Mastering Data Validation: The Key to Clean and Reliable Data | by Lior Barak | October 2023

In my post’Demystifying Data Flow: Explaining the Four Zone ConceptI was addressing the need to have data validation from the starting point where the data is created and across the entire pipeline. If you haven’t read the article, I encourage you to do so as it’s a good starting point.

Data validation is the process of checking the accuracy, completeness and consistency of data. It can be done manually or automatically and can be applied to all types of data, including structured, semi-structured and unstructured data.

There are a number of different data validation techniques that can be used, including:

  • Verification format: Checking columns to make sure the data is in the correct format, such as a valid email address or sales with a decimal number.
  • Metadata Validation: When checking that the correct metadata is sent with the event, for example each event watched and fired must have a session id, IP and user_id if possible.
  • Scope Verification: Checking if the dates fall within a certain range, such as a date between 1/1/1970 and today’s date.
  • Value Verification: Checking that the data has a valid value, such as a product code, that exists in the database.
  • Consistency check: Checking that data is consistent across different systems or applications.

As a data producer, it is critical to ensure data validation. Clean and accurate data improves decision-making and customer service and minimizes errors. For example, in marketing, accurate data ensures that messages reach the right audience. In financial decisions, accurate data is vital to making the right decisions with your company’s funds.

Data validation and data governance

Data governance is the process of managing data throughout its lifecycle. It includes defining policies and procedures for the collection, storage, use and destruction of data. Data governance is important to startups and e-commerce businesses for a number of reasons related to data validation, including:

  • To improve data quality. Data governance can help businesses improve data quality by ensuring it is accurate, complete and consistent. This can lead to better decision making and better business performance.
  • To reduce costs. Data management can help businesses reduce costs by eliminating redundant data and streamlining data management processes.

For other reasons not related to data validation:

  • To ensure compliance. Many industries have regulations governing the collection, storage and use of data. For example, the General Data Protection Regulation (GDPR) in the European Union imposes strict requirements on how businesses can collect and use personal data. Data governance can help businesses ensure they comply with all applicable regulations.
  • To protect data from unauthorized access and use. Data breaches are becoming more common and startups and e-commerce businesses are often targeted by hackers. Data governance can help businesses protect their data from unauthorized access by implementing security measures such as encryption and access control.

Data validation is an important part of data management. It helps to ensure that the data managed is accurate, complete and consistent.

For example, data governance policies may require that all new data be validated before being added to the production database. This helps ensure that the data in the production database is reliable and trustworthy.

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