Why Working Through Your Backlog Won’t Save Your Product
Many B2B product teams face a common challenge: reaching escape velocity — the point where they can shift from reactive backlog management to proactive product discovery. Breaking free from the gravitational pull of an ever-growing backlog of feature requests, technical debt, and sales-driven commitments (what some refer to as "promise debt") requires more than better backlog grooming. The true solution lies in making higher-quality business decisions — not in managing the backlog itself.
The Hard Truth About Escape Velocity
Every product team faces the same core constraints:
Time is scarce: You can’t do everything.
Effort is scarce: Engineering resources are finite.
Money is scarce: Prioritization always involves trade-offs.
And yet, many teams remain stuck in reactive loops, trying to address every request without stepping back to evaluate what truly matters.
Reframing the Problem: From Backlog Management to Delivering Value
To make better product decisions, we need to shift our perspectives. Rather than asking:
How do we build everything in our backlog?
The real question should be:
How do we make the best decision possible based on the information we have today?
and we accomplish this by shifting our perspectives from deciding:
whether or not we should build feature XYZ
to:
Which should we build first?
This transition moves the focus from execution to evaluation. Instead of tracking progress by how quickly tickets are cleared, it should be measured by how effectively teams prioritize work that delivers meaningful impact.
It means that escape velocity is not achieved by working through your backlog faster. Instead, it’s a by-product of making smarter business decisions — decisions that ensure the right problems get solved first.
So how do we do this and why is it so hard to implement?
Why It’s So Hard to Make Better Decisions
Shifting away from reactive backlog management isn’t just about putting your foot down and making a bold decision to prioritize proactive discovery. It’s about ensuring that new initiatives are backed by compelling, high-quality evidence — strong enough to compete with the existing justification for backlog items.
The most overlooked yet critical correlation in product management is:
The quality of the decisions you make is directly correlated to the quality of the data you have available.
For instance, if the backlog includes a long-awaited improvement to feature X, but new evidence shows that focusing on initiative Y would deliver greater impact with less effort, which should the team prioritize? Clearly, initiative Y. But uncovering such opportunities is only possible with a systematic approach to gathering, structuring, and evaluating data. Without this, teams default to gut feelings, the loudest voices, urgent fixes, or simply the longest-standing backlog items.
A Data System for Better Product Decisions
To build a system that can enable proactive product discovery, teams need a framework for collecting, structuring, and leveraging better data. A simple four-step model helps:
1. Gather Raw Inputs
First, you need a holistic understanding of reality. Raw data can come from multiple sources:
Customer Feedback (emails, NPS responses, interviews)
Commercial Team Insights (win/loss analysis, competitor intel)
Tech Debt Quantification (velocity impact, stability)
Usage Analytics (PostHog, Amplitude, Pendo)
Surveys & Market Research (JTBD research, CSAT, competitive benchmarking)
2. Process the Raw Data
Raw data must then be processed effectively to extract meaningful insights. Instead of simply adding feedback to a long list of opportunities, we need a systematic approach to maximize its value.
The first step is to normalize input so it can be compared across different feedback sources (for more on this, see my approach to data normalization).
Next, feedback should be broken down into key components by answering:
What new insight statements can we record about the customer from this feedback?
What problems have been identified?
What potential opportunities arise from this feedback?
Which stakeholder is most impacted?
What is the overarching job the user is trying to accomplish in relation to this problem?
Each of these questions generates an insight, problem, opportunity statement, or idea.
3. Store Insights
Your statements then need to be documented and stored in an easily accessible database to avoid being fragmented across Slack, Notion, or Google Docs (slide 15 of the presentation shows the data entities that need to be created and the data relationships that need to be established for this framework to work). Any repository such as Jira, or Productboard, will do, but it’s important to correctly establish the relationships across statement types.
4. Deploy the Insights
Lastly, making high-quality decisions requires embedding data-driven insights into structured, recurring processes. These include:
Weekly/Biweekly Insight Reviews: What new insights have we gained about the customer?
Monthly-Bimonthly Product Landscape Reviews: What emerging patterns are we seeing across customer insights?
Quarterly Roadmap Reviews: Which problems should we prioritize based on evolving trends?
Weekly Epics/PRDs/Tests: Ensure each document clearly states, “What problem is this initiative solving?” to maintain alignment across teams.
The Bottom Line
If your team feels stuck under an ever-growing backlog, stop trying to clear it faster. Instead, start questioning why the backlog exists in the first place and whether the right things are being prioritized.
Reaching escape velocity isn’t about speed — it’s about decision quality. The more disciplined you are in how you gather, process, and act on product data, the less time you’ll waste on work that doesn’t matter, and the more time you’ll have for true product discovery.
Want to go deeper? Connect with me on LinkedIn, or check out my other thought leadership pieces on product discovery, decision-making, and B2B SaaS strategy.
