Why sampling falls short in ensuring reliable data systems

Introduction

Imagine assembling a piece of furniture with only part of the instructions. You might get some parts right, but the final product could be unstable or unsafe. Now, consider the importance of data for business operations. Ensuring data quality by only checking a small portion is equally risky. Reports indicate that poor data quality costs companies an average of $12.9 million annually. It’s not just about identifying immediate issues but also about planning for future growth and ensuring solutions can scale. This article explores why the scale of your data should be a critical factor in your data quality strategy and why sampling is insufficient. We’ll also discuss the necessity of full-fidelity data quality monitoring.

Understanding How Volume Influences Data Quality

Handling large data volumes presents unique challenges, particularly in maintaining high data quality. Historically, sampling has been a common approach to managing these challenges. Sampling involves analyzing a small subset of data to infer the quality of the entire dataset. Various sampling strategies, such as systematic, stratified, and cluster sampling, have varying levels of success across different domains, including infrastructure monitoring.

While sampling is quicker and less resource-intensive, it has significant limitations. Sampling can miss anomalies and outliers, leading to a false sense of security. Critical issues can go undetected when only a fraction of the data is checked, potentially resulting in substantial financial losses and reputational damage. As data volume grows, so does the potential for hidden errors, inconsistencies, and anomalies that could impact business operations and decision-making.

How Full-Fidelity Data Quality Works

A more comprehensive approach is needed to overcome sampling’s limitations. Full-fidelity monitoring provides a complete view of data health, capturing all anomalies and offering a clear picture of data integrity. Unlike sampling, full-fidelity monitoring leaves no blind spots, checking every piece of data to find subtle or rare anomalies that sampling might miss.

Full-fidelity monitoring requires sophisticated algorithms and architecture to manage vast amounts of data efficiently and avoid high costs. Machine learning algorithms, like time series analysis, play a vital role. These algorithms learn patterns from the data collected over time without predefined rules, helping identify trends, seasonal patterns, and anomalies.

Traditional approaches involved running several SQL queries to extract relevant metrics and then analyzing them. This method only works for smaller datasets with a limited number of monitored attributes/metrics, leading to a reliance on sampling for larger data volumes. Full-fidelity relies on advanced algorithms and architecture to analyze vast amounts of data efficiently. These algorithms are highly tuned to analyze thousands of attributes across billions of records.

Moreover, full-fidelity monitoring ensures that signals extracted from the data are stable and consistent with historical trends. This stability is crucial for data observability, helping detect unknown issues that haven’t been seen before. By continuously scanning all data, full-fidelity monitoring can identify and alert on any deviations from the norm, ensuring prompt remediation of potential issues.

Another crucial aspect is remediation. Sampling often requires manual intervention to dig deeper, perform a full assessment, and apply fixes. This process can be time-consuming and inefficient. In contrast, a full-fidelity approach handles remediation natively. Since all data is processed, the findings are comprehensive, allowing for automatic remediation. Techniques like data binning and automatic data correction can be applied immediately. Sampling limits these options to simpler solutions, like circuit breaker patterns, which only stop the process when an issue is detected but do not fix the underlying problem.

Conclusion

Ensuring data quality by only checking a small portion is risky and can lead to significant financial and reputational damage. Sampling often misses anomalies and outliers, especially as data volumes grow. Full-fidelity data quality monitoring captures all anomalies and provides a complete picture of data integrity.

Using sophisticated algorithms and techniques like time series analysis, it ensures stable and consistent data signals. This approach also streamlines remediation by allowing automatic fixes, avoiding the inefficiencies of manual intervention.

This featured article was originally published on Telmai’s official blog.