1. Introduction

High quality data is essential to ensure:

• regulatory compliance

• optimal internal decision-making

• the Open Finance ecosystem can deliver the intended customer value in a reliable manner

This section provides a guideline on data quality measures an LFI can implement to ensure high quality data, and defines what constitutes high-quality data for the purposes of regulatory compliance and internal decision-making. This includes specifications on data accuracy, consistency, completeness, and timeliness.

2. Data Quality Measures

Below is a guideline of internal measures that should be implemented to ensure compliance with data quality standards.

• Establish Data Governance Policies: Develop comprehensive data governance policies that outline the roles and responsibilities of different stakeholders within the institution regarding data management. This ensures accountability and clarity in data handling and processing.

• Training and Awareness: Conduct regular training sessions for all relevant staff on the importance of data quality, how to achieve it, and the consequences of inadequate data. This helps to align everyone's understanding and commitment to maintaining high standards.

• Implement Data Quality Tools: Use technological tools to automate data quality checks and validations. This reduces human errors and enhances the ability to quickly identify and address data issues.

• Continuous Monitoring and Reporting: Set up systems for continuous monitoring of data quality, with regular reporting to management. This helps in quickly identifying trends of data inadequacy and taking corrective actions in a timely manner.

• Feedback Mechanisms: Create feedback loops within the organization where data quality issues can be reported and addressed. This promotes a culture of continuous improvement.

• Vendor and Partner Alignment: Ensure that external partners and vendors meet the same data quality standards as the institution. This might involve conducting audits and providing them with similar training and resources.

3. Data Quality Principles

This section outlines the data quality principles by which LFIs are required to adhere to, defining expectations in terms of accuracy, completeness and timeliness. LFIs are subjected to ongoing monitoring to ensure adherence to these principles.

3.1 Accuracy

LFIs MUST ensure that all data is accurate, consistent, in the correct format, and that all data is accurately mapped according to the Standard. All data is expected to accurately reflect the real-world values it represents, in the required format.

3.2 Completeness and Consistency

Completeness refers to the extent to which all required data is present. LFIs MUST provide complete data containing all necessary values without any missing elements. LFIs MUST complete all mandatory fields and SHOULD complete any optional fields for which they are able to provide the data.

Consistency refers to the uniformity of data across different systems, datasets and sources. LFIs MUST present consistent data regardless of where it is stored or accessed.

3.3 Timeliness

Timeliness refers to the availability of data when it is needed. LFI data MUST be up-to-date and available in accordance with benchmarks defined in Availability, Performance and Usage Benchmarks.