Automate your Digital Analytics QA process

Data Quality
Mariona Martí
Automate your Digital Analytics QA process

Applying QA (Quality Assurance) throughout the development process and software lifecycle has become the industry's standard in the last two decades. Unfortunately, this is something that has been lacking for way too many years in the data space, but Trackingplan's Digital Analytics QA solution is here to change that.

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Analytics Testing Landscape

If you have ever been involved in the process of collecting accurate and trustworthy data, you will sadly understand us when we say this is an exasperating process especially prone to bugs and breakages.

But if you have never gone through this tedious process, let us introduce you to Mark first. Mark works as a digital analyst, and these are all his duties:

1. Data accuracy: One of the biggest challenges for Mark is ensuring the accuracy of the data his team collects so that business executives, data analysts, and other end users work with good and accurate information.

This is key to avoiding faulty results in analytics applications and, of course, bad business decisions. But unfortunately for Mark, this is easier said than done. Let’s continue reading to discover why.

2. Integration: Mark and his team also spend a lot of time in the complex task of integrating data from web analytics tools, social media, CDP platforms, and CRM systems.

However, integrations fail easily and invisibly, making the task of ensuring that all this data is integrated properly even more difficult.

3. Data privacy: With the increasing focus on consumer privacy laws, Mark also needs to keep a close eye on ensuring all the data collected is compliant and protects users’ privacy if he doesn’t want to make his company face serious legal risks.

A big responsibility considering his limited resources in terms of time, budget, and personnel.

5. Data visualization: Once data is collected and integrated, digital analysts like Mark need to be able to effectively analyze and visualize the data to provide insights to stakeholders.

This forces Mark in having to maintain various spreadsheets and data repositories that constantly get outdated. A tedious work that has eventually become a double-edged sword as it prevents all members across the company to be on the same page unless they are on the same team.

Thankfully, the growing interest in analytics testing has led to the emergence of new digital analytics QA solutions to give companies end-to-end coverage of what is happening in their analytics at every stage of the process.

Here at Trackingplan, we soon realized we needed to introduce additional quality control steps in our client’s Digital Analytics to prevent both the impact and the cost of fixing data bugs. In that sense, investing in automating the analytics QA process has dramatically enhanced the quality of our client’s data and the decisions based on them.

Trackingplan’s Analytics QA Solution: How Does it Work?

Our mission is simple: allow companies to capture and fix data bugs before they reach production by automating the analytics QA process into an earlier phase of the data lifecycle.

Automating this Digital Analytics QA process allows us to:

  1. Discover your data integrations and automatically understand what data you are actually collecting, as well as the schemas beneath this process.
  2. Present all your data integrations and schemas (pixels, events, and properties) in a single source of truth. That way, all teams involved in first-party data collection can collaborate, detect errors, and easily debug any issues by quickly understanding where the problem takes place (pages, campaigns, landing, sources, etc.).
  3. Automatically alert you when any anomaly happens in your Digital Analytics so you can quickly coordinate the different teams working on the data collection and collaborate efficiently with updated information on any data problem.

To streamline this collaboration, Trackingplan allows you to set up multiple environments at run-time. This way, you can:

  1. Help developers implement events in your applications according to your specifications and immediately alert them about any issue in its implementation to get it fixed before the event goes live.
  2. Test how your data and specifications change when moving from Development, to Preproduction, and Live environments to automatically find differences and problems when firing those events.
  3. Compare environments to detect mistakes before they reach production (e.g. compare staging data to your master specification baseline to capture bugs or regressions before they go live).
  4. Allow the different teams involved to easily navigate through what is being captured from your users' interactions by looking at a specific environment. That way, our fully automated analytics QA solution makes much more simple seeing the data collected in your apps, websites, or landings and detecting data formatting issues, duplicates, or inconsistencies before these break your digital analytics.

Setting up Trackingplan to QA your Digital Analytics

Our Digital Analytics QA solution supports complex deployment setups with custom preproduction environments.

You just have to modify this init according to your specifications and your own TP_ID and add it at the top of the <head> section of your site (instructions here), or include it as a new Google Tag Manager Script (instructions here).

The same applies for iOS and Android, where you will only need to set the environment variable within the init provided in the link above.

To integrate different environments using Segment, just add the query parameter &environment=<environment_name> to your webhook endpoint to have the desired behavior. For the production environment, you should use PRODUCTION.

Integrating your staging and testing environments and comparing them to your baseline allows you to see the differences between one release and the next, detecting broken events or schemas before releasing them. Any existing automated analytics QA you have implemented, such as functional or non-functional regression testing (e.g. with Cypress), will stress your analytics under the watch of our system.

As a result, you can also cover the analytics service integrations in your existing release testing by simply integrating Trackingplan without changing your feature or testing code in any way.

Life After Analytics QA

And if you’re still wondering what happened to poor Mark, let’s compare his overwhelming list of duties after Trackingplan’s easy installation.

  • Data accuracy: Now, Mark can compare the data his team collects against the expected outcomes. This allows him to verify in real time if the data collected is accurate and consistent with the expected results. What's more, he now sleeps peacefully knowing that, if any anomaly or bug happens, he will be automatically notified as soon as it happens.
  • Data completeness: Ensuring that all the relevant data is being collected and that there are no gaps or missing data points is now easier than ever.
  • Trackingplan allows you to validate you are capturing the expected data sources and will warn you if any required data field is not present before it’s too late and you’ve already lost data.
  • Data consistency and relevance: Trackingplan automatically verifies that the collected data is consistent across different platforms and that there are no discrepancies or inconsistencies in the data while ensuring that the collected data is relevant to the business goals and objectives.
  • Data format: Trackingplan not only automatically detects your data schemas, but also allows you to enforce whether a variable is required and of what type it should be. We support RegEx and any kind of complex Validation Function that can help you automatically know if the data you collect conforms to the values you have specified.
  • These conditions allow Trackingplan users to go deeper when validating their data by easily defining what data is expected to be collected after a particular customer action.

All in all, our Digital Analytics QA solution helps you avoid transaction processing problems in operational systems, faulty results in analytics applications, and serious legal risks related to data privacy. By implementing Trackingplan, you can get to an implementation culture where every new event gets a proper test to ensure that the analytics data being collected is accurate, complete, and useful for making informed decisions.

Our fully automated Analytics QA solution has been designed to empower companies with accurate and reliable analytics. If you want to avoid compromising your data by catching errors before these break your digital analytics, get started today to experience the benefits of Trackingplan first-hand.

For more information, you can always contact our team.

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