Enterprise Data Quality for All Data Types

Anomalo uses a mix of data quality monitoring techniques to ensure coverage across your entire data warehouse. First, you can set up low-cost, metadata-based data observability monitoring for all your tables to ensure on-time delivery and completeness. Then, for the tables where you want to additionally monitor the data values themselves, you can set up Anomalo’s automated data quality checks, including AI-based anomaly detection. This uses unsupervised machine learning to learn the historical patterns in your data and look for unexpected changes or poor data quality. Finally, for tables where you want even more custom monitoring, you can set up user-defined validation rules and track key metrics.

Data quality monitoring is essential to ensure that bad data doesn’t lead to poor business outcomes. Low-quality data can cause all kinds of problems, from broken products and user experiences, to inaccurate dashboards and reports, to machine learning and generative AI models that behave erratically. Data quality monitoring is important for compliance and data governance as well. If your business users don’t believe that they can rely on your data, they are less likely to make data-driven decisions. Without data management tools like Anomalo, you may end up investing a lot of time and resources to modernize your data stack, only to find that your efforts to get value from your data are blocked by inconsistency, inaccuracy, and overall lack of trust.

To offer data quality management for any data warehouse, even with tens of thousands of tables, Anomalo provides a suite of data quality checks. First, we offer data observability checks, which are a low-cost way to monitor your entire warehouse using table metadata. From there, you can configure tables to automatically measure data quality in one click. Automated data quality checks go deeper than observability to sample and inspect the data values themselves. You can also track KPIs about your data to identify trends and changes in segments. Finally, for that subset of key business tables where you need to write strict validation rules that your data must conform to, you can define those in Anomalo’s UI.

For most businesses, it’s impossible to write rules about every column and every table. That’s why Anomalo is considered one of the best data quality tools on the market, with our algorithms that use AI/machine learning to understand patterns, set thresholds, and know when to alert. These algorithms learn and adapt as your data assets grow and change over time.

We do. Tables that are configured for data monitoring in Anomalo can display visual data profiling information, such as the distribution of data values in each column. Furthermore, each data quality check offers rich visualizations to understand why and how your data is passing or failing. For additional analysis, you can monitor key data quality metrics, which will generate more charts for exploration and notify you when there are significant changes.

Yes! For each table that you monitor in Anomalo, you can see data lineage information that is pulled directly from your data warehouse/lakehouse, including a mapping of how data flows upstream and downstream. This allows you to quickly diagnose data issues and understand the potential impact of any failures, resulting in high-quality data for your company.