Own your marketing data: To-dos for good data governance
While it's not glamorous, columnist Alison Lohse points out that working to achieve strong data governance is crucial to ensuring consistency, both within your organization and with external partners.
The famous quote frequently attributed to Peter Drucker goes, “You can’t manage what you can’t measure.” Marketers agree. They want to make data-driven decisions based on cohesive marketing measurement.
Accurate measurement needs good data — or else it’s garbage in, garbage out. While the importance of data is undisputed, data governance is often last on the priorities list, giving way to more exciting martech tools. Yet, lack of data governance translates to poor decisions and ironically, challenges to onboarding any new tool or technology in the martech stack.
When we start an attribution implementation, more often than not, we end up house-cleaning clients’ data. We find the dusty corners of ignored channels, the clutter in naming conventions and the mismatch of governance across the organization. This usually means different parts of the organization have their own way of looking at the business, making cross-functional decisions difficult.
While cleaning and organizing aren’t at the top of everyone’s to do list, the efficiencies gained are worth it, even if it’s incremental wins. Here are some basics for every organization to get started on better data governance and on the right path to data-driven decisions.
1. Understand data and metadata
Identify all data sources and dimensions captured, both currently and historically. What is or was being tracked internally or by a third party? How it’s being tracked is important — are there business rules to what actions or values are counted in the reports? Are they documented?
For example, conversion windows are commonly set on site-side tags and changed through the life cycle of a client. This can result in wide swings when comparing, year over year, periods where business rules have changed.
Isolate the dimensions being captured by various teams. What detail is important to different constituents? Why? What’s common across the business? Ultimately, data and dimensions consistency across various channels and owners leads to usable universal data for any martech tool.
2. Gain internal alignment
Given constraints of tracking, internal alignment on how the data is applied organizationally is key. Different types of data may also need different security measures. For example, perhaps the first-party client data is not divulged to partners and has strict PII norms guiding it, whereas a hashed anonymous user ID is not subject to the same regulations.
Identifying business use cases and outcomes expected is a great way to showcase potential and align objectives. Which brings us to the next point.
3. Establish some ground rules
Standardizing key performance indicators (KPIs) across the business, ensure that all marketing is driving to the same business objectives. For example, if the SEM channel owner is focused on overall click volume, but the email manager is about qualified leads, the investment could work at cross purposes.
Once the KPIs are established and agreed upon, all data roll up to those KPIs and help in consistency of measurement.
4. Enforce compliance
Don’t let the tail wag the dog. Every technology vendor will want you to implement their technology and may try to change your data governance to meet their specifications. You know your business best, and vendors should comply to the guidelines you’ve established. Require vendors to use the nomenclatures and KPIs you have defined. This allows you to layer more technology solutions in your stack over time with limited disruptions.
5. Own your data
Historical data is a gold mine for analysis, but data tends to live in a lot of locations, including agencies and other partners. Having a centralized repository of marketing data for partners to feed into, with an identified owner, can put you in charge of your data story. This can not only facilitate future analysis, but it can also aid in technology or transitions as your organization grows and changes.
6. Memorialize it
Clear technical documentation ensures alignment and accountability with cross-functional teams about ongoing ownership, and updates help sustain data governance. This type of documentation can include data schematics, source APIs, data mapping tables, naming conventions, business rules and more. Not only does it ensure consistency, but it becomes part of the culture. Even when there are exits and new resources onboarded to manage programs, there is documentation to ensure continuity.
Finally, I would say take heart, as it’s not easy, but achievable. Set your strategy, identify internal advocates, find partners to facilitate and start with small wins. Benchmark to get better and structured. An organization with good data governance has better defined goals and better probability of achieving them. And what is more satisfying that a clean house?