Customer Data Platforms (CDPs) are often seen as the silver bullet for achieving unified customer profiles, but that assumption can be dangerously misleading. Simply having a CDP in place does not mean your data is ready for business use. In fact, quite the opposite is true; this misconception is a leading cause of failed CDP implementations and unmet expectations.
Many organizations discover too late that their CDP data lacks the completeness, accuracy, and trustworthiness needed to drive meaningful outcomes. This blog explores why CDP data readiness is essential, and how it ensures your data is not just collected, but truly business-ready.
What is CDP Data Readiness?
When we consider the original intent of a CDP – to make customer data right and fit-for-purpose – any activity that entails moving data around (reverse ETL, etc.) should be secondary to the CDP data itself. That is, CDP data readiness is about meticulously preparing data for use in the CDP. It elevates the processes central to the building of the unified profile that enables marketers and business users to trust that CDP data accurately reflects the customer, household or other entity the brand is trying to understand.
The Six Pillars of Data Readiness
CDP data readiness consists of six distinct criteria vital for ensuring that a CDP’s data is ready for any use case – whether it’s for CX, AI, or another business purpose.
The first three pillars are vital to make sure that CDP data builds a unified profile that reflects the right customer or household:
Once CDP data is validated through those three steps, CDP data readiness also entails making sure that the CDP data is fit-for-purpose, which covers the next three pillars of data readiness:
Understand How Your CDP Data is Prepared
Often, CDP data fails to meet the exacting threshold required by the six data readiness pillars because data quality is not given its due. Many CDPs ignore data quality, operating instead on the presumption that CDP data quality occurs either upstream (before it enters the CDP) or downstream (the responsibility of end systems that eventually use CDP data).
Data readiness consists of the steps needed to make sure that data is right and fit-for-purpose.
For instance, many CDPs often unify flawed inputs without resolving root-level issues, leading to sub-optimal outcomes and high data consumption costs. Ideally, standardization and error correction should occur the moment data enters the system, so downstream processes stay efficient, accurate and on track. Identity resolution and profile unification processes only work when the input data is clean.
CDP Data and Advanced Identity Resolution
Furthermore, some CDPs consider performing a simple deterministic match in time to be sufficient in terms of the steps a CDP must take to ensure that CDP data is ready for business use. This is in stark contrast with a CDP that prioritizes all facets of data quality, part of which includes advanced identity resolution processes that not only cleanses and standardizes all CDP data, but that also uses the right combination of deterministic and probabilistic matching and persistent key management to provide users with a high degree of certainty that the customer or household a brand wants to engage with is the right customer or household.
Applying advanced identity resolution techniques as a key part of CDP data readiness not only lets the brand know a customer’s identity, but when rolled into an updated unified customer profile it also provides a contextual understanding of a customer across an ongoing customer journey. That’s because unlike a basic match in time, persistent key management provides a longitudinal view of a customer that is important for understanding a customer’s preferences and behaviors over time – knowledge that helps a brand better understand customer intent.
The Impact of Poor CDP Data Quality
The truth is, very few CDPs are designed to address all of the underlying data activities that need to occur to get data right and ready for business use. The offshoot is a unified profile that fails to accurately capture a customer or household in the moment of engagement, which leads to downstream issues and, ultimately, a poor CX, AI results that can’t fully be trusted, or otherwise poor business results. What also happens is a deprecation of the single customer view – a problem that the CDP was originally intended to solve for. Because when data quality process are either ignored or assumed to be performed elsewhere, different systems will then apply different data quality standards – leading to different interpretations or understandings of a customer.
When a system is not purpose-built to handle CDP data readiness, issues having to do with a lack of data quality, governance and a lack of repeatable processes both become harder to identity and harder to resolve.
The way forward is not with a run of the mill CDP, but rather CDP data readiness; a data readiness platform that takes CDP data and makes it both right and fit-for-purpose for whatever your intended use case.
For more on how Redpoint can help you get your data right and fit for business purpose, or to talk to a data readiness expert, click here.
