At M&G plc, we believe that creating an inclusive culture is critical to our success. By bringing together individuals from diverse backgrounds, we can better engage colleagues, uncover new ways of thinking, and better serve the needs of our customers and clients. We spoke to Denholm Hesse, Head of Data Engineering and Analytics at M&G plc about how the data and insights teams work to ensure a diverse and inclusive workplace, why diversity in data science is so important to him, and why a career in data is open to anyone with an enquiring mind.

1. Can you tell me about your role?

I head up the data engineering and analytics teams within customer technology. We build all the data and insights capabilities for the Customer and Distribution business. This includes data platforms that provide our self-serve dashboards, analysis to support business decisions and data science. We want to see our customers, clients, and colleagues in a position where they can make data informed decisions as frequently as possible so they can meet their savings and investment goals.

2. What have you delivered recently that has added value to the organisation?

Great question! There are a few things I would call out; the first is a product we have built which will help with the growth of The Advice Partnership (TAP) - our network of self-employed advisors that sits within M&G Wealth. The tool supports our mission to fill the advice gap in the UK by helping us explore different advisor segments and identify which advisors are best suited to joining TAP. Secondly, we have done a lot of work in our Wealth Operations team looking at how we can improve the experience for advisors and customers contacting our call centres. By analysing customer and advisor calls we can get a better understanding of how best to equip our colleagues to support queries, and enhance our digital capabilities to provide support on-demand. And finally, we’ve developed a machine learning algorithm that helps match interested customers with the best financial advisor for them.

3. How do you ensure you avoid unconscious bias when recruiting for your team?

We have several initiatives in place to help avoid unconscious bias. We share job descriptions with our whole team for feedback and comment – this means we can get feedback from our own diverse team on wording or phrases that may discourage certain groups of people from applying for our roles.

We also have a Careers framework that provides us with objective measurements of whether a candidate would be a good fit for the role and eliminates ambiguity. This in turn helps to decrease bias in hiring decisions and encourages interviewers to address capabilities rather than personality. This same tool is used to help attract and retain a more diverse team, as well as encourage promotion based on true proficiency rather than personality. Finally, we have a recruitment tracker where we can identify where we tend to lose people in our recruitment process.

4. Why is diversity in data science so important to you?

We take a relatively simple view here, we want to provide equal opportunities and support to people irrespective of their background or gender. This means we can hire, promote, and celebrate a diverse team and in turn build the best analytics solutions that fit a modern diverse society. A diverse team brings diversity of thought which in turn builds products that consider the perspectives of diverse groups of customers and clients.

5. What do you think puts women off a career in data science? How can that be challenged?

I think it’s hard for me to say that I truly understand why it’s harder for women or underrepresented minorities to get into careers in data – I haven’t been in that position and there are many things that I won’t have experienced. I think recognising that is important. I’ve spent a lot of time with my female colleagues getting to understand what they’ve experienced so I can share some of their insights but I’ll also continue to learn.

Particularly at the beginning of people’s careers, there’s often a misconception that data science is purely a technical job. People sometimes worry they are not ’techy’ enough and are put off applying. It’s important here to remind people that there’s lots of non-technical parts to data roles such as storytelling, inferring results/conclusions and presentation of data visualisation which are often overlooked. Data on its own is useless, we need people to interpret it, to turn it into insights and then tell stories with it.

6. Why have you partnered with Women in Data?

They have a mission that’s really closely aligned to ours, so the partnership made complete sense to us. Women In Data will help us right across our Diversity and Inclusion (D&I) mission in how we encourage women and girls into data careers, how we ensure we have balance and inclusivity within our own team through recruitment and initiatives like their jobs board and they’ll support us on continuing to build out new initiatives to support our D&I mission. You can find out more about Women in Data here: https://womenindata.co.uk/mg/