We’ve talked before about getting your marketing analytics squared away, but one of the first steps is understanding the health of your data so that before you start analyzing performance you can know if the numbers are right. So where do you start? We’ve outlined a few key areas that will help you understand how your data is doing.


First of all you want to see if your site or sites are tagged properly. Are you using the Google Analytics tag, Google’s Global Site Tag, or Google Tag Manager? Are there multiple tags on the site and are you using all of them? Once you identify the tags you have, you can see if they are set up properly: are they in the correct spot on your website or are there any elements missing? Has the tag been placed on all pages of the website?  Google Tag Assistant is a great way to take a look at how your code is set up, and the tool will flag any issues with your site tags as well, such as duplicate code or improper placement. Moreover, it might be a good opportunity to move to a tag management system (if you haven’t already) that can help you to streamline your tags without constantly adding them to your website’s code (you can find some other benefits of using Google’s Tag Manager here).

Data Structure

Another important element to consider is how your data is structured within the platforms you use. For example, which views do you have in your Google Analytics property? Generally, you should have one view that contains all your “raw” data. And since any changes made to that data, such as filters, will alter it, you should also include a separate view for those changes. For example, your view might exclude internal traffic or traffic from specific IP addresses. Or you only want to see data relating to a specific category or set of pages. When creating or auditing these views, you’ll want to check settings and filters. It’s also a good opportunity to check on things like time zone, currency, and site search settings.

How you define your goals or important interactions also hinges on how you structure your data. If you are tracking conversions within Google Analytics you want to check that they are set up and defined correctly. Is the goal destination the correct goal completion page? If tracking events, ensure that they are set up correctly in Google Tag Manager or that the code is correct if implemented directly on the site. There are a few different ways to check whether a goal or event (as defined in Google Analytics) is working, but you can always complete the action on your site and check the “Real Time” tab of your Google Analytics view. And if you are tracking e-commerce data, make sure the transaction elements such as product and revenue information are pulling through correctly (you may need to go back to the code).

Data Integration

As you can see in the examples above, the health of your data often depends on the code, its placement, and how your data is structured within Google Analytics. However, you want to be sure that you get the most out of the data that comes to your site from sources like social media and paid search. If you’re not already familiar with UTM tagging, this is when you add parameters to a URL that allow you to capture data such as the traffic source and the campaign name within Google Analytics. UTM tagging is essential to understanding where your traffic is coming from and how these sources and campaigns perform. Some platforms, such as Google Ads, automatically add these URL parameters. You should, in addition, check that this auto-tagging is implemented correctly. And you’ll want to link your Google Ads account to your Analytics property for optimal tracking.

Google Analytics can also filter out (known) bot traffic that could potentially impact your data. Take a look at this option within the Google Analytics view settings in order to filter out any hits from bots or spiders.

Audit and Review

Your data can always use a check-up whether you’ve been running Google Analytics for years or just getting it set up. Developing a checklist for your organization and starting with these key areas can help you see whether your data is healthy or maybe needs a little help.