Trust is an underrated concept as it applies to an organization’s use of business data, including customer data. Marketers and other business users of data often take for granted that the data they’re working with has been vetted, is accurate, up-to-date and fit-for-purpose for the business objective they’re trying to achieve.
Data enrichment and data validation, in other words, are under-appreciated as important components in underpinning the accuracy of a resulting Golden Record: a pristine, unified customer profile that is the basis for delivering a personalized, omnichannel customer experience.
There is some confusion about what the deprecation of the third-party cookie means for data enrichment, which by definition entails using third-party data to enrich an organization’s own first-party data. The loss of the third-party tracking cookie, however, does not impact the validity of all third-party data. What is true is that data enrichment and data validation are not an open invitation to collect and/or trust every additional source of data to bolster existing first-party data.
Data Enrichment vs. Data Validation
Before addressing best practices for incorporating data enrichment and data validation processes into the creation of a Golden Record, a quick primer on the terms. Data validation is commonly understood as the process of verifying the accuracy, structure and quality of data prior to processing. Data validation can be as simple as confirming that a U.S. state abbreviation value is two characters. . However, data validation can also involve leveraging a trusted third-party to confirm a given dataset. A good example is the National Change of Address (NCOA) database, of which there is only one in the United States. As such, it is accepted as the single source of truth for validating a customer’s current mailing address. (The NCOA logs and keeps all change of address records for four years).
Data enrichment is not as clear cut. It can refer to leveraging first-party data to enhance existing data sets (common in more siloed organizations), or the use of one of many third-party vendors for processes such as demographic or geographic overlays, any of which may use different methods to find out information about a person. Or, for that matter, have different standards. For instance, many companies will offer age overlays, wealth overlays, or reverse email appends. Unlike the NCOA, however, which relies on a customer filling out a change of address form, there is no universal understanding of where the information about a customer’s age, income/wealth or email originates from, or how the vendor files it. Even if a vendor returns an age overlay with a specific birthdate, the company that buys the overlay is asked to trust its veracity. Other vendors might return ranges – here’s an overlay of people aged 25-34.
One way to think of the difference between the two is that data validation should be used along with data enrichment. That is, when a given dataset is sent for enrichment, the return data should be run through the same data validation processes as first-party data where applicable. Data enrichment without validation may not be harmful on its own, but in order to maintain data integrity for the end goal of a Golden Record, the validation step is critical prior to leveraging any data in a production environment.
Consider Your Business Purpose
Other factors to consider when incorporating data enrichment and validation processes into the creation of a Golden Record is the underlying business purpose, which frequently relates to the type of industry a business is in.
A non-profit media company that heavily relies on accurate, up-to-date donor lists – and with a target demographic typically skewing older – will be far more interested in age overlays (or even death overlays) than a retailer. Likewise, a financial services company will have more of an interest in a wealth overlay or credit scores than a healthcare organization.
As for the business purpose itself, generally speaking the more personal a communication the more a business will have to trust in the method of enrichment. A healthcare organization that emails customers about a new provider joining the network may be comfortable with enriching its email files with a reverse email append, whereas it would not rely on a verified email to communicate with a customer about a diagnosis.
Privacy and Compliance
The above example also takes us into the area of privacy and compliance. Data enrichment and data validation do not give an organization free reign to use data any way the business sees fit. Rather, there are a host of regulations – industry-specific and otherwise – that businesses must comply with for how data is collected, stored and used. Most people are familiar with GDPR and CCPA as government regulations that protect consumers’ data privacy, but other regulations such as HIPAA in healthcare and GLBA in financial services spell out in more detail how those organizations must treat patient and consumer data, respectively.
Government regulations aside, a business must also self-regulate in terms of its approach to using customer data. That is, customers themselves are the ultimate arbiter for what’s acceptable or not for how their data is used. It might be off-putting, for example, for a customer to open a Happy Birthday email from a brand it rarely patronizes. Following the tried-and-true “less is more” adage, often the best approach is to only reveal what you know about a customer when it will help guide a customer through a customer journey or give the customer something of value.
The customer data value exchange, which we’ve covered extensively, refers to consumers willing to provide organizations with personal data in exchange for a more personalized – i.e. relevant – customer experience. In a recent Dynata survey, 73 percent of consumers surveyed said they either “rarely” or “never” provide personal data without knowing explicitly how it will be used. Meanwhile, 59 percent said receiving personalized offers or discounts is their main inducement for providing personal data.
Data enrichment and data validation, then, are instrumental in ensuring that first-party data is fit-for-purpose to provide consumers with the type of personalized customer experience they’ve come to expect. Even so, brands must take heed that enrichment and validation are not an open invitation to use all data that comes into an organization, fit-for-purpose or not.
An Ongoing 24/7 Process
One best practice for data enrichment and data validation is to undertake those processes with the understanding that a businesses’ first-party data is the core of a Golden Record. Perfecting an organization’s own first-party data and making it fit-for-purpose is, after all, the entire purpose of data enrichment and data validation.
To that end, identity resolution is the process of using probabilistic and deterministic matching to increase the veracity of a customer record, and is indispensable in creating a Golden Record. If an updated NCOA file shows a customer by the name of Jon Smith living at 123 Main St. in Boston, and your record shows a Jonathan Smith at 123 Main Street, identity resolution will make the determination if the two records should be matched.
With that in mind, another important best practice is that data enrichment and data validation are continual processes. If a marketing campaign depends on having a customer’s correct mailing address, a yearly NCOA file might be insufficient for reaching campaign metrics. Likewise, an age append that sends a file of customers aged 25-34 might suffice for some campaigns, but people will age in and out of that demographic daily.
A pristine Golden Record that drives real-time, omnichannel customer experiences must be continually updated with fit-for-purpose data which requires that data enrichment and validation steps are completed as soon as data is ingested.
A Harris Poll commissioned by Redpoint underscores why data enrichment and data validation are vital in providing customers with omnichannel personalization. Just 18 percent of consumers surveyed said they are very confident in the quality of data that brands have about them. They say it’s all-too common that brands engage using outdated, inaccurate or inconsistent data, with more than half (51 percent) claiming that the result is an impersonal or irrelevant experience.
Furthermore, marketers themselves say that improving the accuracy of their customer data is their No. 1 data quality objective. Adopting a sound data enrichment and data validation processes is a good way to start.