Let me start with a quick reality check. How many dashboards or models does your organization have? Hundreds? Thousands? And how many of them are actually used? If the answer is “not many,” you’re facing the same challenge a lot of data teams are experiencing today.
I’ve seen this so often: teams drowning in a sea of data products that nobody uses, while stakeholders keep asking for more.
This is what we call the data product death trap (also known as the data death cycle, the tech trap, the project trap, etc. – whatever you name it, the principle stays the same): a state where data teams spend most of their time maintaining old, underutilized products and barely have any capacity left to work on new initiatives.
Sound familiar?
More data products don’t mean better
At first, it feels like progress. Your team delivers dashboard after dashboard, model after model. If you get a nod of approval from your business stakeholders, you move on to the next request. But here’s the catch: no one stops to ask whether these products are still useful—or if they were truly needed in the first place.
I once spoke to a data leader who told me their company had 1,000 employees and 3,000 dashboards. My first reaction? “How does that even happen?” Their team had reached the point where they were spending all their time updating dashboards, with no time left to deliver new projects.
What basically happens is if you never discontinue things as part of the product lifecycle, you always eat up more of your foundational baseline of what your team can actually deliver. Until you have so little capacity that you’re only maintaining what you have. And you slowly create no value over time, while the cost stays the same.
The root of the problem is that often data products are approached with a “more is better” mindset. Every stakeholder request is “urgent”, so you keep building and adding to the portfolio. But when everything becomes a priority, nothing truly drives impact. We all know how demotivating it is to pour weeks into building something that nobody uses. Not to mention the wasted investment.
When teams fall into this trap, there are consequences:
- Wasted resources: Time and money are spent maintaining products that provide little to no value.
- Stalled innovation: With no capacity to explore new opportunities, the team becomes reactive rather than proactive.
- Eroded trust: Stakeholders start to question the value of the data team when they see little impact from all the effort.
The solution? Focus on the data products that matter
Escaping the data product death trap doesn’t happen overnight, but it starts with a shift in mindset: less is more. It’s about shifting the focus from building more data products to building fewer, better products. Here’s how you can get there:
- Audit your portfolio (and be honest about it)
Start by taking a hard look at your existing AI & data products. Which dashboards, models, or pipelines are actually being used? Which ones drive business impact? Be honest about what’s adding value and what’s just clutter creating chaos. If something isn’t being used, consider discontinuing it. Want my suggestion? Shut them all off and see who’s calling you first. You’ll find out very quickly what’s no longer needed 😉
- Think like a product manager
Data products aren’t set-it-and-forget-it solutions—they have lifecycles. Some might need regular updates, while others eventually outlive their purpose. Treating your portfolio like a product catalog helps you focus on maintaining what matters and letting go of what doesn’t. And yes, that means sometimes having tough conversations about retiring legacy products.
- Get aligned on business goals
One of the biggest reasons teams fall into the data product death trap is a lack of alignment with business priorities. Before jumping into “build mode,” ask yourself: does this align with our business priorities? Do we understand the value this product will deliver? Often, the loudest stakeholder doesn’t represent the most important need. Push for clarity and alignment early—your future self will thank you.
Tip: Need help with consolidating data product requests, aligning them to business priorities, and prioritizing them? Delight was built for you.
Overcoming common challenges
Shifting from a “more is better” mindset to a quality-first approach isn’t easy. There are some common challenges you might face:
- Stakeholder pushback
Stakeholders often resist the idea of retiring products, even if they’re unused. The key is communication. Explain the trade-offs: maintaining low-value products limits your ability to deliver new, impactful solutions.
- Team resistance
Your team might be attached to the products they’ve built—it’s human nature. Encourage a culture of iteration and improvement. Remind them that retiring a product doesn’t mean failure. It just means making room for better opportunities.
- Lack of metrics
If you don’t have data on product usage or impact, collect it. Usage statistics, business outcomes, and stakeholder feedback are really valuable for making data-driven decisions.
Reclaiming the space to innovate
When you stop trying to do everything, you create room to do the right things. By streamlining your portfolio, you free up capacity to focus on proactive discovery, explore new ideas, and build products that actually move the needle.
This approach also changes how your business sees you. Instead of being a reactive service team, your data organization becomes a strategic partner, actively contributing to business success.
We took a deeper look into this in our recent white paper on Maximizing Business Impact through Opportunity Management.
Break free from the data product death trap
Breaking free from the data product death trap isn’t just about saying “no” more often—it’s about saying “yes” to the right things. Yes, it’s tough to shut down products you’ve invested time and effort into. But the alternative is worse: being stuck in maintenance mode forever, with no room to innovate or deliver impact.
By focusing on fewer, better quality-data products, you can reclaim your capacity, rebuild stakeholder trust, and create a culture where you’re prioritizing impact. Remember, less isn’t just more—it’s the smarter, more sustainable path forward.
Watch my full discussion in our AI Impact Live where I covered this and more or feel free to reach out!
