Marketing teams talk a lot about growth. More leads, more conversions, more revenue. But without solid data foundations, scaling your business can feel more like stacking cards in a windstorm than building something sustainable.
Diana Gonzalez, Director of Revenue Operations at Riverside, has seen what happens when businesses overlook data quality early on. In her conversation on The Undiscovered Metric, she laid out why strong data foundations are critical and what marketers can do to get them right.
Watch the full episode below or read on for the key takeaways.
Do your future self a favor and get your data architecture right the first time
Marketers are often tempted to treat data governance as a “later” problem, but that mindset leads to costly, time-consuming fixes down the road.
"The very first thing that you have to make sure is that from the beginning, you do have a data architecture that will enable you to expand in the future," says Diana. This means defining key data structures (contacts, accounts, deals) and ensuring the right data is being captured in a way that won’t need an overhaul six months later.
Diana warns marketers, "If you don’t do it really, really thoroughly from the beginning, you do have to spend a lot more time fixing things down the line."
That fix isn’t just expensive—it’s a nightmare of broken workflows, unreliable reporting, and wasted effort. Diana stresses that even if resources are tight, marketing teams should prioritize setting up the right data architecture first.
Make sure you have a data architecture that will enable you to expand in the future right from the beginning.Enrichment and access: The key to actionable data
A core part of streamlining data architecture is enriching your data. For Diana Gonzalez, Director of Revenue Operations at Riverside, this isn’t just about filling in missing data—it’s about prioritizing the right data for different markets. "There are tools that will have better data for US markets versus the European markets versus APAC, et cetera. And so having a situation where you're able to prioritize the enrichment based on the region and do things like waterfall enrichment." By layering data from multiple sources and using AI for categorization, they ensure that each record is as useful and relevant as possible.
“The second piece for me is around being able to see the full picture. So the whole journey of a company and a contact. And there's so many different pieces that get tied to that,” explains Diana. At Riverside, that means ensuring every team—marketing, sales, and customer success—works from the same enriched, unified dataset instead of cobbling together fragmented information.
By pulling data from multiple sources into HubSpot, they create a single view of every customer interaction—from first marketing touch to post-sale engagement. “If a business development rep is looking at an account, then they're able to see the entire trajectory—from marketing interactions to past conversations with sales, to whether they've had a Riverside plan before, and even interactions with our CS team.”
This alignment doesn’t just clean up data—it makes it actionable. By enriching data to create a complete customer view, Riverside ensures teams make smarter, more strategic decisions without gaps or guesswork.
Data democratization: Getting teams on the same page
Data is only valuable if teams can access and interpret it. That’s why Diana emphasizes, "Data democratization is not a nice-to-have. It's a must-have."
At Riverside, she ensures teams aren’t working in silos by aligning them around shared dashboards that track the entire funnel—from MQLs to retention. Diana explains, "If everybody is able to have access to that exact same dashboard, then we're able to create that alignment where everybody's looking at the same thing."
This transparency helps teams move beyond vanity metrics to focus on conversions that actually matter. It also keeps marketing, sales, and customer success teams accountable to the same performance metrics.
Two common pitfalls marketers should avoid
Despite their best intentions, many marketing teams still make data mistakes. Diana points to two major pitfalls:
1. Focusing too much on top-of-funnel numbers
It’s easy to get bogged down with engagement metrics and traffic, and while these are important indicators, we need to step back to see the full picture, especially when it comes to reporting. "There’s a false sense of achievement with the higher number of MQLs or leads," says Diana. Instead of celebrating big numbers, marketers should look deeper at conversion rates to see if those leads are actually turning into revenue.
2. Creating data structures that only work for marketing
"I've seen marketing teams creating multiple properties or workflows exclusively based on marketing, without any regard of how this is going to be looked at throughout the rest of the funnel," explains Diana. Data needs to serve the whole business, not just marketing. Teams should think ahead about how data will be used across sales and customer success, not just in marketing reports.
Which metrics should marketers be watching?
When asked which marketing metrics deserve more attention, Diana doesn’t hesitate: "The number one KPI for marketing should be MQL to close-won conversion."
Marketers often obsess over generating leads, but without a clear focus on how many of those leads actually turn into customers, they risk optimizing for the wrong things.
Beyond conversion rates, she also highlights the importance of tracking the ‘dark funnel’—the interactions happening outside traditional marketing attribution, such as social media engagement and brand mentions. "Marketing is everywhere," says Diana, "and it’s really difficult to track how a certain campaign may have influenced a lead to eventually enter your funnel."
While tricky to measure, understanding these hidden influence points can give marketers a fuller picture of their impact.
Final thoughts: Invest in your data now or pay for it later
Marketers may not always have the resources to perfect their data strategy upfront, but Diana makes one thing clear: ignoring data quality and architecture today means spending far more time and money fixing it later. By focusing on data enrichment, democratization, and cross-team alignment, marketing teams can set themselves up for success—without a costly clean-up job down the line.
