By Mariana Abdala
For years, I have been of the mind that more data means better decisions. This is a core component of building digital products. But the world has changed.
Privacy regulations, platform restrictions, and customer expectations are reshaping how we collect, measure, and interpret data. Signals that could once be interpreted as precise now feel fuzzy. Dashboards that once implied certainty now raise new questions. While running the Mobile product portfolio at Williams-Sonoma, I looked at Attribution to tell a clean story about how we were acquiring new customers and retaining existing ones through a net new channel like the branded mobile apps we had launched for Pottery Barn, West Elm and Williams-Sonoma. A spike in conversions could be traced to a particular campaign, a channel, even a keyword in our SEO strategy. But we were on the precipice of new privacy changes, cookie loss, AI-assisted browsing, and dark social, “direct traffic”, which is now a catch-all bucket that hides more than it reveals. It was also a given that Revenue growth was a proxy for lifetime value (LTV), but in reality, if we take a closer look at revenue metrics, we have to ask ourselves: Is ARR increasing due to genuine adoption, aggressive discounting, seat inflation, or procurement cycles catching up late? Again, the metrics may be clearly leaning upward in a clear direction when you look at a dashboard, but the story underneath is becoming increasingly more ambiguous.
This is not a phase. It is the new operating environment.
Product leaders must optimize for insight without surveillance, clarity without overreach, and responsibility without paralysis. This means that we need to change the way we approach metrics gathering and data analysis in our work.
The Collapse of Perfect Visibility
Platform rules have limited the ability to fully track end-to-end attribution. Browsers have blocked cookies by default in some cases. Entire journeys have faded from view depending on a person’s or an organization’s settings. As privacy frameworks mature and users become more aware of how their personal and activity data are used, Product teams need to consider how they will account for the increasingly reduced visibility, and evolve how they think about measurement.
From Surveillance to Signal
When teams can no longer track everything, they are forced to decide what actually matters. That constraint is healthy. It pushes focus toward outcomes instead of activity, indicators instead of exhaust, and clarity instead of volume.
This is not a downgrade. It is a refinement. Less data does not reduce insight. It increases the signal-to-noise ratio.
Redesigning Metrics Intentionally
When data disappears, teams often hunt for replacements. What new tool can we use? What workaround can restore what we lost? These questions miss the real issue.
Before tools, teams must revisit fundamentals. A strong metric strategy begins with understanding what progress truly looks like for customers and which behaviors signal it.
Modern measurement starts with four anchors:
What meaningful value looks like to the customer.
Which behaviors signal forward motion.
Who owns outcomes across teams.
What decisions metrics are meant to inform.
As Product people, we get to design our metrics environment, even in cases where we’ve inherited the metrics we’re monitoring.
Living With Proxies
In privacy-constrained environments, proxy metrics become unavoidable. If you’ve ever worked with me, you know that I reference proxy metrics often to introduce a broader perspective or to facilitate building hypotheses when some of our more desirable metrics are absent. The danger is treating the convenience of these proxy metrics as truth. Keep in mind that good proxy metrics are behavioral, interpretable, and directional. These are a few examples of how proxy metrics can help with metric gaps:
If retention becomes hard to measure, feature usage becomes a guide.
If funnel data breaks, pipeline movement becomes a signal.
If journey tracking erodes, repeat behavior fills the gap.
Proxies do not weaken decisions, but misinterpretation surely does.
Listening as Measurement
My favorite data are usually the voice of the customers themselves. As quantitative signals fade, qualitative insight becomes essential. User interviews, support conversations, and feedback channels provide texture that dashboards cannot. They explain the “why” behind behavior. The strongest teams can triangulate:
Directional product metrics.
Customer feedback.
Real-world observation.
No single signal is sufficient alone.
Trust Becomes a Metric
Privacy is not just compliance. It is experience design. Customers increasingly judge products by how respectfully data are handled, which is why I cannot stress enough that the transparency of the way your product is handling information builds confidence. Confusion destroys it. Trust is reinforced when customers understand:
What data are used?
Why is it used?
How decisions are made.
What control they have.
I cannot stress enough that part of our jobs as Product leaders is to be ethical. Ethical analytics is not risk avoidance. It fosters product quality and customer trust.
Leading Without Perfect Data
Reduced visibility exposes weak strategy. Teams that relied solely on dashboards feel lost. Teams that understand their customers continue moving. This is the difference between reporting and leading. In uncertain environments, leadership matters more than analytics software.
Mature product leaders provide direction when data are incomplete, confidence when metrics conflict, and discipline around what not to measure.
The Takeaway
The age of unlimited tracking is over. The age of intentional measurement is here. Product metrics are no longer about knowing everything. They are about knowing enough to decide, adapt, and earn trust. Privacy has not weakened product organizations. It has made the strong ones stand out.