Data Quality

What defines high-quality data?

High-quality data is characterized by several key attributes that collectively ensure its reliability, accuracy, and usefulness in decision-making processes within an organization. For it, there are data 6 core dimensions that can be used to measure and predict the accuracy of your Data Quality. Let’s dig into each of them in more detail:

Key Attributes of High-Quality Data

Data Accuracy: Accurate data is free from errors, inconsistencies, or discrepancies. It reflects the true state of the entities or events it represents. Trackingplan provides a fully automated QA solution that empowers companies with accurate and reliable digital analytics. Our end-to-end coverage of what is happening in your digital analytics at every stage of the process is designed to help you prevent your test executions do not break your analytics before going into production and offers you a quick view of the regressions found between them and their baseline so that you can understand the root cause of those errors in order to fix them before compromising your data.

Completeness: Complete data contains all necessary information without missing values or gaps, offering a comprehensive view of the subject matter. Trackingplan ensures your data always arrive according to your specifications and automatically warns you when it detects missing events or properties or any data format problem.

Consistency: Consistent data maintains uniformity across different sources or within the same dataset, ensuring coherence and compatibility. Trackingplan automatically monitors all the traffic that flows between your sites, apps, and CDP platforms. That makes us the only solution that offers a single and always updated single source of truth to show you the real picture of your digital analytics status at any given moment. All teams involved in first-party data collection can collaborate, detect inconsistencies between your events, and properties, and easily debug any related issues.

Timeliness: Timely data is relevant and up-to-date, reflecting the most current information available. Trackingplan offers you an always updated picture of the current state of your digital analytics in real-time that connects and ensures all teams involved in the data collection process are on the same page.

Relevance: Relevant data aligns with the intended purpose and needs of the user, providing valuable insights without unnecessary details.

Validity: Valid data conforms to predefined rules and standards, meeting specified criteria and ensuring it is fit for its intended use. Trackingplan allows you to set up Regular Expressions (RegEx) to validate that all the values for your properties conform with the pattern you specify or, in case it’s not, automatically send you a warning. Moreover, you can also set up any kind of complex validation setting, like validating whether all products logged in a cart carry a valid product_sku given the page section, with custom validation functions.

Accessibility: Accessible data is easily retrievable and available to authorized users when needed, ensuring its usability and value.

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