Bismart Data Quality Framework is a solution designed to support the quality of an organization's data. The tool validates, documents and performs profilings on the data, ensuring an optimal level of quality.
Is some of your data duplicated, does it not meet the required validation standards, or does it contain inaccurate information?
Would you like to validate the consistency, integrity and reliability of your data in a user-friendly environment?
Would you like to detect abnormal behaviors in your loading processes?
Automate quality validation in your loading processes and act proactively through an alert system that warns you when an anomaly is detected!
Bismart Data Quality Framework centralizes all data quality processes in a single user-friendly environment accessible at all levels within an organization. In addition, it is fully compatible with data integration frameworks. Such centralization democratizes data quality processes and allows business users —not just IT managers— to validate data quality and define their own quality standards.
The solution —based on Great_Expectations— allows users to create their own quality standards to meet specific needs.
The tool has an alert system designed so that data quality managers can act proactively. The system warns you every time an anomaly is detected. In addition, the tool can be integrated with collaborative platforms so that you receive the alerts where you need them the most: Microsoft Teams, Slack, email, etc.
Bismart Data Quality Framework goes beyond ensuring that your data is of optimal quality. It also allows you to execute corrective actions.
The following figure illustrates the integration of Bismart Data Quality Framework as a validation flow within a pipeline.
Centralizes data quality processes in a single user-friendly environment.
Allows you to set your own expectations and quality standards.
Technical and functional validations.
Unlike traditional unit tests, validations do not work with code. Instead, they use easy to understand expressions.
Automatically detects mistakes in the data and allows you to execute corrective actions.
Monitoring and historification of results in different environments: Portal, Power BI, Azure Monitor, Log Analytics, Azure Data Lake, etc.
Includes an alert system that allows you to execute actions depending on the results of the validations.
Alerts can be received in monitoring tools, such as log Analytics, and collaborative tools, such as Microsoft Teams, Slack, email, etc.
Can be integrated into data integration environments (ETL/ELT processes). Data quality auditing processes are part of the integration process.
It is an open system, easily extensible and customizable.
You can check all the technical features of Bismart Data Quality Framework in the solution's datasheet.
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