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What is Data Quality? Why it is Important
In today’s business world, data quality is essential. Businesses rely on data to carry out essential processes. This can include everything from day-to-day marketing and advertising to key business strategies.
Ensuring that data is high-quality, accurate and purposeful is a must. Without quality data, businesses cannot make reliable decisions based on reliable information. The implication: a serious risk to both short and long-term success.
Data quality is the degree to which data serves its intended purpose. There are several criteria used to measure data quality, including accuracy, completeness, consistency, timeliness, validity, and uniqueness. These are commonly referred to as the 6 data quality dimensions.
These measures are used by data managers to assess data quality levels, identify data errors, and assess whether the data is fit to serve its intended purpose. If data doesn’t meet these standards, it risks failing to support decisions or processes effectively.
For instance, marketing datasets that contain errors are not able to fulfil their purpose, since very little can be done with inaccurate data. This would be an example of poor data quality.
Data quality and data integrity are related, but distinct concepts. While data quality ensures information serves its purpose, data integrity focuses on safeguarding data accuracy and consistency throughout its lifecycle. Quality is often the result, while integrity involves the process of preserving this quality by preventing unauthorised modifications or corruption.
Both work hand-in-hand; without integrity, data quality efforts are limited.
High-quality data enables businesses to make reliable decisions and improve operational efficiency. Increasingly, organisations are using data to assist in key decision making processes, which has led to an increased emphasis on data quality in business.
- Improved decision-making: Quality data ensures that the information used to make key business decisions is reliable, accurate, and complete. With reliable data, businesses can accurately analyse trends and make informed choices, helping them invest in important decisions more confidently.
- Enhanced customer engagement: Accurate data allows for personalised communication, helping businesses connect more effectively with valid, active contacts. It also helps avoid sensitive mistakes that could harm the brand. For example, many organisations screen data for deceased contacts in order to avoid sending marketing materials to the individual or their families, which could otherwise be viewed as insensitive.
- Better operational efficiency: As well as improving the dataset itself, high-quality data can help optimise productivity. With quality data, workers spend less time identifying and validating data errors, and more time focusing on high-value tasks that drive business growth.
- Cost and risk reduction: Reducing duplicates and inaccuracies lowers unnecessary expenses and mitigates risks, like compliance penalties or brand damage.
- Compliance: Regulations like GDPR require businesses to keep personal data accurate, up-to-date, and relevant. As a result, inaccurate or outdated data can lead to non-compliance, which exposes companies to significant fines and legal repercussions.
By ensuring data quality, businesses can drive better ROI, improve customer loyalty, and reduce costly errors.
There is a direct crossover between data quality and data compliance. For example, data protection laws, such as the General Data Protection Regulation (GDPR), require businesses to correct inaccurate or incomplete personal information. To maintain high data quality standards, businesses must ensure the accuracy of their information.
Data inaccuracies are often the leading cause of data leaks, accounting for 88% of UK data breaches, which is one of the reasons these laws are in place.
In order to remain compliant and secure, businesses should undertake regular data quality audits. In one of our 2021 studies, surveys found that 80% of SMEs were aware of GDPR laws around clean and accurate personal data. This means that 20% of SMEs were not.
Additionally, failing to implement data quality standards can result in GDPR fines. Under GDPR compliance laws, businesses can face fines of up to £17.5 million or 4% of the preceding financial year's global turnover - whichever is higher.
These fines highlight the importance of keeping data clean and of high quality. Not only for better business performance, but also for data compliance.
Good data quality can look different for every dataset. Data quality is less about hitting a certain standardised criteria, and more so about ensuring that the data is suitable for its specific purpose.
For example, a healthcare company might require a list of complete, accurate, and valid healthcare records in order for the data to be high quality. Whereas this kind of data would not be relevant in other industries.
It is therefore not necessary for every value to be flawless; this is why there will be different levels of good quality in different datasets. It is ideal to remember that good quality datasets do not have a universal criterion, but a proactive approach to data quality management and improving poor quality data is crucial.
1. Uniqueness
Data is considered high quality when it is unique. This helps ensure that there is no duplication in values across the dataset, keeping data clean and precise. Removing duplicate entries can help avoid sending multiple marketing communications to the same contact, reducing costs and protecting the brand image.
2. Completeness
Data is complete when the dataset contains all the necessary information required to carry out specific activities. Completeness does not mean that every possible entry has to be full – it is about fulfilling relevant data entries specific to the intended activity. For example, an email marketing database would require a full set of email addresses in order to be complete, but it would not require phone numbers in order to carry out the core activities.
3. Consistency
Consistency refers to how well the data entries follow the same format throughout the dataset. To ensure consistency, the same data values and formatting should be used throughout. For example, phone numbers should all be presented in the same way for each contact, such as 07 vs +44.
4. Accuracy
Accuracy is one of the most important characteristics of high-quality data. This refers to how well the data reflects reality. For instance, a postcode that is not truly reflective of the contact’s address would be inaccurate. Businesses need reliable information to make informed decisions. Inaccurate data needs to be identified, documented, and fixed to make sure they have the highest quality information possible. It is essential that the data used in marketing and advertising is accurate in order to ensure that communications target active customers and prevent mistakes.
5. Timeliness
Timeliness refers to how readily available the data is. Data needs to be easily accessible in order to be useful. If not, then this can hinder the performance of campaigns – especially where time is of the essence.
6. Validity
The validity of information refers to the format it is presented in. For example, birthdays can be formatted in different ways: day/month/year or month/day/year. This format can vary depending on the country, industry, or business standards. In order for data to be valid, it needs to be entered in the way that the data system recognises. For instance, the birthday 14/05/1998 would be invalid in a system that formats birthdays in the month/day/year format – since months of the year do not exceed 12.
Common data quality issues
As data grows, so do quality challenges. In fact, results from the Wakefield Research data quality survey found that over half of businesses reported that data quality issues impacted 25% or more of their revenue.
- Data silos: Data stored in separate systems can lead to inconsistency and inefficiency
- Duplictate entries: Repeated entries clutter the database, hindering accuracy and wasting resources
- Human error: Mistakes in data entry or processing can lead to inaccuracies, affecting everyhting from customer details to financial records
- Incorrect formatting: Inconsistent data formats, such as varying date styles or phone number formats, create confusion and hinder data analysis
Addressing these issues is essential to maintaining data accuracy, reducing costs, and improving overall data utility for decision-making.
As businesses adopt cloud computing and big data, data quality challenges have expanded significantly. Cloud environments and massive datasets enable faster data collection from a wider range of sources, but they also increase the complexity of managing and maintaining data quality.
With data now coming from various systems, including IoT devices, social media, and third-party vendors, ensuring accuracy, consistency, and security across all sources has become more difficult.
Additionally, large datasets stored in the cloud are vulnerable to duplication, outdated information, and compliance risks, making effective data quality management essential for businesses to leverage the full potential of their data without compromising on reliability or compliance.
Improving data quality requires a structured approach. Here’s a step-by-step guide with actionable strategies to enhance the accuracy, completeness, and consistency of your data.
When considering how to improve data quality, the first step is to assess your data’s current state. Take a look at what you have, and compare this to what you need to perform your intended activities.
This will help you identify the main concerns and areas of improvement in your dataset. For example, are there duplicate entries? Are there data inaccuracies? Is there missing information?
Once you have identified your main data quality concerns, put together a list of clear objectives. As an example, you might need to correct data inaccuracies, deduplicate data, standardise its format, or discard data from a certain time.
Identifying these can sometimes be challenging, especially for those data errors that are hidden in the dataset, which is where data cleansing solutions can really help.
Once you have defined your objectives, it’s time to implement these actions across your datasets. It is also important that you assess data quality across all datasets in order to improve data quality throughout your entire organisation.
One way to automate this process is with data quality management tools, which can help simplify an otherwise complex process. Additionally, tools can help minimise the risks of human error.
Key actions include:
- Deduplicating the data
- Standardising formats across teams to prevent inconsistencies
- Suppressing unwanted or outdated information
- Training employees on standard data entry practicies
- Implementing checks to validate data before it enters your systems
A consistent approach across your organisation helps prevent isolated data quality issues and promotes reliable, high-quality data.
After everything is set into motion, schedule regular data quality audits. This will help you ensure consistent data quality practices moving forward, and ensure that new errors are addressed as they occur.
Regularly review and refine your data quality practices as business requirements or compliance needs evolve. Continuous monitoring helps you adapt to new challenges, ensuring your data remains accurate and fit for purpose.
Improve your data quality
High-quality data is essential for informed decision-making, efficient operations, and compliance.
Get in touch to learn how our data quality solutions can help you reduce errors, enhance customer engagement, and build a trusted data foundation.
