Why Clean Data Is Often the Wrong Goal

A practical look at data quality, data cleaning, and why “fit for purpose” matters more than perfection

Most organisations say they want better data quality. What they usually mean is that they want fewer uncomfortable conversations about their data.

When reports look clean but decisions still stall, the instinct is to clean more. Remove more blanks. Tighten more rules. Standardise more fields. The assumption is that if the data looks right, confidence will follow.

It rarely does.

When organisations talk about data quality, they often default to data cleaning rules instead of defining what “good enough” actually means for the decisions they are trying to make.

Data quality sounds like a definitive term. Most people nod when it is mentioned, as if everyone agrees on what it means. In practice, it is one of the most abstract ideas in data work.

Ask ten people what quality data looks like and you will likely get ten different answers. Some will say it means no blanks. Others will say no errors. Some will say it means everything is consistent and neatly formatted. All of them sound reasonable. None of them are complete.

The uncomfortable truth is this. Data quality only feels clear until you are forced to define it.

Clean does not mean useful

A common assumption is that quality data is clean data. No missing values. No incorrect entries. No inconsistencies. On the surface, that sounds sensible. Who would want messy data?

But the moment you look closer, the cracks start to show. Clean according to what rule? And more importantly, clean for what purpose?

Removing blanks is easy. Deciding whether a blank actually matters is much harder. A dataset can be technically clean and still be useless for decision making. It can also be messy in places and still be perfectly adequate for the task at hand.

This is where many teams get stuck. They optimise for cleanliness without first asking what the data is meant to support.

Defining acceptable before chasing perfect

Data quality becomes meaningful only when we define what is acceptable. That usually starts with deciding which fields are mandatory and which are optional. Yet even this simple step is often skipped or rushed.

Mandatory fields sound straightforward, but they hide a judgement call. Mandatory for what outcome?

If the objective is high level reporting or trend analysis, line level completeness may not matter much. A few missing values rarely change the overall picture. In contrast, if the goal is operational follow up or customer contact, those same missing values suddenly become critical.

Blindly enforcing completeness can even make data worse. People fill in placeholders just to satisfy a rule. Blanks disappear, but accuracy quietly suffers.

Quality is not about forcing everything to be filled. It is about knowing which gaps are acceptable and which ones are not.

Fit for purpose changes everything

One useful way to think about data quality is to stop treating it as a property of the dataset and start treating it as a property of the use case.

The same data can be high quality in one situation and poor quality in another.

This shows up clearly in meetings. A dashboard looks polished. Numbers reconcile. No obvious errors appear. Yet someone hesitates and asks whether the figures can really be trusted. The room goes quiet. The decision is deferred, not because the data is wrong, but because no one is sure whether it is right enough.

Consider consolidated summaries for management. Small inaccuracies at the individual record level often have little impact on the final insight. What matters more is consistency and directional accuracy.

Now consider contacting customers directly. Line level precision becomes non negotiable. A wrong email address or phone number is not a minor issue. It breaks the entire process.

Even then, not all details carry equal weight. If communication happens mainly through email or mobile, a physical address may be optional. Insisting on it adds friction without adding value.

Quality is not absolute. It is contextual.

How accurate is accurate enough

Accuracy is often treated as a binary concept. Data is either correct or incorrect. In reality, accuracy sits on a spectrum.

For trend analysis, being approximately right is often sufficient. The goal is to understand direction and magnitude, not perfection. Minor errors rarely change the conclusion.

For financial transactions, billing, or compliance reporting, tolerance drops sharply. Small errors can have real consequences.

This is not a technical decision. It is a business one. The acceptable level of accuracy depends on risk, not preference.

Many data cleaning debates stall because this question is never addressed explicitly. Teams argue about rules when they should be discussing consequences.

Not all fields matter equally

Another quiet assumption in data work is that every field deserves equal attention. In practice, this is rarely true.

Some fields drive decisions. Others exist mainly for reference. Treating them the same wastes time and effort.

Customer name, email, and contact number may be critical for outreach. Secondary demographic details may not be. Collecting everything just in case feels safe, but it often leads to bloated datasets and lower overall reliability.

Relevance matters more than completeness. Knowing what you can ignore is just as important as knowing what you must fix.

Asking better questions before cleaning

Before touching the data, it helps to pause and ask a few simple questions.

What decision or action will this data support? Which fields are essential for that outcome? What level of accuracy is acceptable? What happens if this data is wrong or missing?

These questions sound basic, but they are often skipped. When they are answered clearly, data cleaning becomes focused and purposeful rather than mechanical.

Why data cleaning is harder than it looks

This is why data cleaning is not just about tools or steps. It is about judgement. Most people are taught how to apply rules, but not how to decide which rules matter.

This gap is where many problems originate. Teams clean diligently and still struggle to trust their outputs. The issue is not effort. It is intent.

This is also why structured training in data cleaning and processing focuses less on removing errors and more on understanding context, trade offs, and consequences. Tools change. Judgement does not.

Stepping back from the chase for perfection

Most teams struggling with data quality are not careless or unskilled. They are usually doing exactly what they have been told to do. Remove blanks. Fix errors. Standardise formats. Tick the boxes.

The frustration sets in when all that effort still does not lead to better decisions or stronger confidence in the output. Reports are technically clean, yet conversations remain hesitant. Trust does not improve, even though the data looks better on paper.

The problem is not effort. It is definition. When quality is never clearly tied to purpose, teams end up optimising the wrong things.

Quality data is fit for purpose

Chasing perfect data is expensive and often unnecessary. Defining quality clearly is cheaper, faster, and far more effective.

Quality data is not flawless data. It is data that is accurate, complete, and detailed enough for the decisions it is meant to support, and no more than that.

Once quality is defined in those terms, data cleaning stops being a frustrating chore and starts becoming a strategic activity.