Ever feel like you’re dealing with the same problems over and over again, no matter how many times you try to fix them? Maybe one of these rings true for you:
Marketing implements an AI workflow to more efficiently segment prospects into nurture programs. After a month, the team is back at the drawing board because too many prospects have been put into the wrong segment and received irrelevant content.
At the monthly pipeline review, instead of focusing on strategy and tactics, the majority of time is spent arguing over which report has the correct numbers. Marketing, Sales, and RevOps leave frustrated with no strategic decisions made yet again.
A high-value customer raised a ticket with Customer Success a week ago to resolve a critical issue. When the Account Manager reaches out for a quarterly check-in, they get an unexpected earful from the customer about their frustrations. Meanwhile, the Account Manager is equally frustrated: this is the 3rd customer this month that had issues and he didn’t know about it.
At face value, these situations may seem like the only connection they have is that they’re a typical frustration for Go-to-Market (GTM) teams. In reality, they’re all underlined by issues in customer data governance.
What is customer data governance, how does it differ from data governance, and is it really that important? Let’s get into it.
Data Governance vs Customer Data Governance
Although the terms are sometimes used interchangeably (guilty as charged!), they do signify two distinctly different focuses.
Data governance deals with all data in an organization, establishing rules and responsibilities around: what is collected, how it is defined and maintained, where it’s stored, who has access, and how it can be shared and used. This covers everything from HR records to inventory levels, from financial ledgers to legal compliance.
Under the umbrella of data governance, though, there is a specialized subset that specifically focuses on the data that is captured, maintained, and used throughout the customer lifecycle. It’s this subset that is the focus when teams refer to customer data governance.
Unsurprisingly, given the area of focus, the teams most impacted by these practices are Marketing, Sales, and other teams under the GTM umbrella. Essentially, customer data governance helps ensure that these teams are all using the same language and playbook in their daily operations. In particular, these governance policies look to set:
Definitions: What the data captured means
Standards: Rules for how information is captured, updated, and used
Ownership: The person or team who specifically has responsibility for the data
Expectations: What information needs to be shared, when, and with whom
For example, a governance rule around Sales Qualified Leads (SQL) could look like:
Definition: An SQL is a prospect that has booked a meeting with Sales
Standards: Key data to capture is prospect’s first & last name, company, email, and phone number
Ownership: Marketing will capture the key data, Sales will update missing or incomplete data during their interactions with the prospect, Operations is responsible for ongoing enrichment and updating
Expectations: Key data will be captured before the lead is passed, and it will be entered into Salesforce so that Sales can access it
By establishing shared definitions and expectations, each team understands what data needs to be captured, who will do it and when.
Just How Important Is It?
While it’s all well and fine on paper to say that customer data governance brings alignment and structure to Marketing, Sales and other GTM teams, what does that actually mean in practice? Let’s go back to the examples at the start of this article.
The failed AI project: AI is only as strong as the data and rules it works with. If Marketing and Sales have different definitions of customer segments, or customer records are inconsistently categorized in the CRM, AI can’t reliably determine which nurture campaign someone belongs in. Without agreed definitions, standards for maintaining customer information, and clear ownership of that data, the AI workflow is working with a shaky foundation. Instead of improving efficiency, that automation simply accelerates the errors already happening in the business.
The inconsistent reports: Reporting isn’t just about having data available to make a report. It’s equally important that teams agree on defined terms, the primary places to take data from, and that data is regularly reviewed and updated to avoid inconsistent records. It’s the difference between a pipeline report that has the same results no matter who builds it and Marketing basing their pipeline report on opportunities created this month while Sales builds their report on opportunities closed this month because the definition of pipeline hasn’t been agreed to. Both reports show pipeline, but Marketing and Sales arrive at the meeting with wildly different reports.
The frustrated customer: In today’s platform heavy world, it is commonplace that different teams use different systems. Also common: not having clear expectations around what customer information needs to be shared, with whom, and when, resulting in a siloed view of the customer. If Customer Success does all their work in Zendesk while Sales lives exclusively in Salesforce, and those platforms aren’t connected and passing the right information between them, then Sales will struggle to know that a customer is having an issue that they need to follow up on. With customer data governance guiding how information is shared, teams can build a more complete picture of their customers and respond proactively to issues.
Often, teams hear the term “governance” and assume policies around customer data is just more bureaucracy. In reality it is the structure that can help teams avoid repeatedly running into the same problems while also helping to fuel effective processes.
What You Can Do Today
Here are 3 simple things you can do today to get started on your governance journey:
Observe: Are there bottlenecks, challenges, or inconsistencies that seem to come up regularly in a report, customer information or a cross-team handoff? Make a note of what these are and observe: does the problem happen at the same moment every time? How is it currently handled? Do others complain about it or is it just accepted as “the way it is”? Sometimes the first step is just noticing.
Get curious: Talk with other teams about how they work. Are they using systems that you didn’t know about? What data is most important to them to capture or have they even given it any thought? What happens when work is handed off? Understand what is happening outside of your space and whether this surfaces additional gaps in how teams work together.
Pick a problem: Now that you have more context, look at the list of bottlenecks, challenges and problems you made. Pick one from the list and do a mini diagnostic, working through the 4 areas of governance mentioned above: Definition, Standards, Ownership, Expectations. Are there gaps or misalignments that surface? These are issues you can begin addressing.
If any of those three scenarios sounded familiar, you’re not alone, and it’s usually fixable. Let’s talk about what’s happening in your team’s data and where to start.