Even outside of the data source itself

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Keeping the integrity of that data set Our reports ride on a lot of trust that the data we’re using is… actually right. Consider: Who has access to your Google Analytics data? Your Search Console account? Who can add filters, delete views, change custom groupings, delete properties? It’s really important to lock down who has the ability to make changes to your data that could drastically affect its reliability. It’s even more important to build processes that will reduce the risk of it happening.


Even the most seasoned Google Analytics user will not necessarily be aware of how adding a india business email list traffic filter to the account may affect the integrity of data that someone else in the team is reporting on. Screenshot of Google Analytics filter examples. You may well be trying to clean up your data source by better attributing channel data, or filtering out bot traffic. These are all good ideas. However, unless it's carried out in conjunction with other people who are using that data, properly noted and even annotated within your data source, it can cause a loss of integrity.


, there can be factors that affect it. For instance, Google Analytics channel data relies on people using UTM codes correctly. Make sure you invest time in creating training programs that inform stakeholders of how their actions can impact data. Create processes that limit the effect of those changes. Consider keeping a centralized document of UTM codes used on marketing campaigns and develop a system for creating them consistently.