5 Mistakes That Make Your Data Projects Crash and Burn

Aug 23, 2025

Parents get the greatest satisfaction when they watch the child they brought into the world grow up to become a responsible citizen. If you think about it, raising a child is a project, and what the child becomes is a direct result of that project.

Well, parents aren't the only ones with projects. Data analysts have their projects too. Even though these projects are not at the scale of raising a child, data analysts feel a sense of pride when their project recommendations are adopted by the stakeholders. Watching these projects' implementations take shape gives these analysts that proud parent-like feeling.

Data analysis projects hold immense potential to drive decisions and solve real-world problems. Yet, many projects never make it past the planning stage or fizzle out before delivering value. It's like a child who never lives up to their potential. Why do these initiatives end up on the chopping block? In this article, I want to look at five common reasons why your projects fizzle out before delivering value and what you can do to ensure your project thrives and lives.

1. Your Project is Drowning in Data, Starving for Insight

My colleague once sent a very detailed email to one of the heads in another department. Since I was part of the team, I got cc’d. What shocked me was not the email, but the reply he got. The head simply wrote back, "So what is your point?" Yes, in all that email mumbling, my colleague failed to highlight the actual purpose. He marked the target but missed the shot.

This is what happens in most projects. With the intention of impressing the audience with how much you know, you create a report with 50 charts, 10 pivot tables, and 30 slides. But the decision-makers still ask, "So what?" In all that mess, they cannot find the point of your project. More does not always equate to value. The more you share, the more you lose your audience.

How do you solve this problem? Cut ruthlessly. Show only what drives the story forward. One strong chart with a clear insight beats a dashboard jungle.

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2. You Answer Questions Nobody Asked

If you have ever taken a car to a mechanic, you probably know that most mechanics like to 'search for monsters to destroy.' You take your car for one thing, and they give you a list of 10 other things that are wrong with your car. For most mechanics, the intention is always sincere. They are trying to save you from a lot of trouble down the road. However, the irritating part is that nobody asked them to do that.

When you are working on a project, it's easy to get sidetracked by the 'other' things that you find in the data that may not align with the original goal of the project. Like a zealous mechanic, you may feel obligated to highlight such findings in your report. The problem is that you will end up answering questions that nobody asked or answering questions that the company is already aware of. You end up with a bloated report answering questions that the stakeholders do not actually care about. You may be solving a problem, but it’s not their problem.

Solution: Stick to the original purpose of your analysis. Do not go searching for other monsters to destroy. Ask yourself, "What decisions will this analysis support?" Once you answer that, ensure that your report is aligned accordingly. If you absolutely must mention it, tuck it away in a footnote. Keep the main story clean, sharp, and focused.

3. Your Story Has No Punchline

I love stand-up comedy. In stand-up comedy, good jokes are like planes; they take off, and then they land. The landing is known as a punchline. The most horrible jokes just take off and leave the audience hanging. They never land.

In data analysis, you must have a story. Why? Because numbers alone don't stick; humans remember narratives. Your story must have a hook or a punchline. Yes, it should always land. Without a story, you may have solid analysis, but your audience won't remember it or care about it.

Build a narrative around your analysis. Start with the problem, show the tension in the data, and end with the insight that matters. Your project becomes more than numbers; it becomes a story your audience can grasp and act upon.

Here is an example. Let's say you are working on a report to analyze a summer promotion campaign for a company. Instead of just showing the raw numbers:

"Our summer campaign increased revenue by 86% in June compared to April."

You can build a story:

Set the scene (problem/goal):

"Back in May, our summer promotion was about to launch. We needed to see if it would actually move the needle on revenue, or just be another campaign that cost money with little impact."

2. Show the data (tension):

"We tracked revenue from April to August. April revenue was $140k. By June, revenue jumped to $260k. An 86% increase. July and August stayed high compared to pre-promo levels."

3. Deliver the punchline (insight & action):

“This jump proves the campaign worked and gives us a clear next step: repeat it next year, focusing even more on high-performing channels to maximize ROI."

4. Your Visualizations are Vanilla

Just like long emails can leave people searching for the point of the email, a poorly done visualization can leave people searching for the purpose of the graph in the first place.

Let's say you are working on a report to analyze a summer promotion campaign for a company. Here is how a poorly created report would look:

Look at this report. Sure, it’s technically correct. But ask yourself: Where’s the story? Where’s the punchline?

All bars are the same color; nothing stands out.
No annotations; the audience has to calculate the summer jump themselves.
The title is generic; "Summer Promotion" doesn’t tell the audience why this chart matters.

A vanilla chart like this forces your stakeholders to do the thinking for you, and that’s exactly why your project could get ignored.

Here is a better chart:

What makes this storytelling chart better:

May (green): biggest increase from April (start of promo spike).
June is orange; it immediately draws the eye. Highest revenue overall (peak of promo)
Other summer months are blue: context without distraction.
Annotation shows the insight directly: no mental math required.

5. You Try to Be a Smartass

Jay-Z once famously said, "I dumb down for my audience and double my dollars." His point was that it makes no sense to try to sound smart to an audience that won't even understand you. To be understood and appreciated, you have to bring yourself down to the level of your audience.

We know you are smart and that you went to a fancy school. However, it makes no sense to present a slide full of R-squared values, p-values, confidence intervals, and regression coefficients to an audience that won't understand it. It’s not impressive. To the audience, it’s gibberish. Don't let your insights get buried under a mountain of jargon. Remember, if decision-makers don’t understand your findings, they don’t trust them, and suddenly your project is back on the chopping block.

Here is how a smartass would try to report a summer promotion data finding:

"The linear regression model indicates a statistically significant increase in revenue during the summer campaign, p < 0.05, with an 86% effect size compared to April."

Sounds smart, but most likely nobody heard you. Solution: Dumb it down. Speak human. Translate the stats into plain language and actionable insight:

"Revenue jumped 86% from April to June thanks to the summer promotion. That proves the campaign worked and we should repeat it next year, focusing on the high-performing channels."

See the difference? One version impresses with technical terms. The other informs and drives action. Keep your language simple, focus on the insight, and your project will survive, not just the chopping board but also the meeting room.

Wrap-Up

Data analysis projects don’t die because of bad math or because the people behind them are not smart enough. They die because they fail to connect with the audience and drive decisions. If you want your projects to survive the chopping block, remember these principles: