In a perfect world, marketers would have information at their fingertips about whether enterprise customer data is fit-for-purpose. Metadata-driven automations that reveal something about how active data is flowing through a system provide some information of value. Harnessing this value to take a prescribed action brings us into the realm of a data fabric and its role as a marketing automation tool, including for marketers intent on optimizing customer data to differentiate on customer experience.
Almost by definition, dynamic, active metadata that presents a live view of what’s happening inside of a system is always changing. At a basic level, the automation of tasks to understand those changes is a data fabric. As an example, consider a data management automation that continually assessed a data quality index for a set of columns and paused a workflow if the index dropped below a preset level.
The automation of data processes can range from simple to extraordinarily complex. In the customer data realm, a good example of data fabric automation might be using active metadata to extend a CDP, perhaps when a new database comes online, automatically querying its content and mapping customer data to the CDP.
Conceptually, a data fabric may have an expanding role within a CDP. At its core, a CDP is an automation platform for customer experience. It stands to reason, then, that harnessing active metadata through the automation of multiple data processes will directly contribute to customer experience enhancements. Automation based on data is already prevalent, such as the automatic suppression of emails to an audience that opened a previous email within a campaign.
Automating a task based on metadata carries this a step further, such as initiating a retention campaign if an NPS score falls below a certain level. Or, if an NPS average dips below an acceptable number diverting resources to address the root cause. As an automation platform for customer experience, a robust CDP can be an important component of a data fabric architecture in both directions; feeding metadata in the form of inferences, propensities and decisions into a data fabric to make automations occur, and conversely using automations managed by the fabric to effect change. In this context, a robust CDP is still laser focused at the data level on customer data required to drive a superior CX, which can be an important component of a data fabric, but the CDP is not operating as the entire underlying data infrastructure.
Data Fabric: An Ally in Improving CX
If a data fabric is at its core about automation using active metadata, data mesh is about governance, i.e. setting up rules for metadata to improve outcomes, such as determining the acceptable NPS average, for instance, or when to pause a workflow. Both data fabric and data mesh share the goal of using automation for better outcomes through metadata, with the key difference that a data mesh is centered around building governance into systems.
From the marketer’s standpoint, it’s primarily a difference of semantics; both involve use cases intended to become smarter about using metadata. What, then, does this all mean for the day-to-day operational marketer? In the short term, I would argue not much. Again, if we were to look at an ideal world in which a marketer had unlimited information about how consumer data is fit-for-purpose, that might manifest itself in something like a trust index, where an underlying data fabric contributes to a continuous assessment of data’s readiness, thus giving marketers confidence to make bold decisions.
In the longer term, as the data fabric design matures, existing systems that may now simply share metadata might become more active participants in a data fabric architecture, adapting to alerts and recommendations generated by the fabric through AI, for example.
At its core, though, a data fabric is about metadata-driven automation, particularly active metadata. And the reason it should be on the radar for marketers in the CX arena is that there an endless number of marketing CX initiatives that can be improved by metadata-driven automation.
Related Redpoint Orchard Blogs
What to Know About Metadata at the Data Layer
What’s the Deal with Customer Metadata? Why it Matters for CX
What We Mean When We Talk About Data Quality
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