Data Strategy & FiDA: Ready for Open Finance

The EU's push towards Open Finance through the Financial Data Access (FiDA) Regulation marks a new era of data sharing: financial institutions will be required to share customer data with third-party providers in a standardised format upon request, giving customers greater control over their data and better access to innovative financial products.

Whilst the final design of the FiDA regulatory framework is still taking shape and organisations are awaiting clear guidance, these foreseeable regulatory changes already demand fundamentally new approaches to data management today. FiDA is merely the tip of the iceberg and forms part of a broader transformation scenario: Artificial Intelligence is automating processes, shifting customer expectations are calling for platform-integrated solutions, and the Internet of Things (IoT) is revolutionising risk models through real-time data.

The four trends mentioned reinforce one another and are creating a "perfect storm" of change. FiDA is forcing open data silos, AI requires high-quality data foundations, evolving customer expectations demand data-driven services, and IoT is generating entirely new data sources.

The common denominator across all these trends: they all require a well-considered, future-proof data strategy as their foundation. This is precisely the message that Intero Consulting shared at the Versicherungsforen Leipzig on the topic of Open Finance.

A recent study confirms the urgent need for action.

The good news is: the problem has been recognised. The bad news: implementation is lagging behind. A recent study of 210 global insurers illustrates the challenge clearly: only one third are comprehensively digitalised, and only a handful achieve the highest level of data maturity. Despite significant investment in digital transformation, most insurers are still grappling with fragmented data landscapes and outdated legacy systems.

What is holding progress back? In our day-to-day project work, we encounter three recurring, central problems:

• Case-by-case data management: Data is made available for dedicated use cases but is not used across the organisation.
• Lack of ownership: No clear assignment of data types to internal responsibilities.
• Fragmented data landscape: In one study, 65% of executives confirmed that nobody in their organisation has a complete understanding of, or access to, all their data.

These problems are solvable, however, if organisations are willing to embrace new approaches.

The key lies in a paradigm shift: moving away from data silos towards a product-oriented Data Mesh approach. A future-proof data strategy is built on three fundamental pillars:

1. Product-Oriented Data Management

Each specialist domain independently develops its own Data Products and makes them available via the harmonised data infrastructure (including a self-service platform). Data Products bundle individual data sets for a specific purpose. Rather than maintaining data on a case-by-case basis, this approach creates reusable Data Products with clear ownership, which can be leveraged for further use cases across the organisation. These Data Products can be seamlessly used for FiDA-compliant data sharing (via API), AI applications, or strategic partnerships.

Practical example from insurance: Data can, for instance, be divided into four domains: 1) The Customer Data domain covers master data, demographics, contact information, and product usage history. 2) The Claims Processing domain focuses on claims reporting, processing status, and fraud detection. Further domains can be added depending on the business area (such as 3) Underwriting or 4) IoT & Telematics).

2. Scalable Data Infrastructure with a Self-Service Platform as a Toolkit

A modern, harmonised infrastructure forms the technical backbone and enables the practical implementation of Data Products. A self-service data platform is the central anchor point of this infrastructure. It provides specialist departments with a comprehensive toolkit: data organisation with a data catalogue and data lineage tracking, policy management for interface standards (API) and access management, reporting and monitoring for quality assurance and usage reports, as well as the underlying technology platform. These tools enable teams to develop Data Products independently, without relying on central IT teams.

Practical example from insurance: The self-service data platform specifies Microsoft Fabric as the central technology and provides specialist departments with the relevant competencies and guidelines (e.g. API interface standards). Through the platform, domain teams gain access to unified standards, governance requirements, and best practices, enabling them to independently develop and manage Data Products based on MS Fabric.

3. Established Governance with Senior Leadership Commitment

A successful data strategy requires clear governance structures as its foundation. Clear responsibilities and consistent rules create this foundation. A Chief Data Officer oversees the strategic direction, whilst Data Domain Teams operate in a decentralised manner and implement the centrally defined governance for their Data Products. This structure combines strategic consistency with operational flexibility.

Practical example from insurance: The "Customer Data" domain team independently develops Data Products for KYC processes. The Data Platform Team operates the central infrastructure, the Enabling Team provides support through best practices, and the Governance Group ensures cross-functional standards are upheld. Through consistent documentation and automated quality checks, all teams can act swiftly without compromising compliance.

"A data strategy doesn't happen by itself, but it's no dark art either" – transformation doesn't happen overnight, and Intero Consulting will guide you systematically on your journey towards a future-proof data strategy: