Headline Partner

Phil Yeoman, Group Chief Data Officer, Cardano

Describe your career to date

I am an experienced data executive, with a focus on pensions, fiduciary and investment management and expertise in aligning business strategies with data delivery to get the most out of the organisational investment. I also have a proven track record in delivering complex data-led transformation programmes in both the public and private sectors.

I have worked within the field of data for more than 20 years; covering a wide career portfolio. As a senior civil servant, I led economists and analysts in using data to shape social policy and economic reforms; I wrote the code of practice for the data requirements of Workplace Pensions and built and led data governance for the UK Pension Regulator.

In more recent years, as the Cardano Group CDO, my career has focused on fiduciary and investment management. Cardano Group is a market leader in providing risk and investment management services, including administration and investment management of one of the UK’s largest workplace pensions, designed to make pension outcomes more stable and robust.

My passion is for data-driven transformation to deliver improved business outcomes and enhanced customer service, blending tried and tested methods with innovation and new technologies. To this end, I have recruited, developed and led teams/functions/directorates in the practical application of data strategies, data management, data science and analytics, governance frameworks, enterprise architecture and technical service delivery.

I am a member of the Data Management Association (DAMA), an active contributor to the wider data community and a regular speaker on data governance and data cultures throughout the UK and Europe.

What stage has your organisation reached on its data maturity journey?

When we started our data governance project, we did not fully understand how our data was produced, transformed and, ultimately, used. Now, all that has changed. Our data estate is now mapped, modelled and catalogued. In a single view, I can show the business where their data resides, how it flows through systems and applications, what data quality rules apply, the quality score, and what data is subject to regulation and legislation.

Tell us about the data and analytics resources you are responsible for

My resources are spread across three offices, in London, Nottingham and Rotterdam. I believe in lean and agile teams, so I keep my core team small, with additional data SMEs spread throughout the organisation. I flex and expand my team dependent on the type and nature of the task and project, contracting additional skill specific resource as and when needed.

What challenges do you see for data in the year ahead that will have an impact on your organisation and on the industry as a whole?

The increased focus on data within organisations is of course to be welcomed but with it comes challenges. Driving revenue and growth through the use of data places pressure on our teams and technologies to deliver. Many of us are transforming our data estates, moving from legacy infrastructure to the shiny and new. But we need people with the skills and knowledge to support the transformation; the data modeller, architect, engineer, scientist and governance. It is access to these key resources that I see as challenges that I and many others will face over the coming years.

Have you set out a vision for data? If so, what is it aiming for and does it embrace the whole organisation or just the data function?

Our increasing focus is on making sure that we apply constant standards and approaches, move away from spreadsheets to data visualisation and culturally embed the principle that we are all accountable for the quality of data throughout its lifecycle.

Have you been able to fix the data foundations of your organisation, particularly with regard to data quality?

The majority of our data estate is now mapped, modelled and catalogued. In a single view, I can show the business where their data resides, how it flows through systems and applications, what data quality rules apply and the quality of that data across. We have followed the DAMA Data Quality Standards methodology, covering the full range of quality dimensions.

Phil Yeoman
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