Data Hygiene and Cleaning – The Key to Accurate Analysis. Why Does It Matter in Business?

Proper data hygiene is the first step toward effective analysis and, ultimately, making accurate business decisions. But what exactly is data cleaning, how does the process work, and what benefits can businesses expect when they base their growth strategies on clean, reliable data?

What Is Data Cleaning?

Data cleaning is the process of correcting or removing inaccurate, incomplete, corrupted, improperly formatted, or duplicate data from datasets. Poor data leads to poor analysis, and as we’ve mentioned in previous articles, that results in poor business decisions—often with direct negative consequences for the company. That’s why data hygiene is foundational for reliable analysis.

The more data sources and the more data you manage, the more complex the cleaning process becomes. In startups or fast-growing companies, combining multiple data sources is essential to get a real picture of the business. While time-consuming, proper data hygiene and analysis are now essential—and well worth the effort.

Why Clean Data Matters in Business

So why is data cleaning and hygiene so crucial for companies? Simply put—it prevents the cost of bad decisions. In business, prevention is cheaper than correction.
Here’s why clean data pays off:

1. Lower costs, higher margins. Duplicate or messy data clutters your systems, leading to inefficiencies. Clean data enables better decisions, highlights hidden opportunities, and supports margin growth. For instance, outdated or noisy data can obscure demand for a new product or service.
2. Better targeting. Inaccurate data leads to wasted marketing and sales efforts. With clean, up-to-date customer data, businesses can reach the right audience and stop chasing dead ends. Automated cleaning tools help prioritize the most recent and relevant data.
3. Operational efficiency. Messy datasets slow down reporting and drain computing resources. Reports become heavy, slow, or unreadable—often filled with irrelevant historical data that adds no value and causes process delays.
4. Data integrity. Duplicated or manually entered records are a major source of reporting errors. Clean data helps avoid misleading statistics and ensures management is working with real insights—not flawed assumptions.
5. Protecting your BI investment. Business Intelligence tools are only as good as the data you feed them. Dirty data erodes trust in reports and wastes resources. In time, poor data can cost more to fix than starting from scratch.
6. Smarter decision-making. Being data-driven means you can rely on clean, trustworthy information to make faster and more informed decisions—giving you a competitive edge over slower-moving rivals.

How Does Data Cleaning Work?

Now that we’ve covered what data cleaning is and why it matters—how does it actually work? Imagine you’ve already aggregated your data from multiple sources into one central database.

Step one: remove irrelevant data. For example, if your customer data still includes unused fax numbers from years-old contact forms, don’t waste time cleaning it—just delete it.

Step two: eliminate duplicates. Let’s say a client contacted you through a form and by phone. Without deduplication, they’ll show up as two separate records, distorting key metrics like customer count or conversion rate.

Step three: standardize formats. The same phone number might appear as:

• 666 333 000
• 666333000
• +48 666 333 000

Without formatting consistency, you may treat the same person as three separate contacts. The same goes for typos, extra spaces, and casing—context matters more than spelling when cleaning data from different systems.

Step four: scale your values. Similar to standardization but focused on numeric formatting. Whether it’s 13 decimal places or inconsistent units (e.g., thousands vs. millions), scaling improves data readability and analysis.

Step five: handle missing or faulty data. Before running any reports, investigate gaps or anomalies and decide whether they matter for your analysis—or if they need correction or exclusion.

How to Maintain Clean Data

Of course, this is a simplified overview—depending on your business model, industry, or data volume, cleaning may require much more effort. Large corporations often build dedicated data teams. In fact, data scientists can spend up to 80% of their time cleaning data—not building models or producing insights.

For most SMEs and startups, a full-time analyst team may be overkill. That’s where outsourcing makes sense. If your business needs help with data, reporting, or analytics, get in touch with us—we’ll help you get clean data that delivers real business value.