Data quality

Start trusting your data and insights. Automatically detect data corruption and prevent it from spreading with production data quality monitoring.

Our clients

Finance & Insurance
We have helped Fortune-1000 companies improve their data quality in the most demanding data platforms. This includes platforms holding 5+ petabytes of data, processing hundreds of thousands of events per second, across thousands of datasets and data processing jobs. This provided us with the expertise to develop a complete set of data quality management tools as part of the development of our starter kit. The starter kit is based on an open-source cloud-native technology stack and is infrastructure agnostic - with the ability to deploy in AWS, Google Cloud, or Microsoft Azure. It integrates best with Hadoop and Spark-based data lakes with Apache Airflow orchestration, but also supports integration with SQL-based data sources out of the box and integrates with any other analytical data platforms, data warehouses, databases, and ETLs.
Validate simple or complex business rules

There’s a variety of data quality checks that can be implemented as business rules. With our solution, data analysts and engineers can create rules to ensure that certain data columns don’t exceed pre-defined ratios of nulls, validate that data falls into certain ranges, or check that a data set complies with a certain profile. The tool assists with data profiling, measuring data quality metrics, cleansing and auto-correcting data, and alerting the support team when something goes wrong.

Uncover hidden anomalies with AI

If your data analytics platform already has thousands of data processing jobs or the business rules being used aren’t detecting complex data defects, anomaly detection can help build a more comprehensive data quality solution. Data scientists can configure automatic data profiling to collect key data metrics, use statistical process control techniques, and configure deep learning anomaly detection to uncover suspicious patterns and alert the support team if predefined levels of confidence are reached.

Ensure completeness and consistency

Good data quality starts with ensuring that the raw data imported into the data analytics platform is done correctly and completely, is consistent, and not stale. With our solution, we can configure various types of checks that integrate with data sources in data lakes or SQL-based databases. Measuring and improving data completeness is critical for streaming use cases such as clickstream processing, order processing, payment processing, or Internet of Things applications, when events can be dropped or processed more than once.

Data quality industries

We develop data quality management solutions for technology startups and Fortune-1000 enterprises across industries including media, retail, brands, gaming, manufacturing, and financial services.

Read more about data quality

Why you need data quality automation to make data-driven decisions with confidence
In this article, we give an introduction to the data quality problem and discuss approaches to automate it and increase confidence in data.
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Unsupervised real-time anomaly detection
Anomaly detection is a powerful technique to automate data quality and scale it to thousands of data pipelines with minimal effort. In this article, we give an introduction to anomaly detection and show how different algorithms, including deep learning AI, achieve the best balance of precision and recall.
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5 technology enablers for DataOps
Data quality is one of the techniques to consistently unlock the business value of data. In this article, we discuss DataOps and data governance, what role data quality plays in DataOps and data governance, as well as the tools and techniques to implement the right processes and foster the right culture.
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