9 Common Data Quality Issues and How to Overcome Them

In today's dynamic business landscape, data serves as the cornerstone for both strategic and operational activities – from shaping high-level plans to engaging with customers on the ground. Yet, managing data often comes with challenges. Many businesses face data quality issues that can hamper the effectiveness of their most crucial activities.

According to the Wakefield Research data quality survey, over half of businesses surveyed reported that 25% or more of their revenue was affected by data quality issues. Notably, the average impacted revenue surged from 26% in 2022 to a staggering 31% in 2023.

This statistic highlights how the strive for high-quality data isn't just about avoiding pitfalls; it's a catalyst for success, transforming accurate information into a competitive edge.

Why is data quality important?

Data quality relates to how well your data serves its intended purpose. Ensuring high-quality data is crucial for making informed business decisions, as it directly impacts the accuracy and reliability of your analyses. Inaccurate or unreliable data can lead to misguided strategic planning and ineffective implementation of business initiatives.

For example, a customer database might be used by businesses to send regular email communications, or direct mail campaigns to contacts. These activities, often involving data segmentation for tailored messaging, rely heavily on accurate and consistent data. However, introducing inaccuracies or inconsistencies to this data undermines its intended purpose, disrupting critical business activities.

In essence, if your data is subject to quality issues, it undermines its ability to support the necessary functions of your business. Recognising and addressing common data quality issues is paramount. In the sections to follow, we'll explore the most common issues and strategies to ensure your data remains a reliable foundation for informed decision-making.

The 9 most common data quality issues

Data quality issues can hit your business from different angles, and sometimes, you might not even realise they're there. Some of the most common data quality issues include:

Human error
Incorrect formatting
Data duplication
Inaccurate data
Incomplete data
Fraudulent data
Unstructured data
Hidden data
Too much data

1. Human error

Human error contributes to a substantial 75% of data loss, highlighting the significance of addressing data quality issues. Given that data management hinges on human input, any missteps can render the data practically unusable. Such errors are not confined to one side; they can originate from both customers and businesses during the data entry process.

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 simplify data management for businesses, providing a practical solution to mitigate human error and enhance overall data quality.

2. Incorrect formatting

Inconsistent or incorrect data formatting stands as a significant contributor to data quality issues. Formatting encompasses the organisation, processing, and categorisation of your data. 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 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 information appears more than once in the database, or multiple variations of the same data values 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

A report from MarketingSherpa finds that, each year, around 25-30% of data becomes inaccurate, resulting in less efficient sales and marketing campaigns. Poor data quality can cause businesses to lose up to 20% of their revenue. 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 leading marketing data validation solution or our data cleansing suite.

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 us 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. However, there are several ways to overcome this data quality problem. For instance, through data cleansing or data enhancement solutions – which are 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.

That’s why it’s useful to regularly check the quality of your data, either through an online data management platform, or professional data cleansing services.

7. Unstructured data

Unstructured data poses a significant challenge to data quality, as it lacks a predefined data model. This type of data, which includes text documents, emails, and multimedia files, doesn't fit neatly into traditional databases, making it harder to organise, analyse, and derive meaningful insights. Unstructured data issues can impede effective decision-making and hinder the extraction of valuable information.

8. Hidden data

Hidden data, often overlooked, can be a silent culprit affecting data quality. Businesses may possess valuable data that goes unnoticed, residing in obscure corners of their data warehouses or data lakes. A dedicated data catalogue solution is key to unveiling underutilised data.

A study from Aberdeen Strategy & Research finds that best-in-class companies are 30% more likely to have a dedicated data management solution, ensuring that all valuable data is identified, catagorised, and utilised optimally.

9. Too much data

The sheer volume of data can become a data quality issue, impacting efficiency and hindering decision-makers. Business users, data analysts, and data scientists spend a staggering 80% of their time searching for and preparing data. This inefficiency can be addressed with effective data governance strategies and management systems, streamlining the process of locating the right data and allowing teams to focus on analysis and strategic decision-making.

How to solve data quality problems

Addressing data quality problems requires a systematic approach to guarantee accurate, reliable, and actionable information. Here's a checklist on how to solve data quality problems:

Identify data quality issues: Conduct a thorough audit of your data to pinpoint inaccuracies, inconsistencies, and other issues. Scrutinise data sources and verify their reliability.
Implement data validation: Utilise data validation tools to verify the accuracy and completeness of your data. This step ensures that information aligns with predefined standards, minimising errors.
Standardise data formats: Ensure uniformity in data formats and labelling conventions to prevent issues arising from inconsistent formatting. This includes fields like emails, addresses, and phone numbers, promoting a standardised approach across your dataset.
Data cleansing tools: Employ data cleaning tools to systematically identify and correct errors within your dataset. These tools are designed to automatically detect inconsistencies and inaccuracies, streamlining the error-fixing process.
Eliminate duplicates: Employ deduplication techniques to remove redundant entries from your databases. This not only enhances data accuracy but also optimises resource utilisation.
Invest in employee training: Empower your team with comprehensive training on data management practices. Well-trained employees are less likely to introduce errors during data handling, migration, or inputting.
Utilise data governance practices: Adopt robust data governance practices to unveil hidden data gems. This approach ensures all valuable data is identified, managed, and utilised optimally as part of an overarching data management strategy.
Streamline data management systems: Leverage data management platforms to simplify data management processes. These tools are designed to mitigate human error and enhance overall data quality.
Ensure comprehensive data enhancement: Combat incomplete data by incorporating data enhancement solutions. These tools fill in crucial missing gaps in your dataset, promoting consistency and completeness.

By implementing these steps, businesses can navigate through and overcome data quality problems, ensuring a solid foundation for informed decision-making and successful operations. Understanding the different dimensions of data quality can help, offering criteria to check the quality of your data.

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 tailored business and data solutions. From making your data clean and compliant, to enhancing it with the right information, we work with you to maximise your data’s potential. To learn more about maintaining high data quality, see our blog on data quality management.