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Oh, not this cr*p again!

When it comes to data products, a lot of data people are in the habit of being told exactly what to build, and building exactly that.

We pour hours and hours of effort into it, only to find out that the so called “urgent” report/dashboards/data pull is hardly used at all.

Frustrating, right?

But before jumping to conclusions, it's worth asking: why is this happening in the first place?

Understanding why engagement is low isn’t just about tracking usage logs and jumping down the throats of people not using them (however fun that sounds), it’s about ensuring the things we build are actually valuable to the business.

More importantly, it's about reclaiming our time and effort for initiatives that are truly worth our attention and effort. If we don’t address this, data teams will continue investing energy into products that don’t get adopted and continue to be seen as a cost centre (and the first on chopping block for layoffs!)

Let me know your thoughts in this week’s poll 👇🏻

_{(check below for last week’s poll results)}

💥 The real impact of building unused data products

🚨 Wasted time and resources

When a dashboard or data tool goes unused, that’s hours of development, stakeholder discussions, and maintenance effort wasted. Time that could have been spent on high-impact projects instead gets sunk into something that adds little value.

📉 Diminished credibility for data teams

If people aren’t using the tools we build, leadership may start to question whether the data team is aligned with business needs. Over time, this erodes trust and makes it harder to advocate for future initiatives. It’s also a sign of potential hard times on the horizon for data folks.

⚡ Burnout from constant rework

Nothing is more frustrating than being asked to redo a dashboard (or scrap it altogether) after investing significant effort. Without clear evidence to push back, data teams get stuck in an endless cycle of reactive work.

🔄 Lack of strategic influence

When data teams don’t have leverage to challenge low-value requests, they become order-takers instead of strategic partners. This limits their ability to drive real business impact. This is not a fun place to be for most.

🛠️ Here’s how we start to fix It

The key to solving this isn’t just tracking (and reporting) who’s not using dashboards, it’s using that data to make better decisions about what we build in the first place.

1️⃣ Question the need before building
Just because someone asks for a dashboard doesn’t mean it should be built. Most of the time, it probably shouldn’t. In these situations, data teams need to probe deeper:

• What problem is this solving?

• How will success be measured?

• Is there an existing solution that already meets the need?

2️⃣ Use usage data to push back
Tracking engagement isn’t about blaming people, it’s about protecting your time. If you can show that similar dashboards have gone unused in the past, you have the leverage to challenge new requests and suggest alternatives. Remember - you’re a data person, use data to make your case.

3️⃣ Make adoption easier
If engagement is low, consider:

• Did users get proper training on how to use it?

• Is the dashboard intuitive and answering the right questions?

• Are there barriers (e.g., poor access, too complex) that prevent adoption?

4️⃣ Align with business priorities
Data teams that work closely with business leaders can proactively identify high-impact needs instead of reacting to low-value requests.

5️⃣ Adopt an agile product mindset
Instead of treating dashboards as one-time projects, iterate based on user feedback. Start with a minimum viable product (MVP), collect insights on how users engage with it, and refine until you achieve high adoption and real business impact.

By taking these steps, you can ensure that the work your team does is impactful, strategic, and valued, rather than just another unused tool in the digital graveyard.

Which, by the way, is where most dashboards belong.

🪦 

⚡️ Previous poll results

Last week we talked about data teams being last in line for budget considerations. In the poll I asked you: What’s the biggest reason your data team struggles to get budget?

Here’s how you responded:

Tristan Burns

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