providing input, but we're not taking on that workload. He's able to manage it but knows that there's a core support team available on our end to help him progress if he needs advice on tools or more suitable approaches to solving a problem."

Dr Adeala Zabair, Director of Data Science and Analytics at Exertis

Automating Commercial Reports

Guillermo Dominguez is a Purchasing Finance Analyst on the Commercial Finance team at Exertis. He analyses inventory and sales data to optimise purchasing decisions.

When he joined the Level 4 programme in September 2022, he had been using Excel to manage lots of data in massive spreadsheets that were tedious to maintain. He saw the potential of advanced analytics to help him improve in his job, such as reducing inventory 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, purchase order data, sales and backorders, forecasting stock, fill rate and other data. He was using filters to sort and extract insights from the data in Excel, a process that took a few hours each week.

Since learning Python on the apprenticeship, he has been able to run a script and complete the same task in just a few minutes.

"We do quite a bit 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."

Guillermo Dominguez, Purchasing Finance Analyst at Exertis

Bringing Transparency to Payment Data

James Mochrie is a Ledger Assistant at Exertis. His job involves reconciling balances, managing commercial costs with different brands and working with large sets of payment and sales data.

He began the Level 4 apprenticeship in November 2022 hoping to one day transition to a data analyst role. One aspect he has most enjoyed about the programme so far is how he has been able to implement learnings into his current role.

James' first portfolio project focused on automating an eBay reporting process. He used to spend two hours twice monthly manipulating payment data in spreadsheets. He can now 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 is being consulted in discussions on data and automation.

"Doing the portfolio project and applying the skills I learned helped cement the skills I was learning. It was really cool to actually use them yourself in your work."

James Mochrie, Ledger Assistant at Exertis

Final Thoughts: How Exertis is Paving a Path to Operational Excellence Through Data Transformation

Exertis' experience paints a clear picture of how data science, driven by strategic investment in upskilling through Cambridge Spark, delivers tangible business results.

From capital improvements of hundreds of millions of pounds within the supply chain, to saving several full-time equivalents (FTEs) across different teams, apprentices have been delivering 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 implemented thoughtfully and in alignment with an organisation's broader goals.

"I think apprenticeships really bring a different way of developing skills of individuals in a business. At Exertis they've been really good because individuals have gained so much knowledge by digging into the vast data we already have. We have this new system which houses data centrally. By making use of this data, you go on a journey where the more you uncover, the more you can turn into actions that benefit the business. And learning skills to navigate large, varied datasets is a key part because you have to be able to make sense of the data that's coming through these systems."

Dr Adeala Zabair, Director of Data Science and Analytics at Exertis