Incorrect Data Is Of No Use

Why data validation is VITAL?

Data validation is vital to ensure the data is clean, correct and useful. If you are gathering billions of events from millions of people, you will not want to have to clean your data before you can run any analysis. Therefore, running validation on your data as it is ingested means you can be confident with the results.

First-class analytics can only happen with quality data. As the old saying goes, ‘garbage in and garbage out’, and it still holds true – incorrect data is of very little use. Data quality is therefore vital to ensure accuracy and reliability. Some analytics systems allow you to query your data without validating it, however you only analyze validated data.

We validate all the parameters to ensure they are the correct type and the values they have sent are within the ranges you are looking for. This is crucial because the validated data is then something that you can trust and use to make informed decisions and decisive actions.

Why data validation is VITAL?

To find out whether or not your analytics system validates data, if it asks you to define an event schema before sending in any data, then there is a good chance that they have data validation. On the other hand, if it doesn’t ask you, any errors you get in your data are going to flow right through into the data warehouse.