Data Quality Evaluation: A 6-Step Framework Anyone Can Use

Drowning in spreadsheets? Staring at dashboards that make you question if you’re looking at business metrics or random number generators? We’ve all been there. Making decisions based on data sounds great in theory, but when you’re faced with conflicting numbers, mysterious gaps, and trends that defy logic, it’s easy to feel lost.

This is where most people either throw up their hands in frustration or blindly trust whatever numbers look good. But there’s a better way forward.

Enter data quality evaluation: the process of checking how good, useful, and trustworthy your data is so you can make better decisions with it. It’s not about becoming a statistics wizard, it’s about having a practical approach to separate reliable insights from digital noise.

Let’s break down the data quality evaluation process step by step in a way that doesn’t make you want to shut your laptop and go for a long walk.

1. Start by figuring out what questions you actually care about

Before you even look at the numbers, take a minute to ask yourself: “What am I trying to learn from this?” It might seem basic, but it’s easy to skip over when you’re knee-deep in spreadsheet chaos.

Think of it like planning a road trip. You wouldn’t just jump in the car and start driving—you’d pick a destination first. Same idea here. Maybe you’re trying to figure out why sales dipped last quarter, or you’re hoping to spot trends in customer behavior over time. Whatever it is, get specific about your goal.

Let’s say you work at an e-commerce company and want to know if your latest promo actually boosted sales. That’s your guiding light. Knowing your question upfront helps you filter out the noise and focus your data evaluation on what matters.

2. See if the data makes sense for your goals

Once your goals are clear, the next step is making sure you’ve got the right data for the job.

Back to that promo example. If your goal is to see whether it increased sales, but the only data you have is delivery times and package weights from your shipping provider—yeah, that’s not going to cut it. You need actual sales numbers. Ideally broken down by time, location, and maybe even customer segment.

And don’t forget to check the date ranges. If the promo ran in March but your data only goes through February, you’re not getting the full picture.

This part of data evaluation is kind of like baking—make sure you have all the ingredients before you start. Nobody wants to be halfway through the batter and realize you’re out of eggs.

3. Check where the data came from and how it was collected

Alright, now that you know what you’re looking for, it’s time to figure out where the data actually came from. This matters because the quality of your data often depends on how it was collected.

If the numbers came straight from your internal systems—like Shopify, Salesforce, or Google Analytics—that’s usually a good sign. But if someone tells you they copied everything from an email thread… yeah, you might want to be a little more skeptical about its quality. Even small things, like time zones or how missing info is handled, can throw off your whole analysis.

As part of your data evaluation, do a quick background check before you start trusting the numbers. Better to catch the red flags now than get blindsided later.

4. Look for missing or weird values

Now it’s time to pull up your data and look for anything that seems off. Are there blank spots? Duplicate entries? Numbers that make zero sense?

If you’re scanning a customer database and see someone listed with an age of 347—congrats, you’ve found a data gremlin. Or maybe someone marked their country as “Mars.” Weird stuff happens more than you’d think, especially when data’s entered manually or merged from different systems.

These kinds of errors can mess up averages, skew trends, or just point you in the wrong direction. And they’re not random—they usually fall into what data folks call quality dimensions. Things like accuracy, completeness, and uniqueness. You don’t need to memorize all six (unless you’re into that), but keeping a few in mind can help you catch subtle problems that could mess with your results.

So do a little cleanup here. Tools like dbt, Great Expectations, or even a quick pass using Polars or PySpark can help flag these issues.

This stage of your data evaluation helps you make sure the information you’re working with won’t lead you astray. Once everything looks a bit tidier and less like a digital junk drawer, you’re ready to move on.

5. Test the data for consistency over time

Your data looks good now—but how does it hold up over time? Check for trends. Does the data behave normally from week to week, or month to month? This helps make sure you’re not missing weird anomalies.

For example, if traffic has been steady and suddenly spikes 10x in one day, that might not be your big viral moment—it could be a tracking glitch. Same goes for big drops. If revenue flatlines overnight, it might be a data issue, not a business emergency.

This step helps you make sure your data is stable and trustworthy. And while these checks can get a little complex, you don’t have to go it alone…

6. Bring in a data observability tool to make your life easier

So, you’ve made it this far. You’ve asked the right questions, checked your sources, cleaned things up, and looked for red flags. But let’s be honest—doing all this manually every time? Not fun. And definitely not scalable.

That’s where data observability platforms like Monte Carlo come in. They help you stay on top of your data by flagging when something’s off—like if data disappears, changes unexpectedly, or stops flowing altogether.

Instead of getting caught off guard in meetings, Monte Carlo helps you catch and fix issues before they become real problems. You can finally stop wondering, “Can I trust this report?” and just know your data is solid.

If you want to make data evaluation less stressful, and more reliable, it might be time to bring in some backup. Drop your email, get a demo, and see what a calm data life feels like.

Our promise: we will show you the product.