Data is at the heart of every modern business. Without data, businesses would have no way to strategise campaigns, engage with customers, or discover new contacts. For many businesses, data management can come with several difficulties. Datasets can encounter many data quality issues, hindering the effectiveness and performance of the activities that rely on them.
Data quality issues are very common. So, if you’re wondering why your customer data isn’t delivering the results you need, you are not alone. It’s key for businesses to stay on top of data quality issues with the appropriate management.
This article covers the most common data quality issues that businesses face, and how to overcome them. Let’s get started!
Why is data quality important?
Data quality is a term used to describe how well your data serves the purpose it is intended for. For example, a customer database might be used by businesses to send regular email communications, or direct mail campaigns to contacts. This process might even involve additional activities, such as data segmentation, to tailor specific communications to specific customer categories.
But what if this data contained inaccuracies or inconsistencies?
If there are issues with data, then it cannot serve its intended purpose, putting a halt to the business activities that require it. Data quality is therefore integral to any successful business model.
So let’s understand how to look out for the most common data quality issues, and more importantly, how to overcome them.
6 Most common data quality issues
Data quality issues can arise from all angles. They can even be hard to spot, with many businesses unaware that their data actually contains fundamental errors that are holding back its potential.
Here’s an overview of the most common data quality issues that organisations face.
1. Human error
One of the most common causes of data quality issues is human error. Data entry relies on human input, so when this goes wrong, the data becomes practically unusable.
Human error can occur on either side of data entry, both from the customer and the business.
For instance, if customers and prospects provide their contact details via a website form, there is a chance that they may input this information wrong. Equally, businesses can also be responsible for human error. It is possible for employees that manage the data to make mistakes when handing, migrating, merging or inputting data.
It is important for businesses to ensure that all employees understand how to use data management systems and reduce the likelihood of human error.
That’s why online data management platforms are so handy. These tools are specifically designed to make data management much easier for businesses.
2. Incorrect formatting
Another cause of data quality issues is inconsistent or incorrect data formatting. Formatting refers to the way your data is organised, processed and categorised.
For example, email information might be formatted under the category name ‘email’, but it could also be formatted under the name ‘email_address’ or something similar.
If this is not consistent across the dataset, this can lead to big data quality issues. Systems that process this data will not recognise that these labels actually mean the same thing, instead, attributing them to different identifier types.
This makes it very difficult to consolidate, adapt and use the data effectively. It can also increase the likelihood of human error and data duplication, as anyone who tries to reformat the data more consistently might create other data errors in doing so.
The good news is that this can easily be overcome with some simple data cleansing.
3. Data duplication
There are now so many ways for organisations to acquire data. From website sign-ups, social media, to buying customer databases. Data comes from all directions, so it’s important to ensure that data isn’t being duplicated.
Duplicate data occurs when customer information appears more than once in the database, or multiple variations of the same individual appear. This can have a negative impact on business performance for a number of reasons.
Firstly, budget is wasted on duplicate records, having a significant impact on return on investment (ROI). It is more beneficial to make space for new, unique customer records. That way, the budget is not wasted on engaging with the same contact twice.
Secondly, duplicate data can damage your brand image. It is unlikely that a contact who receives the same information twice will be happy about it. This is an easy way to frustrate customers and prospects, and can make your business appear disorganised and untrustworthy.
4. Inaccurate data
Accuracy is critical to quality data. In any industry, data inaccuracies will hold you back from achieving your aims – both in the short and long term.
In essence, there is almost no point in engaging with contacts in your database if the information is incorrect. By not addressing this key data quality issue, incorrect contact data could actively be hurting your business performance.
It is tough to identify inaccurate data. In some cases, communications might bounce. However, in other cases, you just have no idea. This is a commonly unnoticed data quality issue, since you can only really know for sure by verifying the data through a trusted source – such as our market-leading data validation service.
Why inaccurate data is a big issue
Accurate data also ensures businesses are prepared in the event of unpredictable change. For example, in the emergence of the COVID-19 outbreak, businesses quickly had no choice but to rely on customer data in order to operate their businesses online, and adapt in new directions.
This was no more apparent in the USA’s response and management of COVID-19, which was hindered as a result of health data quality issues. Reports highlight the underappreciated significance of healthcare data accuracy in the event of unprecedented change, where low-quality data made it difficult to cope and adapt.
Though, many organisations took proactive data-driven approaches to adapt their strategy in the wake of the pandemic.
For instance, Marie Curie partnered with REaD Group to incorporate COVID-19 risk data into their data strategy. This helped Marie Curie identify the impact of COVID-19 on the UK population, and manage their marketing strategy and future fundraising activities around this.
5. Incomplete data
Similar to inaccurate data, incomplete data can also have a negative impact on your business performance.
This data quality issue might not be as severe as having totally inaccurate data, but it is nonetheless another hindrance to your marketing capabilities.
If data is entered manually through form fields, the customer might not input their full details. Furthermore, some customers might opt to only input certain fields of information such as their name and phone number, whereas some might opt to input their name and email address. This results not only in incomplete information, but inconsistent information.
One way that organisations can help control this is by making certain form fields a required entry. That way, data entries will become more consistent and complete.
However, if your incomplete data is too far gone, this fix won’t be enough.
But not to worry. There are several ways to overcome this data quality problem. For instance, through data enhancement services – which is designed to fill in those important missing gaps in your dataset.
6. Fraudulent data
A less common but very serious data quality issue is fraudulent data. Sometimes, incorrect data is inputted for spam or fraudulent purposes. In no case is this information ever valuable to businesses.
This data quality issue has a serious impact on ROI, since there is no value in this kind of data. Keeping your data clean and preventing customer identity fraud is of the utmost importance.
How purposeful is your data?
Data is only as valuable as you make it. We help businesses get the most out of their data with a range of professional data services. From making your data clean and compliant, to enhancing it with the right information, we work with you to maximise your data’s potential.
Get in touch with us to find out how we can help.
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