The role of data governance in adtech & martech
In a world where data is core to running business, establishing and maintaining responsible data practices is critical. Data governance plays an important role in outlining guardrails and policies for secure and compliant data handling. As key data users, marketers and advertisers need to understand and follow these policies. The consequences of not doing so can be severe, and include increased likelihood of data breaches, non-compliance of regulatory requirements, and brand reputational damage. Here are some key best practices to keep in mind.
Collect only data that’s needed
It can be tempting for marketers and advertisers to want to acquire as much data as possible about their customers. However, data richness is not so much about the quantity of data an organization has, but rather how relevant and actionable that data is. Whether collecting first, second or third party data, it’s important to consider what information is actually required in order to understand the customer and run effective campaigns. This is particularly true for PII (personally identifiable information). If there is no use case that justifies collecting a customer’s full home address, for example, then this should be avoided.
Only keep data for as long as it’s needed
Data retention policies exist to ensure that data is only kept for a reasonable period of time. A data destruction schedule defined by the organization should be in place so that data beyond a defined period is deleted (with the exception of data that has been requested for audit or legal hold). These data lifecycle policies apply not only to data residing in databases, but also data stored in martech & adtech platforms, as well as “loose” data files (such as externally obtained files). Data shared with third parties, including campaign audiences shared with agencies for activation, should also be purged after an agreed upon period after the campaigns have ended.
Share data securely
A data sharing agreement is essential for outlining how data will be shared among parties. It indicates the nature of the data shared, the permitted uses, and each party’s responsibilities. To the extent possible, it’s ideal that data be transferred directly from platform-to-platform backend rather than handled manually. Customer Data Platforms are advantageous in that they typically enable secure delivery of campaign audiences directly from the platform to activation destinations such as social, advertising and outbound marketing platforms, all while hashing customer identifiers such as email addresses.
Data Clean Rooms allow parties to bring together datasets from various sources into a controlled environment for joint analysis. This environment anonymizes PII and doesn’t allow data to be shared outside the room, making it a secure option for data collaboration.
Strive for data accuracy
Maintaining a high data quality standard benefits not only organizations, but also their customers. Among the more critical data points to capture accurately are customer consent preferences. Marketing communication opt-in status updates should always be captured reliably and synced across systems in a timely way. This helps ensure not only compliance with CASL regulations, but also a positive customer experience.
When it comes to data cleanliness, standardization and completeness, customer self-entered data is notoriously problematic. Having automated validation in place at the point of data entry (for example checking that an entered email address format is valid) helps to ensure cleaner data downstream, but often doesn’t guarantee perfect data. Data fields used in personalization should be checked to filter out empty or “bad” values, such as first names containing profanity, to avoid poor customer experiences.
Data governance should never be an afterthought or purely a reactive response to a data issue. It should be foundational to any business practice involving data. Ultimately, it provides a framework for operating data ethically and maintaining customer trust.
