The demand for trusted data is at an all-time high.
Many companies are investing heavily in artificial intelligence (AI) and advanced analytics, seeking a strategic advantage over their competition. Data democratization is receiving more attention than ever, and data analytics is becoming a central element in compliance. Data governance is going mainstream as well, prompting companies to focus more attention on managing data quality at scale.
Here are some of the most important trends in data quality for 2025.
AI Powered by Data Quality
The discussion surrounding AI continues to evolve as its capabilities reshape various industries by automating tasks and boosting productivity. The rapid adoption of generative AI among consumers since the launch of ChatGPT highlights this trend. AI technologies can address numerous challenges related to data integrity by providing agility and insights into how to improve data quality. They can deliver automatic assessments of data quality that users can modify without requiring extensive manual analysis. However, low-quality data presents substantial risks; without reliable information, AI initiatives may falter due to misleading insights or biases that fail to provide business value. Ensuring high-quality data mitigates these risks while enabling trustworthy AI outcomes that support confident decision-making.
Data Democratization Relies on Trustworthy Data
The movement toward data democratization empowers stakeholders across organizations to make faster and more informed business decisions. This trend has led to an increased demand for data marketplaces that enable quick self-service access to reliable information. However, these democratization efforts come with risks; poor-quality data can lead to serious misjudgments. According to IDC’s Data Trust Survey, trust in data quality tends to be lowest among front-line employees in operations and sales roles, while executives generally exhibit greater confidence in their decision-making data. This disparity underscores the importance of fostering trust in data as organizations pursue greater democratization. When confidence is low, utilization diminishes, resulting in missed opportunities. Proactive and scalable data quality programs can enhance overall confidence in data integrity and yield more accurate insights for improved decision-making.
Scalable Data Quality Solutions in a Cloud-First World
The rise of cloud-based analytics platforms has made large-scale computational processing accessible to organizations that previously found it prohibitively expensive. The digitization of business operations, coupled with the proliferation of mobile devices and affordable IoT sensors, is generating vast amounts of new data. As our world becomes increasingly interconnected, organizations must adapt their data quality initiatives to manage this growing scale effectively. In the past, companies primarily dealt with a limited number of internal datasets, often focusing on issues like duplicate records and declining customer database quality. Today, the complexity and volume of data challenges have multiplied, necessitating a scalable approach to managing data quality. Effective solutions now include enhanced tools that allow for comprehensive discovery, profiling, and cataloging of data while establishing sophisticated business rules that define what constitutes high-quality data. Automation is becoming essential for identifying potential data quality issues promptly.
Compliance is Dependent on High-Quality Data
Organizations are increasingly allocating resources to meet evolving reporting standards and regulatory requirements. Regulators are intensifying their demands for timely and accurate reporting while expecting robust governance and quality assurance measures. Maintaining high-quality data is essential for compliance with privacy regulations and minimizing reputational and financial risks.
Strong Data Governance Enhances Data Quality
Data governance has evolved from being an optional feature to a fundamental necessity for organizations looking to leverage their data effectively. A strong governance framework aligns people, processes, and technology while ensuring that the quality of data is well understood and trusted. Organizations with established governance programs report significant improvements in both analytics (57%) and overall data quality (60%). As business environments grow more complex due to digital transformations and regulatory pressures, a solid governance framework becomes crucial for ensuring accurate, complete, and relevant information.
Want to stay ahead in the world of data quality and observability? Subscribe to the Datachecks Newsletter and join a community of forward-thinking professionals. Get the latest insights, best practices, and exclusive updates delivered straight to your inbox. Sign up now!
