Timeliness in Data Readiness: The Hidden Driver of Relevance

In a Gartner report on how to evaluate AI data readiness, timeliness is one of five data governance components listed as essential for approving data for use – especially when data is captured across multiple systems or when multiple datasets are combined.  

Previous posts in this series on data readiness focused on what it means for data to be complete and accurate to support existing or emerging business, CX or AI use cases. Here, the attention turns to timeliness as the third pillar of getting data right – with more to follow on the fit-for-purpose pillars of being actionable, trusted and compliant.  

Timeliness as a key component of data readiness is distinct from real time actionability – the ability to deliver a next-best action across an omnichannel customer journey. Rather, it refers to timeliness in the building of the unified customer profile that provides a real-time, contextual understanding of a customer, household or business. It is timely if it is updated and available in the cadence of a customer journey, no matter what cadence any one customer has. 

Just as the completeness and accuracy of data are not mutually exclusive, timeliness is an integral part of a data readiness process. That is, data can be complete and accurate without being timely, as well as being timely without being complete or accurate.  

Updated Attributes and Model Scores 

Timeliness in building and maintaining a unified profile means that the profile is continually updated, and that attributes and model scores are continually updated. These updates are essential to guarantee that a profile reflects a customer’s most current behavior, preferences and likely actions.  

The Six Pillars of Data Readiness: Timely

The continuous updating of attributes means that identifiers and every data point associated with a customer – name, email, and contact details, last purchase data, preferred channel, browsing history, loyalty tier, etc. – are continually updated. To make these real-time or near real-time updates to a unified profile, a data readiness hub must continuously ingest new data from all channels and sources to ensure that the profile reflects the latest understanding of a customer. In addition, the sources themselves – POS, CRM, EHR, etc. – must also be updated in real time, ensuring for instance that a transaction record reflects an updated view. 

The same holds true for real-time updates to model scores such as propensity to buy, churn risk, customer lifetime value (CLV) and any other predictive indicator used by the business to engage with a customer. Recalculating model scores as new behaviors and events occur vs. on a batch basis is a foundational requirement for engaging a customer with a relevant experience. 

Why Timeliness Matters 

Together, real-time updates to the profile attributes and model scores are what allow marketers to react in the moment, not after the moment has passed. But more importantly, the combination ensures that personalization is contextual and timely, not based on stale data. This is where the difference between real-time decisioning and timely updates becomes apparent. Because when a brand reacts in real time – such as real-time website recommendations – with outdated attributes or models, the interaction is likely to be irrelevant. 

Conversely, a real-time decision based on real-time updates to a unified profile (complete, accurate and timely) results in triggered communications that strike with pitch-perfect relevance. A partial list includes cart abandonment emails or SMS notifications, offers or content that align with a customer journey. Updated model scores also enable a brand to prioritize outreach, such as targeting an audience more likely to purchase. A timely profile even influences suppressions, e.g., withholding an offer for a customer with a recent purchase.  

Making sure that your customer profiles are complete, accurate and timely are essential for getting data right. That is, for making sure that a profile represents the customer, household or business that a brand is trying to understand. But having the right data does not guarantee that it will drive decisions. That’s where the next pillar of data readiness comes in: actionability. In the next post, we’ll explore what it means for data to be truly actionable – and why even the best data falls short if it isn’t used effectively.