Data quality monitoring for data sharing
Ensure that the data sharing KPIs are met
Data sharing unlocks incredible potential for businesses, but success hinges on clear communication. Traditional agreements often lack details about data quality, leading to a frustrating experience for both providers and consumers. This wastes resources on both sides.
DQOps transforms data sharing by facilitating shared data quality metrics. Define data quality checks on your end to ensure that you are delivering what you promised as a data provider. Similarly, when you are the data consumer, define data quality checks to ensure that the data you receive is valid and on time. Ensure that the Data Sharing KPIs between companies are met.
Shared data quality checks
Data sharing partnerships rely on trust and reliable data. DQOps empowers data providers to ensure data quality meets contract requirements, fostering strong data collaboration.
- Define one set of quality checks for data contracts (SLAs).
- The data quality checks in DQOps are defined as simple, easy-to-read YAML files and can be shared with the consumer.
- Mark SLA-related checks within DQOps for easy tracking.
Data sharing partnerships rely on trust and reliable data. DQOps empowers data providers to ensure data quality meets contract requirements, fostering strong data collaboration.
- Define one set of quality checks for data contracts (SLAs).
- The data quality checks in DQOps are defined as simple, easy-to-read YAML files and can be shared with the consumer.
- Mark SLA-related checks within DQOps for easy tracking.
External data verification
External data verification
Data consumers can leverage DQOps to verify the quality of externally received data, ensuring it meets agreed-upon standards before analysis.
- Define and monitor data quality checks for received data with multiple built-in validity and completeness data quality checks.
- Detect missing days or partitions in external data deliveries.
- Provide feedback to data providers on specific quality issues.
Data received on time
DQOps ensures consistent data delivery by automatically monitoring data timeliness.
- Track data freshness to guarantee minimal lag between recorded activity and present time for up-to-date analysis.
- Create custom checks to compare data arrival against your specific delivery schedule (e.g., weekly, monthly).
- Uncover inconsistencies in data arrival patterns, identifying potential delivery delays before they impact analysis.
DQOps ensures consistent data delivery by automatically monitoring data timeliness.
- Track data freshness to guarantee minimal lag between recorded activity and present time for up-to-date analysis.
- Create custom checks to compare data arrival against your specific delivery schedule (e.g., weekly, monthly).
- Uncover inconsistencies in data arrival patterns, identifying potential delivery delays before they impact analysis.
Ground truth checks
Ground truth checks
Ensure that the data received from an external party is compared with other verified data sources.
- Compare data summaries against external sources (e.g., finance department) to detect discrepancies at relevant business levels (country, state).
- Compare the data with flat files that you can load to a database or directly to DQOps.
- Configure table comparison using a user-friendly interface.