How to find, analyse and resolve data quality issues

Data quality is a hugely important issue for businesses. If your company's data is inaccurate, incomplete or inconsistent, it can have a huge impact on your ability to make good decisions based on that data. This could lead you to make mistakes such as overpaying for inventory or hiring candidates who aren't right for the job.

But what happens when the quality of your data isn’t up to scratch? How can you find, analyse and resolve issues that might be compromising the accuracy of your information?

In this article, we’ll take you through everything you need to know about data quality. We’ll also tell you what you need to know moving forwards so you can ensure your data quality is up to scratch.

Why is data quality important?

Data quality is important because it gives you a clear picture of your business's performance. If your data isn't accurate, then you can't use it to make informed decisions about how to improve your business. Without reliable data, you won't be able to tell whether your marketing initiatives are working, or if the changes you make to your product or service offerings are helping you get more customers and keep them coming back. You won't be able to evaluate what works and what doesn't and make adjustments accordingly.

Without accurate data, you'll also miss out on opportunities for growth in the future. There are lots of ways that businesses can benefit from having high-quality data—for example by using it to personalize their offers based on each customer's specific needs, or by using it for predictive analytics so that they can anticipate customer behaviour before it happens.

What are typical data quality issues?

Data quality issues are the bane of many a business' existence. When you're trying to make decisions based on the data you have, it can be frustrating when it's not accurate or reliable. Here are the most common data quality issues that businesses encounter:

Inaccurate information

Inaccurate information is one of the most common data quality issues in business. It happens when there's a mistake in a database, an incorrect field that needs to be updated, or something that needs to be removed altogether.

Too much information (and not enough context)

When you're working with data, you want to make sure you're getting the right information and not too much of it.

The problem is that when you have too much information, you can't tell what's important and what's not. So then you're stuck with a bunch of data that doesn't really help you make decisions. You should always think about context—what does this piece of data mean in relation to other pieces of data?

No standardization across systems/processes

Data quality issues are often a result of a lack of standardization across systems and processes. If a company has multiple systems that are used to collect data, but no common method for ensuring that the information is collected consistently, then it's highly likely that there will be problems with that data.

This can happen when different departments within an organization use different software to manage their tasks, or if different teams have different processes for measuring certain metrics. It can also occur when one department doesn't know what another department is doing, which means they don't have access to the same information as they would otherwise have had.

Lack of data governance

Data governance is the process of identifying, controlling, and protecting data throughout its lifecycle. This helps ensure that all data is accurate, consistent, complete, and can be easily accessed and shared by authorized users.

Lack of data governance can be a major issue for businesses, as it can lead to inaccurate or inconsistent data.

Find and analyse data quality issues with an integrated data platform

Data teams need an integrated data platform that enables end-to-end observability, allowing them to quickly identify and address data quality issues.

Integrated, end-to-end observability of data quality is critical for data teams to ensure that the data they generate remains trustworthy. With an integrated platform, teams can track the entire life cycle of their data from creation through delivery and verify that it meets the requirements for each use case.

Data teams should work with a partner who can provide a full suite of tools that are designed to work together seamlessly. This ensures that teams can focus on their mission instead of searching for tools and developing processes to support it.

We’ve got your data quality issues covered

Our event monitoring system offers both intra- and inter-company end-to-end views of processes. Whether you're looking for a way to keep tabs on your own internal processes, or you need to monitor your suppliers' processes, we've got you covered.

With our system, you'll be able to identify and address data quality issues before they have a chance to impact your business. We'll help you get the most out of your data by providing insights into how it's being used and how it should be used—and then we'll show you how to take action.

Whether you want a better understanding of where your data is coming from and what it means, or if you need help interpreting that information, we've got an easy solution for you!