5 Trends That Will Drive Data Quality in 2025

As data becomes increasingly central to business strategies, ensuring its quality remains a top priority. However, studies cited by data governance platform Collibra suggest that although investments in data and analytics are a top priority for 88% of companies, only 37% say their efforts to improve data quality have been successful.

In such a scenario, what are the factors that organizations must look at in 2025 to enhance and strengthen their data quality? A webinar by Collibra examined five key trends that will shape the data landscape this year and the best practices to build a robust and future-proof data quality program.

1. Accurate AI and Analytics

Speakers during the webinar stressed that organizations must remain focused on finding data quality issues before they become business issues, to ensure reliable data for both traditional machine learning models and emerging AI technologies like generative AI. This can be achieved in several ways:

Profiling data to identify issues like missing values, outliers, and skewed distributions that can create bias and errors in models.

Finding hidden anomalies in data pipelines feeding production algorithms, such as schema changes, missing values, and data type changes that can degrade model performance.

Accelerating notification and response through proactive notification to stakeholders about data quality issues.

Integrating with data science workflows through APIs on platforms like Collibra that enable easy integration with pipeline orchestration tools.

2. Quality in Data Products

The quality of data in data products will remain an important focus area for organizations in 2025. A best practice recommended by Collibra is to identify the right business entities like customers, products, and suppliers that are often the foundational data for data products.

Organizations can pursue automatically tagging fields containing sensitive data and linking governance policies to explain appropriate handling. They can utilize Collibra’s lineage capabilities that document data transformations and controls, providing transparency for auditors to verify that data is processed according to defined business rules.

3. Audit-Ready Reporting

Organizations will not only need broad connectivity and support for different data sources and file types but also centralized data classifications and metric definitions to ensure consistency. Automated data quality checks and creating custom rules will play an important role. The webinar highlighted how Collibra can help with the latter for users without SQL knowledge by using its rule writer solution.

The key focus is on providing the visibility, transparency, and automated controls needed to demonstrate regulatory compliance and avoid penalties, particularly for complex regulations like BCBS 239 in banking.

4. Data Quality During Cloud Migration

The webinar emphasized identifying and addressing data quality issues before and during migration to the cloud especially for organizations migrating from their legacy SAP systems to SAP S/4HANA. Ensuring data quality during a migration can help organizations manage their cloud costs more effectively. By ensuring data integrity and reducing migration errors, organizations may be able to avoid unexpected cloud storage and processing costs. According to the speakers, ensuring reliable data for the cloud migration process itself can help in this area.

5. Holistic Governance

Lastly, a holistic governance platform can address several key aspects of data management by providing unified data and AI governance, centralized management and policy enforcement, end-to-end lineage and traceability, integrated data quality and collaborative governance. This breaks the siloed tools for catalog, lineage, governance, quality, and observability, which can lead to inaccuracies, inconsistencies, and difficulty scaling governance.

What This Means for SAP Insiders

Data quality is crucial for SAP users. SAPinsider research shows that the need to ensure effective data governance and quality (35%) is the most important factor driving strategy for data, integration and SAP Business Transformation Platform (BTP). The study recommends that organizations should ensure plans for data governance and quality are part of all future data, integration, and platforms strategy.

Data visibility is important to ensure its quality. Organizations must utilize platforms and solutions that provide visibility of data quality within the data catalog. For example, Collibra helps data product teams determine the best data assets to use by automatically applying out-of-the-box data quality rules to data sets and allows creating custom rules to validate the output of data products.

AI governance is gaining momentum. As organizations prepare for a future where AI plays a more significant role in the way that they run their businesses, ensuring data quality across the enterprise is becoming increasingly important. In January, Collibra advanced AI governance with the launch of its EU AI Act Assessment Tool within its platform to help organizations streamline compliance efforts, for responsible AI deployment and enhancing internal trust.