A data layer is a javascript object on the page of a site that contains important data points typically served up by the server. It is widely accepted that data layers can help ensure the quality of your data and protect your data’s integrity. Should your team be leveraging a data layer? Is it really that important to the success of your analytics endeavors? Let’s find out.


3 Compelling Arguments for Why You Need a Data Layer

    1. Data layers make information available to us that we don’t normally have access to in any other way. The high level outcome that analytics engineers are aiming for is to provide data to analysts that accurately replicates a visitor’s behavior, journey, and experience on a website or app. Sometimes the data we need to accomplish this is unavailable to us when we need it to be. We don’t know certain things about a visitor just by looking at how they are behaving on the website. For example, if they are a registered user and not signed in or if they have purchased similar types of products from you before. These data points can, however, be served up at any time by pulling them from the server and making them visible to your engineers. Suddenly your engineers have become omniscient, and omniscience means insights.
    2. Data layers enable us to have more consistency. I can’t tell you how many times I have had to write code that scrapes the page for data about a product a user is looking at, and then scrapes the page for the same data point for the same product that a user has purchased, only to discover that the format for the data is slightly dissimilar. In cases like this, your reports are showing two different products that are actually the same, and this hinders analysts from reporting accurately. The data layer, however, is going to serve up the same data for that product no matter what, not to mention make it 10 times easier to consistently capture. It feels more like shooting fish in a barrel than catching them by hand in a river.
    3. Data layers allow you to use the same data points for multiple different reasons. For example, we may be sending data over to Adobe Analytics, and using that same data point to fire off any number of third-party tags. Having your data in one place allows you to shepherd it to multiple different vendors so that it is consistent across the board. This means your great grandpa will no longer be rolling in his grave wishing you would remember to work smarter not harder.


Why Is Data Quality Important?

Being able to trust your data is of the utmost importance. Without data that analysts, stakeholders, CEOs, and decision-makers believe is accurate, your marketing teams are stuck in gridlock traffic. The smallest feeling of distrust will sow fear and apprehension, and overcoming these challenges is not easy. On the other hand, if you are collecting high-quality data, it will lead to advanced insights and powerful transformations for your business. In other words, winning! And I’ve never met a marketing team that doesn’t like to win.


Now that you know why you need a data layer and why quality is important, it’s time to put the two pieces together. Let’s check out how to build a data layer that ensures data quality in just 3 steps.


3 Steps to Build a Data Layer that Ensures Data Quality

    1. Strategize, brainstorm, and collaborate to get an understanding of what data you need populating your data layer. This is the planning stage of designing your data layer, which determines what data you want served up. These plans aren’t arbitrary, but align with what types of business goals are trying to be met. These goals or different focuses are unique and custom to each business. This is a great first step to help get you to understand what you need and want out of your data.
    2. Develop healthy communication between the analytics engineer teams and the developers. The developers are the ones who are actually going to create the data layer. Analytics teams don’t typically handle the logic that is sourcing the data layer. Instead, they work with the client’s development teams who are the ones building the website or app. Therefore, having a healthy relationship with whoever is building your data layer is important. Communication is the heart of a good relationship and your data layer will thrive with that kind of parenting.
    3. QA the data layer. It’s important to make sure that your data layer is populating with the data you want on the pages that you need. Test the data layer in multiple scenarios and develop analytics around its health. Catching errors in a data layer early is really important and will prevent lots of heartache down the road.



Data layers protect the integrity of your data and enable you to move away from having to scrape the page for data points. They help to eradicate human error and are extremely useful because they enable analytics engineers to architect an implementation that provides healthy data collection that is trustworthy for your business.


We at Cognetik are extremely familiar with architecting data layers and strategizing what kind of data is important to have in your data layer. Contact us today to ensure that the quality of your data is top-notch.

About the author

Wesley Huth

Wesley Huth
Wesley is an analytics engineer who specializes in data collection architecture and implementation for Fortune 1000 companies. He is passionate about inspiring companies to develop data-driven cultures that enable them to trust their data collection systems and equip them to effectively compete in a data-centric marketplace.

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