Yes, you do data – but what does your data do to you?

How concerned would you be if 80% of your customer data proved to be unusable? Would you let it be, or take action to fix the situation? In this blog, we’ll explore why you should take action, what happens if you don’t, and what practical steps you can take to improve your data quality.

The hidden cost of “just doing data”

At first, 80% might sound like an exaggerated number, but it’s not. In one real B2C data sample we analyzed, only about 24% of the data passed even basic quality checks. And meeting those checks doesn’t automatically mean the data is good.

Today, collecting and storing data is easier (and cheaper) than ever. As a result, organizations often gather data simply because that’s what everyone does. But without intention, even the most advanced data platform, or the simplest Excel sheet, becomes a digital junkyard.

Problems usually surface only when the data is actually needed; say, for marketing, reporting, or operational decisions. That’s when many realize that “doing data” isn’t enough.

Why you should care

At the very least, bad data means wasted money. You pay to collect, process, and store data that doesn’t deliver value. But the consequences go much deeper than cost.

This brings us to Master Data Management (MDM), the foundation of any data-driven organization. Master data covers your core business entities: customers, employees, products, services, locations, and cost centers.

Because master data is relatively static and reused across many processes, poor-quality data can quietly wreak havoc:

• Marketing campaigns fail to reach the right audience
• Shipments go to the wrong places
• Reports mislead decision-makers.

So, what are the downstream effects? Lower sales, frustrated customers and employees, inefficient operations, and wasted investments in AI and analytics built on unreliable foundations.

Compliance needs to be considered too, especially GDPR. Poor-quality customer data can make it nearly impossible to meet legal obligations around consent, deletion, and transparency. You can’t manage what you can’t trust.

Where to begin: fixing or preventing?

The instinctive reaction is to fix bad data. But unless you also address the root causes, you’ll be cleaning again soon. Sustainable improvement comes from prevention, and prevention means focusing on three dimensions: people, processes, and tools.

Let’s explore these from the customer data perspective.

1. Systems: know where your data lives

Start by identifying where customer data exists and how it flows between systems. Your data might live in:

• Excel files
• CRM or ERP systems
• Dedicated Master Data Management platforms.

Any of these can work if the surrounding processes and people do. Once you’ve mapped your systems, assess your data quality by reviewing integrity (are the values accurate?) and completeness (do you even have all the necessary data points?).

2. Processes: form follows function

Next, map your data processes; how data is created, used, modified, and deleted.

The processes that generate data determine its quality. The processes that use data define what’s needed and acceptable. For example, sending newsletters requires far less data than managing insurance policies.

Improving these processes might mean:

• Enhancing data models or creating new ones
• Defining and enforcing data quality rules
• Setting up validation and enrichment procedures
• Implementing match-and-merge logic.

Your tools may set limitations, so iteration and adjustment are often necessary.

3. People: the true owners of data quality

Finally, let’s talk about the most critical element: people.

People define and run business processes, build and use tools, and create and consume data. In B2C contexts, they’re also the subjects of that data.

Data quality starts with motivation. Even the best systems can’t fully automate good data habits. If people see validation rules as tedious obstacles, quality will suffer.

To motivate your employees, emphasize the why: show them how their data entries impact the entire business, from marketing to customer satisfaction. Education, transparency, and feedback loops go a long way.

And when it comes to customers, offer value in exchange for accurate information. Loyalty programs, giveaways, or personalized experiences can encourage customers to share their data willingly and correctly.

Turning awareness into action

Bad data hurts your systems, which shapes your decisions, influences your customer relationships, and ultimately defines your brand’s credibility. In other words, your data does something to you, whether you realize it or not.

The good news, though, are you’re not powerless.

Start by knowing your data: where it lives, how it’s used, and who touches it. Build awareness among your people, create clear accountability, and make data quality a shared responsibility and not just something for IT to think about.

When you shift from doing data to understanding data, your organization transforms. You stop reacting to problems and start building with confidence. Your reports become reliable, your customers more engaged, and your teams more empowered.

Because in the end, the real question isn’t “Do you do data?”. It’s “Does your data work for you, or against you?”