Building a Business Data Fabric with Reliable Data

⇨ Data quality is critical for businesses to avoid disruptions, incorrect decisions, and regulatory challenges, especially with increasing reliance on AI and stringent data regulations.

⇨ Traditional manual data verification methods are insufficient; organizations must adopt automated solutions using machine learning to ensure data integrity and streamline data management.

⇨ Collibra provides innovative tools for data quality monitoring, enabling businesses to create and manage data quality metrics easily, thereby improving data reliability and supporting effective decision-making.

As businesses rely more on data as a key asset, ensuring its quality has become essential, especially with the rapid advancements in AI. However, many data leaders still find themselves doubting the reliability of their own data, which can create significant challenges across the organization. Poor data quality can have serious consequences, including business disruptions, incorrect decisions, and a loss of customer trust. Additionally, unreliable data can create regulatory or legal challenges, such as non-compliance with regulations like GDPR, CCPA, HIPAA, and Sarbanes-Oxley, which can result in significant financial penalties and legal issues.

Inaccurate data also undermines the effectiveness of AI and machine learning models, leading to compromised outcomes. Given the growing amount of data, the surge in AI usage, and more stringent regulations, the old ways of manually checking data just are not workable anymore. Embracing automated solutions that use machine learning can help maintain data integrity and support better decision-making.

Data reliability is essential for creating a robust business data fabric, especially as data volumes grow and AI becomes more integrated into business operations. Organizations need scalable, continuous solutions to manage data effectively, ensuring accuracy and reducing the costs associated with unreliable information.

Challenges with ensuring data reliability

Ensuring data reliability in today’s digital landscape is a complex task due to the widespread use of multi-cloud and hybrid cloud environments, alongside various disparate data sources. This diversity often results in a lack of centralized data management, which can lead to inconsistencies, duplications, and gaps in data integrity. As the number of data sources grows, so too does the risk of failures, making it challenging to maintain a consistent, reliable data ecosystem. Another significant challenge is data fragmentation, where ownership, accountability, and policies are often unclear or poorly defined. This fragmentation is further compounded by the inconsistent standards, formats, and practices across different departments or teams, leading to additional hurdles in achieving uniform data reliability. Such discrepancies can cause misalignment in data handling and hinder the overall effectiveness of data-driven decision-making.

For example, data engineers and data stewards often face challenges with data reliability due to downtime and the complexities of preparing data, especially with the limitations of manual rule writing. Implementing a robust data reliability framework helps overcome these issues by using machine learning to automate anomaly detection and rule creation, streamlining data preparation. Reliable data measures are crucial as they enhance decision-making by ensuring data accuracy and consistency, reducing the risk of errors and inefficiencies, and improving overall operational efficiency.

Automate anomaly detection and rules with Collibra

Collibra empowers businesses with clear data quality (DQ) metrics and self-service rules in natural language for no-code, tailored DQ dimensions. Collibra Data Quality and Observability (DQ&O) uses Adaptive Rules and machine learning for intelligent monitoring, providing comprehensive visibility into technical metrics like null checks, row counts, and outliers. This approach helps identify root causes of data issues, linking them to data ownership, lineage, and reliability analysis.

Collibra integrates data quality across the data catalog and offers robust anomaly detection and health reporting, providing clear insights into data quality without overwhelming users. Features like DQ Pushdown and secure handling of exception records enhance efficiency, cost management, security, and reduce time to value. This enables users to create customized DQ dimensions without needing any coding skills. The platform covers various aspects of data quality, such as completeness, accuracy, consistency, validity, uniqueness, and integrity, allowing organizations to effectively assess and improve their data standards.

For instance, data quality dimensions serve as categories for evaluating data, like measuring completeness by counting records with missing values or assessing accuracy based on the percentage of incorrect entries in a dataset. Meanwhile, metrics provide a means to gauge how well these dimensions meet specific quality criteria, helping businesses identify and capture exceptions or anomalies within their data. This comprehensive approach ensures that organizations not only understand their data but also maintain high-quality standards.

Conclusion

As businesses deal with the overwhelming growth of data and the complexities of new regulations, keeping an eye on data quality is more important than ever. By leveraging machine learning to automate and enhance monitoring data, organizations can maintain high standards, stay compliant, and make smarter decisions. Collibra offers innovative solutions for data quality and observability by allowing business users to create data quality rules in natural language, minimizing the need for technical skills and accelerating validation and testing. With a GenAI-based SQL assistant, users can automatically generate checks and transform them into data quality rules without needing to write SQL, promoting a self-service model for data governance.