Data Reliability vs Data Quality vs Data Anomaly … A complete showdown

It’s critical to keep data reliable and accurate as companies rely on more and more data to power increasingly complex pipelines. Data pipelines can fail for various reasons, including schema changes, null values, and duplication.

However, if data becomes corrupted, you must be informed immediately. Even a stale table or erroneous metric left unchecked can have negative downstream consequences such as data downtime, time-consuming data fire drills, revenue loss, and trust erosion. Because data is so volatile and complex throughout the organization, static monitoring approaches based on dashboards and manual thresholds aren’t sensitive, robust, or agile enough to effectively monitor the health of your systems’ data. Read More..

With today’s data volume, velocity, and variance, AI/ML-based monitoring with full data coverage and early detection of data quality and discoverability issues is critical for avoiding data downtime.

Read More: https://www.qualdo.ai/blog/data-reliability-vs-data-quality-vs-data-anomaly-a-complete-showdown/