Financial institutions continue to invest heavily in data platforms and artificial intelligence (AI), yet many still struggle to realise consistent performance gains. In the Journal of Financial Services article Data culture: the silent hero of data value creation, Keeley Miles, Chief Financial Officer at FCE Bank PLC, and Gary Paul, Partner and UK Data Practice Lead at Projective Group, argue that the missing link is not another layer of technology, but a robust data culture. This encompasses the shared values, behaviours and norms that shape how people trust, interpret and act on data.
In a highly regulated and risk‑sensitive environment, data culture emerges as a capability as critical as any system or model, making it a pressing issue for boards and senior leaders today.
The authors position data culture as a strategic organisational asset, not a technical afterthought. A strong data culture exists where data is treated as a strategic resource, trusted as a basis for decision‑making and embedded in daily work across functions. From both advisory and executive viewpoints, the article emphasises the need for explicit attention to regulation, ethics and risk discipline. Where data culture is weak, technology investments remain isolated and fail to translate into enterprise‑wide value.
Paul and Miles underline that advanced analytics and AI only create value when people are willing and able to question assumptions, understand limitations and integrate evidence with professional judgement. From a board and CFO perspective, weak data culture drives two equally problematic outcomes: over‑reliance on models or the dismissal of data‑driven insight. Both undermine decision quality and elevate institutional risk.
As AI and machine learning become more pervasive, the article makes clear that AI risk cannot be separated from data risk. Strong data cultures support better credit, risk and capital decisions, enable responsible innovation such as personalised products, and strengthen the ability to detect weak signals, stress test and respond to shocks. Without this cultural underpinning, even sophisticated AI capabilities struggle to deliver sustainable and trustworthy outcomes.
Data and AI do not fail because institutions lack technology, but because they lack a shared culture that trusts data, challenges assumptions and turns insight into action. In financial services, data culture is what ultimately determines whether investment becomes value.
For financial services leaders, the message is unambiguous: data culture is built, not bought. The authors highlight leadership responsibilities spanning executive and board roles, including visible role‑modelling of data‑informed decision‑making, investment in accessible but well‑governed data infrastructure, role‑specific data literacy, and incentives that reward evidence‑based challenge. Treating culture as a capability to be measured and deliberately nurtured, rather than a slogan, is presented as essential to converting data and AI strategies into tangible business results.
Regulatory expectations around risk data aggregation, explainability and fair use of data continue to intensify, while sustainability and ESG reporting increasingly depend on credible, auditable metrics. Paul and Miles position data culture as the foundation for meeting these demands. Institutions with mature data cultures are better placed to sustain clear ownership, consistent definitions and reliable data lineage, maintain stakeholder trust, and support credible progress on environmental, social and governance goals. Those that neglect data culture risk falling behind on AI adoption, regulatory confidence and organisational resilience.
For deeper insights and the full article, read the article in the Journal of Financial Services.
The Projective Group Institute’s Journal of Financial Services (JoFS) provides structured insights on developments in the European financial sector. Each edition brings together contributions from practitioners, academics and regulatory experts to help readers understand key changes in the industry.
This edition examines how data and Artificial Intelligence are influencing financial services. It looks at how modern analytics and evolving regulations such as GDPR, DORA and the EU AI Act are raising expectations for data quality, governance and oversight. Through concise, thoughtful articles, the edition highlights the practical implications for decision‑making, risk management and operational resilience.
