Best Practices for Data Testing & Validation

The evolution of marketing hinges on two things: data and technology advancements. While history has shown us that technology will continue to advance at an ever-increasing rate, one thing will remain constant – no matter the technology, it will provide data. As marketers continue to seek out the best ways to market to their audiences, the crux of the equation centers around data. 

In most cases, data analytics platforms, like Adobe Analytics or Google Analytics, are the primary method for tracking and analyzing data. However, there is one tremendous caveat to using data to make decisions: the data must be accurate. In marketing, bad data is ineffective at its best and costly at its worst. This is why data testing and validation is so important during a site launch or an analytics implementation. 

Data testing and validation is evaluating your data to ensure that it is accurate. Generally, this practice looks at two primary functions: 

1. It evaluates the systems to ensure the data is being collected from the right sources and transmitted to the right locations in the analytics platform 
2. It evaluates the reports to make sure that the information being generated is correct. 

Data testing and validation can be a significant undertaking – especially on large sites with many pages. And it also isn’t a one-and-done effort; it is ongoing. Given the size and nature of data testing and validation, implementation experts, like Hoodoo (now Rightpoint), typically follow best practices to ensure the data entering your systems are accurate. Here are a few that you should consider. 

Test at every rollout: This means testing in staging, production, and during code rollouts. While testing in staging can catch many errors, it’s still testing with fabricated data. Testing the site under real-world conditions when it is collecting real user data can uncover other underlying issues that may not have been apparent in the isolated staging environment. Additionally, every time new code is rolled out, testing and validation should be run again to ensure that nothing was impacted by the latest code rollout. 

User Acceptance Testing: At Hoodoo (now Rightpoint), we ensure that at least one other person evaluates the data. While we rely on internal quality assurance (QA) engineers to a degree, we also recruit someone from the client’s organization to perform some testing and validation. The client-side reviewer will have more insights and a better eye for things that may be amiss or that may seem out of place because they are far more familiar with the company and its products. 

Consider using tools: While all major browsers will let you inspect which data is being shared, there are other tools like browser extensions, desktop clients, etc., that can help you take a deeper look at the data that is being collected. We have worked closely with companies like ObservePoint, a data governance suite that automates auditing your data collection methods and validating your data. These types of tools can give you detailed insights to your data collection accuracy. Some of these tools can also help you test your site on a large scale, catching issues that would otherwise go unnoticed. 

Keep an eye on the actual reports: This may seem obvious, but an important way to validate your data is to actually look at the reports both before and after launch. The great thing about a lot of analytics platforms is that they are designed to help you explore the data and will often call your attention to anomalies. These anomalies are telling for your business either way – the data is wrong, or the data is right and you have new and valuable information. 

Don’t mix your data: Make sure your test data and production data are going to two different locations. Mixing these two sets of data can make it complicated to identify what source your data is coming from. Naturally, the test data could have significantly different values than the actual data. These can come up as anomalies that make it difficult to identify whether the data is pointing to a validation issue or the data is simply from testing. 

As you drive toward informing your business and marketing decisions with robust data, ensuring the accuracy of the data should be one of your primary areas of focus. Your analytics system should be built specifically for the needs and future goals of your business. Working with systems like Adobe Analytics can help you set up your business to manifest your long-term vision toward digital personalization. 

If you have questions about using analytics, validating your data, or how to incorporate analytics into personalization, reach out to us. We have a team of experts who focus solely on helping our customers implement and utilize their site analytics – whether that’s through Adobe Analytics or Google Analytics.