Data Teams Need To Break Out Of Their Bubble

I know, you’re probably thinking what I’m thinking: why is a CDP part of the martech stack and not the data stack?

After all, storing customer data, and sophisticated data processing like identity resolution and segmentation, is data work. And even if CDPs enable some of the work in a non-technical, no-code way–surely there should be some oversight from the data team? Or at least a close collaboration between marketing and data?

I’m not trying to argue for or against CDPs (my colleagues spend plenty of time discussing the future of CDPs and where to manage things like Identity Resolution.) My argument is that once an organization has reached the size at which a marketing team seeks out a tool like a CDP, it becomes problematic if the marketing team and the data team are not working hand-in-hand.

Given how advanced the data capabilities are of CDPs and similar marketing platforms, it’s absurd to be talking about a data stack and a separate martech stack. After all, these tech stacks are simply a means to an end: delivering a better customer experience and ultimately driving revenue.

All the decisions around a tool or platform–from the purchase and implementation, to how it can be used in an optimal way, to how it fits with other underlying architecture and software–should not be marketing-led. Rather, the evaluation should happen at a business level, and be jointly handled by marketing and data.

It sounds obvious, yet that’s not what happens in most companies.

In some organizations, the data team is not even aware of which CDP the marketing team uses. Which is, frankly, insane. It means that huge amounts of time and money are being invested into marketing’s own data “shadow infrastructure,” without insight from the people whom the company is paying as data professionals!

Shadow Infrastructure

What’s more, CDPs often end up requiring significant onboarding and maintenance work by members of the data team. In the words of Kelly Burdine, Director of Data Science and Analytics at Wellthy:

A data team can end up forced to work on a platform for which they were not consulted in the decision to purchase–and which, had they been consulted, they may very well have advised against.

First Step in Breaking Out of the Data Bubble: Learn to Speak #Martech

I started this article by suggesting that data teams need to break out of their bubble and become more business-savvy. I think a great way to start is this: attempt to bridge the divide with just one of your stakeholders–the marketing team.

Combining Data and Marketing

Step away from the modern data stack for a bit, and learn more about the martech stack and the martech lexicon. Here’s a checklist of questions you can start with:

• How does the marketing team measure success? What are the gaps here?
• What tools does your marketing team use? How do they use them?
• What data does the marketing team already have access to? Is it complete? Is it accurate?
• What other stakeholders does marketing interact with for its data needs, and what does that interaction look like?
• Being aspirational, what would the marketing team ideally want access to?
• What would this enable?

If the data team doesn't already know the answer to these questions, there's potentially a lot of opportunity and business value being left on the table.

At a minimum, you’ll learn how to better serve your marketing team requests.

But more likely, you’ll be empowered to push for deeper, transformative collaboration–work that helps marketing to achieve greater results thereby demonstrating the business value of the data team.

That way, you can say more than “I maintained those pipelines” or “built those dashboards.”

You can also take credit for business outcomes, like “our data modeling led to more accurate customer segmentation, which boosted the success of marketing’s campaigns by ‘x’ percent.” Or, “our self-serve data infrastructure has saved the marketing team ‘x’ number of hours per week, and has also ensured that their data is updated every half hour rather than twice a week.”

THOSE are statements of a data practitioner who has burst out of the data bubble and learned to think in terms of business outcomes. And it’s what we all should be striving for.