Data Quality Audit

A data quality audit by ADVISORI is a structured, methodical inventory and assessment of your critical data assets based on defined quality dimensions such as completeness, consistency, timeliness, accuracy, and uniqueness. The process begins with a scoping phase in which we jointly define the relevant data areas, business processes, and quality requirements. This is followed by technical profiling analyses, rule-based quality checks, and interviews with data users and data owners to capture the business context. The audit concludes with a detailed results report containing concrete recommendations and a prioritized action plan.

The duration of a data quality audit depends heavily on the scope and complexity of the data areas to be reviewed. A quick assessment for a specific data domain can be completed within two to three weeks, while a comprehensive enterprise-wide audit typically takes four to eight weeks. On your side, we primarily need access to the relevant data systems and the availability of data users and data owners for interviews and alignment discussions. ADVISORI takes responsibility for the methodical management and the majority of the analytical work, keeping the burden on your internal resources to a minimum.

In the financial sector, companies are subject to a wide range of regulatory requirements that presuppose demonstrably high data quality, including BCBS 239, MaRisk, DORA, Solvency II, and the requirements of the EBA guidelines on data management and aggregation. A data quality audit by ADVISORI explicitly takes these regulatory frameworks into account and assesses your data quality not only from a technical perspective but also from a compliance perspective. Identified weaknesses are directly mapped to the relevant regulatory requirements, giving you a clear overview of your compliance gaps. Our many years of experience in the financial sector enables us to develop practical recommendations that both meet regulatory requirements and support day-to-day business operations.

An external data quality audit by ADVISORI offers decisive advantages over internal reviews: objectivity, methodological independence, and broad benchmarking knowledge drawn from numerous comparable projects in the financial sector. Internal reviews are often constrained by existing blind spots, limited resources, and a lack of specialized analytical tools. ADVISORI brings proven audit frameworks, specialized data quality tools, and industry-specific expertise that is difficult to build internally. In addition, an external audit report carries greater credibility and weight with supervisory authorities, auditors, and management than internal self-assessments.

The data quality audit is deliberately designed as a starting point for a sustainable improvement process, not as a one-time review event. Upon request, ADVISORI supports you from the prioritization of identified measures through the design and implementation of data quality rules to the establishment of a permanent data quality management function. We assist with the introduction of suitable tooling solutions, the definition of data quality KPIs, and the establishment of governance structures such as data stewardship. This ensures that the insights from the audit are translated into measurable and lasting quality improvements.

A data quality audit is fundamentally relevant for all business-critical data domains, but in the financial sector particularly for master data (customers, products, counterparties), risk and reporting data, financial data, and data in regulatory-relevant systems such as core banking platforms, risk management systems, or reporting infrastructures. Common triggers for an audit include upcoming migration projects, regulatory reviews, the introduction of new analytics systems, or recognized quality issues in ongoing operations. ADVISORI has extensive experience with heterogeneous IT landscapes and can include both on-premises systems and cloud-based data platforms in the audit scope. Together with you, we define during the scoping phase which data areas show the greatest need for action and should be examined as a priority.