Data Governance and Data Quality: the value of data starts with quality

Laura Coletto is Senior Advisor at SimCorp Italiana and has thirty years of experience in investment-area projects at a major insurance group. During SUN 2026, she told us about the increasingly strategic role that Data Governance and Data Quality are taking on in the insurance sector.

Q: Why is there so much talk today about Data Governance and Data Quality?
In recent years, decision-making at insurance companies has become increasingly data-driven. Core processes such as risk management, pricing and reporting depend heavily on the quality of the available data. At the same time, we are seeing continuous growth in the volumes of data to be managed, in the number of sources, formats and transactions. In this scenario, incomplete, inconsistent or incorrect data can have significant impacts in terms of operational issues, regulatory non-compliance, as well as economic and reputational impacts.

Q: How much do regulatory aspects affect the issue of data quality?
A great deal. For insurance companies, the topic is not just an organizational best practice, but a genuine regulatory requirement. Solvency II explicitly requires that the data used be appropriate, complete and accurate, and that control, governance, traceability and auditability processes be in place. Supervisory authorities are also expected to verify the quality of the data used for internal models. For this reason, Data Quality must be seen as a corporate responsibility supported by structured, ongoing processes.

Q: What role does Data Governance play in this context?
Data Governance represents the framework through which an organization governs its data. We are talking about a set of roles, processes, policies and standards that make data reliable, consistent, available and easy to use across the company. The ultimate goal is to ensure that everyone works on the same information and that this information is correct and managed in a controlled way.

Q: What are the fundamental elements of a Data Governance framework?
There are four key elements.
The first concerns roles, in particular that of Data Owner and Data Steward, who have clear responsibilities for data management and quality.
The second concerns processes, which define how data is created, modified, used and controlled.
The third element is standards, essential for ensuring uniformity, efficiency and compliance.
Finally, there is the Data Dictionary, which provides shared definitions and a standardized description of corporate data, aligning the language used across different business functions.

Q: What benefits can an insurance company gain from a proper Data Governance framework?
The benefits are numerous. First and foremost, a significant improvement in data quality and a reduction in the risks arising from incorrect or incomplete information. It also increases operational efficiency, reduces manual activities and facilitates regulatory compliance. Overall, good governance enables faster, more reliable decision-making.

Q: What is the connection between Data Governance and Data Quality?
Data Governance creates the organizational and methodological foundations, while Data Quality represents its practical application. We could say that governance establishes rules and responsibilities, while Data Quality constantly measures and monitors how accurate, complete, consistent, up to date and reliable the data is.

Q: How should an effective Data Quality process be organized?
Today it is essential to adopt a real-time approach, or in any case one that is as timely as possible. This requires tools that support structured workflows, traceability and continuous monitoring. In particular, it is important to be able to define standard and customized validation rules, trigger automatic alerts, manage review and remediation processes, and maintain a complete audit trail of all activities carried out. Only in this way is it possible to act quickly and ensure continuous control over data quality.

Q: What is the relationship between operational processes and data quality?
It is an extremely close relationship. Efficient processes generate better data, while quality data make processes even more effective. For this reason, adopting Data Management platforms that enable centralized data management, matching platforms for reconciling transactions with counterparties, and infrastructures such as SWIFT, which enable STP settlement processes and near-real-time reconciliations with banks, represent strategic investments. They not only improve operational efficiency, but also directly contribute to increasing the quality, timeliness and reliability of information.

Q: How will the introduction of T+1 Settlement affect these issues?
The move to T+1 Settlement will make data quality even more crucial. By reducing the time windows available for checks and corrections, organizations will need to rely on highly automated processes and on data that is correct from the source.

Q: A final message for the insurance market?
I like to sum it up in one sentence: "There is no Data Quality without robust processes, and there are no truly solid processes without quality data." Data quality is not a standalone goal, but the result of effective governance and well-designed processes. Investing in Data Governance, Data Quality and operational innovation means building the foundations for better decisions, greater efficiency and full regulatory compliance.