From BI Reporting Need to a Classic Build-vs-Buy Dilemma

We needed reports. The choice? It came down to investing in Power BI or building our own dashboard from scratch.

It’s a pattern I keep running into, and it always boils down to the build vs buy decision framework. Not just “Can we build this?” but “Should we build this?” That’s the real fork in the road, every time.

And really, this isn’t just about dashboards. If you write code, buy services, or deploy any tech, you’re in the same boat—just swap out the specifics.

The Practical Build vs Buy Decision Framework: Differentiation First

The first thing I do—before any requirements doc or vendor demo—is classify the capability we’re considering within a build vs buy framework. Does it actually differentiate our product, or is it just the technical heavy lifting every team faces? Six months ago, I was certain that our custom reporting engine was “strategic.” In hindsight, it really just tied up two backend folks for maintenance nights. Most of the time, it’s easier, faster, and cheaper to buy products that handle undifferentiated heavy lifting—like compute or storage—rather than reinvent those wheels (InfoWorld). To be honest, it took me a while to admit not every shiny capability moves the needle for our users. When I finally did, it freed us to focus on what matters.

I use a simple scoring approach. Not to chase false precision, just to structure the conversation and make tradeoffs visible. I’ll admit, I worried scoring would miss context. It didn’t—it surfaced exactly where priorities clash, forcing us to talk through assumptions we’d normally gloss over.

So, when do I actually recommend building? Only when deep customization truly strengthens our differentiation, or owning the capability lowers our long-term costs. Think about areas like auth, billing, or analytics—owning infrastructure led Dropbox to double its gross margins in two years, thanks to optimization and revenue growth (a16z). If your edge is real and the math pencils out over time, that’s the signal to build.

And if we’re looking at undifferentiated heavy lifting—those unglamorous foundations everyone needs—I default to buying. Speed and sustainability always win here. But I warn my team: resist the urge to over-customize. The Frankenstein Effect is real. You “buy” a tool but hack in so much customization that it’s neither truly bought nor built. Now, you own a monster, and you’re the only one who can feed it.

What I’m after is simple. Clarity in how we decide, and freedom from the slow drag of maintenance or regret. If you use the framework—classify, weigh, score—you can move faster and focus on what makes your team unique.

Running the Criteria: How the BI Choice Played Out

Let’s look at the actual moment we sized up Power BI against just spinning up our own reporting app. On paper, buying meant a clear upfront license cost and time spent learning the tool. But honestly, the big draw was integration. Power BI connected straight into our data pipelines, which let non-dev teammates build and tweak reports without waiting on us to code another chart. I’ll admit, that’s not my instinct (I like control), but the value in handing off reporting to the broader team was hard to ignore. The real cost wasn’t just the money, it was in letting go of being the dashboard bottleneck.

Contrast that with the build path. Our in-house app could be spun up in weeks—local tools, familiar stack, full flexibility. Very tempting. At first, it sounded faster—no procurement, no waiting. But once I started sketching it out, the long tail showed up immediately. Who was going to handle hosting, keep up with security updates, fix layout bugs every time the browser changed? Plus, it would only serve a single project at first, so the pressure to refactor for reuse was inevitable. Those are the steps we underestimate (“Just a quick fix!”), until we’re drowning in them.

So how did the criteria stack up? If you focus on time to value, Power BI easily won. The team could generate reports in days, not sprints, and business owners didn’t need a developer for every new KPI. Broader access meant sales, ops, and marketing could self-serve—no more report requests piling up. With build, yes, we had complete control: any source, any visualization, our own permission model. That’s tempting if you know your needs won’t change. But the maintenance load, upgrades, and support would snowball.

At one point I caught myself thinking, “Well, if we ever need to change directions, will this app keep up?” Vendor lock-in lurked—Power BI’s roadmap wasn’t ours, and if Microsoft decided to drop a feature or triple a price, we were stuck. But building was its own trap: we’d own the code, but we’d also own every bug, every outage, every time someone wanted a new metric. I’ve felt both kinds of regret—first when buyers realize the vendor owns the roadmap and not them, and second when builders can’t move fast because their codebase is glued to last year’s requirements.

The messiest risk with building lives in the people. When the engineer who designed the data join leaves, who’s left to figure out why the monthly report hiccups every third Tuesday? This happened to us on a past project—turnover made our custom reporting brittle, and no one could remember how it worked or why. Two years out, our knowledge had evaporated. Building isn’t just about code—it’s about memory, and memory fades fast.

Here’s where my brain always drifts: sometimes, you just need a reliable appliance now. The goal is to cook dinner, not invent a new stove. I caught myself midway through joking, “Am I optimizing for the meal or the oven?”—and honestly, in a fast-moving team, the meal almost always wins. The urge to perfect the platform is strong, but shipping the outcome matters more.

This wasn’t a paint-by-numbers decision and I won’t pretend the framework answers everything. What shifted it for us—laying out differentiation, time-to-value, total cost, and lock-in—wasn’t about being clever, just about being clear. If you run this comparison on something you built or bought, you’ll spot criteria you glossed over at the start. That’s where real improvement happens—no more buyers’ remorse, and no more lost weekends trying to remember how a long-departed teammate wired up the data layer.

The Technical Decision Playbook

Let’s make this repeatable. Every durable build-vs-buy decision in our group follows the same playbook. No skipping steps because it’s “obvious” or we’re “busy.” First, classify the capability: is this core to what makes us special (differentiation), or is it standard plumbing every team needs? Next, lay out criteria—the ones that matter for us right now: time-to-value, total cost (including the hidden long-tail stuff), maintenance burden, security and compliance, vendor roadmap, and lock-in. Score each, not for perfection, but to surface tension.

Then, pressure-test assumptions together—are we assuming our needs won’t change or our skills will hold up? After that, run a pilot: take one small slice and prove it in practice. Finally, document the entire choice in a shared team decision log. No mystery, no memory holes—just clarity anyone can revisit.

Buying guardrails matter. Resist the itch to fit a bought tool to every corner of your workflow. Keep customizations sparse and strategic. The less you bend, the more you gain from speed and managed upgrades. I’ve watched good tools become maintenance nightmares when we tried to make them everything. Just because you can add ten plug-ins doesn’t mean you should.

Building needs its own lines in the sand. Commit to onboarding plans, upfront documentation, a pass at security, and a real strategy for who owns the code when your first owner moves teams. Don’t ignore revisit points—a “pause and check” moment on Day 4 of discovery can save you weeks chasing cleverness instead of value. Give your team explicit permission to say, “Are we drifting? Should we reassess?”

Decision hygiene makes all the difference. Write down why you scored things this way, what you’re assuming about future needs, and exactly how this ties to your product strategy. Share it out, even if the doc feels rough. Future teams will thank you for not repeating the same half-memory debate. I’ll admit, I used to skip this step thinking I’d remember it all. I never did. Leaving a paper trail doesn’t just protect you—it’s a callback out of future confusion.

This isn’t about building a bureaucracy. It’s about saving your time and sanity. When teams frame choices clearly, the endless back-and-forth shrinks, and momentum builds. Take the playbook, tweak it for your team, and commit. Next time the build-vs-buy question floats up, you’ll have a process that protects focus—and leaves time for actual engineering instead of debate recycling.

Fast Retrospective: Turn Theory Into Next Steps

Before the week is over, pick one build vs buy software decision your team made—maybe it was last quarter, maybe last year—and give it a quick once-over. Did it actually hold up over time, or do you look back and wish you’d gone the other way? Trust me, ten minutes spent this Thursday beats another year patching a choice you wish you could unmake.

I know the resistance. “Do we really have time for this?” and “What if revisiting the score just tells us what we already know?” Or maybe you hesitate, thinking you’ll lose control if you stick too closely to a framework. I used to worry the process was just red tape—that scoring would water down real context. But the truth is, it did the opposite for us. Structure gave us back control. Once we spent less energy defending history and more on mapping where things stood, the path forward got clearer. In practice, framing cuts down the back-and-forth cycle, which stabilizes iteration and moves decisions out of limbo. The faster you surface blind spots, the quicker you stop repeating the same lessons.

Here’s what I keep telling myself (and my team). The discipline is simple. Protect what makes you different, dodge unnecessary drag, and every time the question comes up, switch from “Can we?” to “Should we?”—then pick speed or ownership on purpose. That’s focus, not paperwork.

I’d like to say I fully trust the frameworks every time, but if I’m honest, I still second-guess some decisions as needs shift—especially when success means letting go of control. I haven’t figured out how much is too much customization before we risk slipping back into the Frankenstein Effect. Maybe next quarter I’ll know. For now, I’d rather keep moving and revisit it than get stuck chasing a perfect answer that never arrives.