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.
What exactly is data quality?
Data quality is the degree to which data performs for its specific purpose. This means that the data must serve the outcomes that it is intended for. 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.
There are several criteria used to measure data quality, including accuracy, completeness, consistency, timeliness, validity, and uniqueness. These are commonly referred to as the6 data quality dimensions. They are used by data managers to measure data quality levels, identify data errors, and assess whether the data is fit to serve its intended purpose.
The importance of data quality
Increasingly, organisations use data to aid in the decision-making process, which has led to an increased importance of data quality in a business. Data quality is important because it ensures that the information used to make key business decisions is reliable, accurate, and complete.
It is critical to ensure data quality throughout thedata management process.Data accuracy and reliability are key factors for executives to be able to trust data and make well-informed decisions.When data quality practices are poor, there can be significant repercussions. Imprecise analytics, profit loss, unreliable business strategies, and operational errors can all be traced to poor-quality data.
Using high-quality data, businesses can analyse data, conduct marketing campaigns, and create reliable strategies much more quickly and efficiently. This results in better return on investment, and more precise marketing.
As well as improving the dataset itself, high-quality data can help reduce risks, costs, and worker productivity. With quality data, marketers and data managers spend less time identifying and validating data errors, and more time using the data for its purpose.
Quality data can also help businesses engage with customers more effectively, ensuring that those in the database are valid, active contacts. It can even help avoid brand damage.For example, many organisationsscreen data for deceased contactsin order to avoid sending marketing materials to the individual or their families, which could otherwise be viewed as insensitive.
Data quality and data compliance
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 for88% 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 that80% of SMEswere 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 canface 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.
What does good data quality look like?
Good data quality can look different for every dataset. Data quality is less about hitting a certain standardised criteria, and more so aboutensuring 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.
- 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.
- 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 fulfillingrelevantdata 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.
- 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.
- 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.
- 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.
- Validity:The validity of information refers to the format it is presented in. For example, birthdays can be formatted in different ways:day/month/yearormonth/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 birthday14/05/1998would be invalid in a system that formats birthdays in themonth/day/yearformat – since months of the year do not exceed 12.
How to improve data quality
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 wheredata cleansing servicescan 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.
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. One way that businesses do this is with anonline data management platform, which helps audit and identifydata quality issues.
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