Data quality KPIs are essential tools for businesses to unlock the full potential of their data. KPIs can inform you about the health and trustworthiness of your data. KPIs can also reveal areas for improvement in accuracy, completeness, and consistency. This empowers you to prioritize data cleansing efforts for maximum impact. Furthermore, KPIs can play a crucial role in data contract compliance. They ensure both data producers and consumers are held accountable for delivering and receiving high-quality information, fostering trust within the data ecosystem. Let’s dive deeper into these three key benefits of data quality KPIs.
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You can track data quality KPIs for free
Before you keep reading, DQOps Data Quality Operations Center is a data observability platform that measures data quality KPIs. Please refer to the DQOps documentation to learn how to get started.
Measure and monitor data's health
Data quality KPIs are quantifiable metrics that measure the health and trustworthiness of your data. They give a score, usually a percentage between 0% and 100%, that shows how well your data meets specific quality standards. Think of them as a complete checkup for your data, showing how good it is for use in different applications.
DQOps, a data quality platform, calculates data quality KPIs as a percentage of passed data quality checks out of all executed checks, as shown in the formula below.
These checks can assess various data quality dimensions, such as completeness (absence of null values), validity (adherence to defined formats), and consistency (absence of duplicates or conflicting values).
By analyzing these different dimensions, data quality KPIs provide a granular understanding of your data’s health, allowing you to pinpoint areas for improvement.
The DQOps platform has many dashboards that show data quality key performance indicators (KPIs). For instance, the “KPIs scorecard” provides a high-level summary of KPIs.
Other dashboards, such as the “KPI per table – summary,” offer drill-down options for identifying specific data quality issues and simplifying root-cause analysis.
Please find more information about different types of dashboards in our documentation.
Drive data cleansing efforts
Data cleansing is an ongoing process, not a one-time fix. Data quality KPIs act as a roadmap, guiding efforts towards achieving optimal data health. By measuring KPIs before, during, and after cleansing initiatives, you can track progress and identify areas that require further attention. This iterative approach ensures continuous improvement of quality and reliability.
Check our documentation for a more detailed description of how to improve data quality with KPIs using the DQOps platform.
Ensure data contract compliance and trust in the data ecosystem
Data contracts are a modern way to manage data exchanges in today’s data-driven world. These agreements, typically established between data producers and consumers, document the format and quality expectations for the shared data. The responsibility for defining the data contract falls on the data producer’s side. However, verification of compliance becomes a crucial step for the data consumer.
The data publisher, which can include marketing agencies, suppliers, subcontractors, distributors, and other entities providing data files, can use KPIs to verify that the data quality meets the requirements defined in the data contract.
The data consumer can integrate data quality KPIs verification to ensure that no fatal severity issues are detected and the total data quality KPI meets the target. This fosters trust and transparency within the data ecosystem, ensuring everyone is working with reliable information.
The DQOps platform provides data quality KPI visualization on dashboards, along with two other useful features. All data quality checks are defined in human-readable .dqotable.yaml files, which you can share between data publishers and consumers, simplifying the definition of data contracts.
Additionally, data consumers can integrate DQOps as part of the data pipeline to prevent the spread of bad data across downstream systems. For more information, you can read our blog on “How to integrate data quality into data pipelines.”
Data quality best practices - a step-by-step guide to improve data quality
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How to start
The Data Observability market is competitive, with many vendors offering closed-source SaaS platforms. You can start a trial period on these platforms and expose access to your data sources from the cloud to run data monitoring on your systems.
Another option is to try DQOps, our data quality platform, which provides a faster solution without exposing your data to a SaaS vendor. You can set up DQOps locally or in your on-premise environment to understand how data observability can help prevent data validity issues.
Follow the DQOps documentation, go through the DQOps getting started guide to learn how to set up DQOps locally, and try it.
