Data Layer & Tagging Specifications
They’re each a big lift. A lot of the work in digital analytics implementation involves building the data layer and tagging specifications manually. Normally, organizations create a custom solution design and then hand that off to developers or outside consultants who then turn the solution designs into a data layer and tagging specifications.
This is often the most time-consuming portion of an implementation, taking weeks or months to complete, depending upon the size and complexity of the website/app and implementation. And since implementations are ongoing, this work continues perpetually, whenever new business questions arise.
Apollo’s Different Approach to Automated Data Layer
& Tagging Specifications
Apollo uses a relational database to produce the exact data layer elements and tagging specifications needed based upon the selected business requirements. Business requirements have a many-to-many relationship with solution designs, which have a many-to-many relationship with data layer elements and tagging specifications. Let’s illustrate this with an example from Apollo. Let’s say that you have a need to see the country associated with website/app activity. In Apollo, you would select the appropriate business requirement:
Selecting the business requirement would trigger the associated solution design, which in this case is to set a data layer event called Page Load Started and populate the Site Country to an eVar (this example uses an Adobe Analytics implementation). At the same time, Apollo would programmatically produce a data layer element (siteCountry) and the tagging specifications required to populate the data element as shown.
As you add more business requirements to your implementation, Apollo adds more solution design elements which, in turn, add more data layer elements and tagging specifications. For example, if you add a business requirement around Site Type, Apollo will dynamically update the data layer and tagging specifications accordingly.
By leveraging a common platform that houses all information related to the implementation in a relational manner, all aspects of the implementation—from business requirements to solution designs to data layer elements to tagging specifications—are interrelated and updated dynamically.