The data layer is among the most strategic parts of your marketing technology strategy. Simply put, it’s the foundation from which you’ll be able to build real-time, forward-looking, action-oriented programs that delivers lasting value and impact to your organization.
Most marketers today are managing a digital presence that is multi-device, multi-channel, dynamic, and, most importantly, data driven. According to the recent Internet Trends 2014 report, only 1 percent of data now being collected is being used in some form. The data layer is instrumental to unifying all this mission-critical data, your applications, and ultimately your teams so that you can take knowledge from one channel to influence action in another.
So, with all this intrinsic value why would you even think about taking short cuts, which could affect the quality and completeness of this critical asset?
The data layer concept
So what is a data layer really anyway? Fundamentally there are two common definitions of a data layer:
- From the standpoint of your digital marketing technology stack, a data layer is simply the behind-the-scenes data that drives interactions in web, mobile, and other digital channels. This could include any data that is known to browser, mobile, web server, or application server technologies.
- The second definition revolves around more specifically structured data that is planned, defined, gathered, standardized, and used to offer common language to your online application infrastructure and user experience layers.
The latter is obviously a more deliberate and strategic way to prepare your data to drive your business goals. But these two concepts are often confused, conflated and sometimes given what I think is dangerous false equivalency. Why dangerous? Because if you think of your data layer as just a technology enabler, you may overlook its strategic importance. One could be tempted to think of the data layer as just a necessary evil that enables better tag management. This is where the danger lies. A properly organized and structured data layer has business value that goes far beyond the tactics of mapping an available data element to a tag. I cringe when I hear tag management vendors imply that they can piece together a data layer by automatic detection methods (scraping), or by allowing your front-end development team to throw together a haphazard data layer through some ad-hoc definitions. It’s the old adage of “just because you can do something, doesn’t mean you should.”
Don’t underestimate the value of your data
Why should you be more deliberate with your data layer planning? Repeat after me: omni-channel experience. We’re all under pressure to deliver a customized brand experience to any and all visitors to our branded touch points. A structured data layer is the first critical step in delivering on that promise. Increasingly, that data flow can be leveraged as real-time fuel for third-party systems that can drive personalization, retargeting, remarketing, or other immediate actions. If your data layer is incomplete or inconsistent, then some of your most profitable customer interactions may be at risk. A poorly constructed data layer can create blind spots in your overall knowledge of your customer interactions, and that means missed opportunities.
A well-constructed data layer may also be the least expensive way to achieve your omni-channel marketing goals. When you think about other approaches to gain holistic customer insight and action, most of them involve huge investments in data warehousing and business intelligence resources. Much of the costs of these approaches revolve around cleansing and augmentation to fill in knowledge gaps from the fragmented data collection, which had no upfront strategy.
People who work in data will quickly tell you that garbage in equals garbage out. A small amount of upfront planning to structure a proper data layer is far less resource intensive and the result is a far more valuable asset. This is the proverbial win-win.
