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Diane Berry, Chief Data and Analytics Officer, Phoenix Group

Diane Berry, Chief Data and Analytics Officer, Phoenix Group

Describe your career to date

My career is fun, and I love it! I did not set out with a particular plan, although I always knew that I had a love of technology and building things. After graduating in New Zealand with a bachelor’s in Technology, I embarked on a software engineering career where we designed, built, and delivered many applications including payment systems, mail delivery, asset management, and commodity trading applications. It was when I was working that trading environment that my love of maths was rekindled, and I returned to university (UCL, London) to go on to work and complete a PhD in what would today be considered Data Science and artificial intelligence (AI).

This was in the 2000s and the whole genome had just been decoded, which led to an explosion of data (over 5 million data points) on individuals that had been studied from birth to death. My primary focus was to develop robust methodologies that were reproducible. I was fortunate enough to be an author of acclaimed papers and involved multi-national collaborations with researchers around the globe.

Shortly after leaving academia, I joined Bain and Company, a top tier management company, right at the time that many industries were starting to take seriously what the tech natives like Amazon, Netflix, and Google were doing in this space. I had an incredible time at Bain developing our own internal data capabilities across cloud-led technology and developing and managing teams. I thrived working across many industries, companies, and regions to develop client’s data strategies and to deliver value, whether that be light-house use-cases, operating models, or their capabilities.

Just under three years ago, the opportunity at Phoenix Group came up and I could not resist the chance to put my feet under desk and to stay the journey. There is always a concern when you leave consultancy that you will miss the fast pace and ever-changing work. At Phoenix, I am pleased to say that this is not the case as we are on a clear multi-year mission to serve people and their families to help enable long-term savings and pensions. It is a mission that is personally dear to me as I have witnessed my parents’ struggles with money to retirement and throughout retirement, as many of us do. Upon joining, I initially had a team of five people, which we have successfully grown in three years to near 70 team members. I have been able to do this by developing and delivering a data strategy with our customer at its heart. Part of this journey was by bringing data talent across the business to join me on this mission, and bring in capabilities where we did not have within the team. Together, we have delivered a Customer Data Lake that is central for our customer strategy, as it brings together customer data across our entities, so we are able to meet our regulatory requirements, understand, and engage our customers in value-added way. None of this is possible without a strong team; I am passionate that people develop and do so in meaningful way.

How are you developing the data literacy of your organisation, including the skills of your data teams and of your business stakeholders?

When I joined Phoenix, I had a team of five people, of which has now grown to about 70, in under three years that easily flexes upwards. I spearheaded and led the Phoenix Customer Data Strategy that designed, built, and delivered a Customer Data Lake, which was a big ask of the team. To be able to get to that point, it meant the Data Strategy had to make business sense and win over people’s hearts and minds, so they are willing to make the investment. Beyond the financial investment, leaders had to take time out of their day to engage, to be part of what we are doing, and encourage their teams to do the same.

The Group Data Office team was the key component to it, and I had the fortune to bring already trusted colleagues together within the single team, which made it easier to convince the rest. As a team, we do the same as many others do in organisations; we run regular educational sessions, send out regular newsletters, maintain an intranet site and update and broadcast it regularly, and present to our boards and executive committees. We try to take every advantage we have; however, we are not complacent and completing the DataIQ literacy survey late last year has been particularly insightful for us. This year, we will be doubling our efforts, engaging and educating in many new ways that we have not tried before such as internal podcasts and co-hosted external events.

How are you preparing your organisation for AI adoption and change management?

Firstly, I love AI with the pace of evolution and the ongoing opportunities it brings. We have three initiatives that are interlinked to deliver our key objective.

Firstly, we need to develop a clear AI strategy which encompasses the vision, the opportunities and threats, capabilities, and operating model required to succeed, roadmap, and lay the foundations for a business case for investment. We are working through that right now and already have several key stakeholders actively following progress and inviting me and the team to speak with them.

Secondly, as delegate for our Group COO, we own and operate our internal risk policy on information management, which we have updated to include AI and use of data to AI. In collaboration with colleagues, we are developing and deciding on our AI principles, framework, and controls through data risk forums that incorporate leadership across the business, IT, information security, and data protection. The more engaged and lively the debate, the better!

This leads me onto the third initiative; ensuring the foundations are in place, so that when colleagues do debate, we have a core definition and understanding of what the types of AI are that we care about at Phoenix, beyond what is commonplace in a Financial Services firm. Additionally, we have the core technology, as delivered by the Customer Data Lake, that provides AI capabilities with the appropriate security and governance model in place.

Our key outcome to all of this is to have and AI KPI initiative that keeps us motivated and is underpinned by the value returned. As with data science use-cases, I believe that experimentation will be key to develop capabilities and greater understanding of what will work within the business. As AI use-cases mature, some will become embedded within solutions built by teams leveraging AI capabilities – such as building on customer personalisation through content creation – and many will likely be standalone. However, those standalone use-cases will provide invaluable insight and value and will lay the pathway for the next wave of AI use-cases to stand on steady shoulders.

Diane Berry
has been included in:

100 Brands 2023 (EMEA)
100 Brands 2024 (EMEA)

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