I just completed my first BI Sprint Series project, and one of the things that stood out was how scope creep can sneak in and reshape a data project. The Subscription Churn project started as a confident step into data storytelling, but ended as a quiet masterclass in adapting to reality. I learned that scope creep isn’t always a villain; sometimes, it’s a signal that you’re learning. This post is a reflection on how my well-intentioned plan got revised (and revised again), and why boundaries matter just as much as curiosity when building portfolio projects.
The Plan vs. Reality
In the beginning, the Subscription Churn project felt exciting. I had a clear roadmap that included five sharp business questions and a simulated dataset spanning five years. The idea was to dive deep: analyze churn by engagement, industry, and other behavioral metrics. But then reality kicked in.
For starters, the simulated data I was working with proved a bit too “heavy” for my laptop to handle.I had to truncate the data from a five-year range to 18 months. Of course, this shifted the scope of my analysis.
Additionally, I worked with a simulated dataset. And while I aimed to make it mirror real-world business scenarios, you can best believe it wasn’t perfect. Some issues were entirely not fixable, which meant further truncating the dataset. For instance, I created a Location table and forgot to give it a way to connect to other tables. Also, some sign-up dates didn’t make sense considering the transaction and coverage end dates. Think a customer’s signup date being 27/07/2024 but one of the transaction dates being 13/8/2023 or the coverage end date being13/11/2023. Fishy, right?!
My original vision also involved modeling churn reasons based on factors like user engagement, location, industry, etc. However, the lack of granular data on these factors forced me to re-evaluate things.
And don’t get me started on the issue of subscription-level vs. customer-level churn in SAAS. Really, those who have worked in SAAS, what do you focus more on?
I ended up focusing on customer-level and went from five business questions to three in the final report. Which consequently means that some of the metrics I hoped to work with didn’t make the final cut.
I went from chasing completeness to prioritizing clarity. In hindsight, the reality check was a nudge toward focusing on what matters most in a portfolio: storytelling, sound logic, and manageable complexity.
How Scope Creep Shows Up
Scope creep doesn’t arrive with a neon sign. It’s often subtle and well-intentioned, disguising itself as “just one more improvement.” But if you know what to look for, you’ll start noticing it early. Here are a few ways it tends to sneak into your data projects:
1. Chasing Perfection
It’s tempting to keep refining your dashboard, adding new metrics, or finding the “perfect visualization. Whether you’re trying to impress recruiters or clients, it’s easy to keep making endless improvements.
The problem with perfection is that it often comes at the cost of clarity, deadlines, and your own bandwidth. What starts as a tight, purposeful analysis can balloon into a sprawling project with no clear end. Constant tweaking under the guise of “improving” can quietly derail progress.
2. Domain Ambiguity
Some concepts in data analytics seem straightforward until it’s time to define them. Take “profit,” for example. In SaaS, it might involve recurring revenue and churn-adjusted margins, while in FMCG, it could lean heavily on COGS and sales volume.
Without clear, shared definitions from the start, your analysis can spiral as stakeholders ask for changes based on their own interpretations. You end up reworking dashboards, tweaking metrics, and chasing alignment. The more ambiguous the domain language, the easier it is for your project to quietly expand beyond the original scope.
3. The Curiosity Spiral
You notice one strange spike in engagement, and before you know it, you’re knee-deep in segmenting user behavior that was never part of the original brief. Curiosity is a strength in data analytics, but without boundaries, it can derail your project fast.
A quick check becomes a full-blown analysis, complete with new tables, experimental queries, and extra visualizations. And what makes it worse is that all this driven by questions no stakeholder actually asked. These side questions can lead to interesting insights, but don’t forget to stay focused on the defined objective.
4. Endless Revisions
Reverting to the issue of perfection, can we talk about that tendency to tweak things? The project is already done, but you tell yourself this chart needs “fixing.” You change colors, test new filter logic, add info panels. It starts as UX polishing but quickly becomes a quest for aesthetic perfection.
These changes may feel small, even helpful, but they often signal a deeper reluctance to let go. Done is rarely perfect in analytics, and endless revisions can quickly burn time and energy without adding any real value. So next time you find yourself making improvements that aren’t a response to an actual need, call it. Done is done!
5. It’ll Just Take a Minute
You find a small issue in your model, perhaps a formatting thing or a missing condition. You go in to fix it, and three hours later, you’re refactoring the entire DAX logic for a marginal gain.
It starts with good intentions, but these fixes often balloon into time-consuming overhauls that were never part of the original scope. When you’re optimizing just for the sake of it, or chasing an invisible standard, it’s no longer maintenance.
Sometimes, the best approach is to fix the bug and walk away.
Lessons From the Scope Creep Spiral
Scope creep isn’t inherently bad. It’s often a sign of learning in action. But left unchecked, it can turn a focused project into a tangled mess. My Subscription Churn project taught me that the difference between productive and chaotic comes down to how you respond to scope shifts.
Here are three lessons I’ll carry into future projects:
1. Clarity Over Completeness
Instead of trying to solve every facet of a problem at once, focus on defining precise, actionable questions that can be addressed with existing data or data that can be realistically acquired within the project’s scope. This disciplined approach allows you to make tangible progress on the core issue, delivering value incrementally. It prevents projects from becoming bogged down in an endless quest for comprehensive data, ensuring that efforts are concentrated on delivering a minimum viable solution that genuinely moves the needle for the business.
2. “Done” Beats “Perfect”
In many projects, especially those involving analysis or problem-solving, there’s a temptation to continuously refine and polish the output, striving for an elusive state of perfection. But the best metric for true completion isn’t perfection. It’s whether the analysis or solution effectively meets the core objective and enables stakeholders to take meaningful action.
3. “Is This Enough?” Is the Wrong Question
You’ll always wonder if your work is “enough,” and that’s normal. But “enough” isn’t about volume; it’s about impact. A concise, well-reasoned analysis beats a bloated one every time. Scope creep often starts with anxiety, not necessity.
Key reframe: Instead of asking, “Could I do more?” ask, “Does this answer the core question clearly?” If the answer is yes, consider it complete and move forward.
The Power of Project Boundaries
The Subscription Churn project was far from perfect. But I certainly learned a lot about having a well-defined scope for your project. A well-scoped project allows the reader to follow your logic, understand your constraints, and engage with your insights. It’s easier for someone to say, “I trust this analysis,” when the scope makes sense and the decisions are documented.
So before you embark on your next data project, set scope guidelines. Define what success looks like before you fall in love with a dozen new ideas. You’ll thank yourself when presenting, and so will your audience.