Data Management

What Is Data Quality – Definition + Why It’s Important

Data is big business. It is used across numerous industries, and everyone is talking about the competitive edge that they can get from data.

But as data has grown to become one of the world’s most valuable commodities, data quality has started to dominate the conversation. The opportunity is clear – but to get the best results, companies need to be able to trust the quality of their data.

The simple fact is that many companies are unaware of what useful data looks like. Managing data, filtering, and improving datasets can have a significant impact on results. That’s why we’ve written this post – to help understand what you can do to source better data, and improve on the data already in your infrastructure.


What is data quality?

So what is data quality? Well, there are usually many factors that contribute to a dataset being better than another. These individual factors may be more or less important for each company, depending on the use case.

However, data quality can usually be divided into smaller categories that are as follows:



One of the most important factors to consider with your data, but what does it mean? Accuracy is the similarity between the data and the actual real-world situation it is related to.

Having data that doesn’t represent the real-world conditions presents numerous problems. It can cause incorrect conclusions and can create real issues further along the line.

An example can be seen in location data when a data point is falsely attributed to a store. The data could be inaccurate as the real-world device might not have gone inside the location. The data suggests that the person has, and this can create issues for somebody targeting devices that are aware of a product inside the location, for example.



This refers to how consistent the amount of data is from entry to entry. Having incomplete data means that some of the fields are missing. This means that the dataset as a whole is not as valuable as there may be some insights that can’t be reached due to missing information.

For example, let’s say that you are collecting data from a form or survey. If there is an option to skip the interests field, then your data set will likely have missing information in this field. In the future, somebody may wish to segment this list based on interests. Doing so would probably remove some people that might have the same interests but didn’t submit them in the data collection process.



This term is used to describe the delay between the real-world event and the data. This should be as close as the real-world event as possible as data becomes less effective the further away it is from the real-world event.

As the world changes, delays in data reporting can have huge effects and can drastically limit the effectiveness of data. For example, investors using data to gauge stock performance can get a significant competitive edge if their data is more timely than the competition.



Making sure that the data you use is relevant to the purpose is essential. Clearly defining goals and the types of data needed to achieve these is a crucial part of the data collection process.

Relevancy makes sure that your data is as lean as possible. By including only relevant information, you make it easier to ingest, filter, and manipulate data most efficiently.



When comparing data, the structure must be consistent, so that you can accurately make comparisons and identify differences and trends between two data points.

This extends out to different departments and people that are using the data. If the same dataset is presented differently or formatted differently, then this can cause huge issues. For example, when measuring KPIs, if the underlying dataset is different, then two entities could have completely different ideas of what’s going on.


Why is quality data relevant?


As new regulations relating to data come into play, compliance becomes more of an issue for companies and their data.

Ensuring that data is appropriately collected and managed in a way that complies with internal and external regulations is now a crucial part of any data business. Data quality is a fundamental part of this issue as bad or disorganized data makes it more difficult to prove compliance.



Ultimately useful data is invaluable for companies as it leads to better outcomes and helps them to reach their goals across many departments and areas. Data quality allows each department to make better decisions and achieve their goals.

Poor data can have an opposing effect; it can lead to drastically wrong decisions. That’s why it’s crucial to be able to manage and control data quality from collection through to use.


Benefits of high-quality datasets

Better insights and ability to plan effectively

The more high-quality data that you have, the better your insights are going to be. This allows you to make better decisions, understand what will happen in the future, and plan effectively.


A bigger competitive advantage

This one kind of goes without saying. If you have better data than your competitors, then you are in a much better position. This competitive advantage allows you to act quicker, with more insight, and get better results.


Less time spent fixing problems after ingestion

Better data doesn’t just get you better results; it saves you time and resources. Having consistently high-quality data flowing into your organization makes it easier to generate insights and makes it easier to deliver these to the right people in the organization. It also makes it easier to map data.

Contrast this with bad data, which can require vast amounts of time, adding structure and reformatting into an acceptable state.


Better segmentation, targeting, and attribution

For marketers and advertisers, higher quality data means improved segmentation and better targeting. Collecting quality audience data allows markets to build detailed profiles and match behavior to conversions across the customer journey.


Improved customer experience

Quality data insights can help to build new products and improve how users use existing tools and services. You can be alerted to areas of customer pain and identify an example of why and how customers are dropping off from your funnel.


Improved commercial results

Better data will have a positive effect on commercial outcomes. It will help to reduce waste and ensure that your marketing campaigns are of the highest quality.


Collecting high-quality data

Data quality issues can occur during the collection process and cause huge problems at a later time. It’s vital to get the data collection stage right as doing so is one of the quickest ways to bring in structured, quality data to your organization.

Generally, issues arise because of a lack of tools or structure. Having the right data governance policies is another area where companies should focus to collect high-quality data.


Make a plan

A lack of a plan is going to seriously inhibit the results that you get from data. Plan for how the data is going to be collected, the tools that you’ll need and how to ingest the data in the best way. This should extend to the roles and people that have specific roles in the process.


Define standards

Make sure that your organization has a communally available and agreed-upon definition of what quality data looks like. Everyone should buy into these standards to ensure that your collected data is high quality.