What is data reliability?

Definition and assessment guide

According to our recent survey, less than half of executives rate aspects of their companies’ data reliability as “very good.” Business leaders need reliable data to make reliable decisions, so where does that leave executives whose organizational data isn’t up to snuff? Well, they’re either using unreliable data – or worse, making gut decisions, which 36% of business leaders admitted to in our survey.

Of course, improving organizational data is easier said than done. And for businesses that are just starting to explore how to improve their data health, the deluge of information and best practices can often feel overwhelming. Data quality, data integrity, data reliability, data trust – keeping track of these and other terms can be hard enough, never mind actually knowing what to implement or where to start.

For many organizations, data reliability can be that starting point upon which more robust data quality and integrity functions can be built. But first, they need to understand what data reliability actually is, how to measure it, and how to raise the overall level of data reliability at their company.

Data reliability definition

Data reliability means that data is complete and accurate, and it is a crucial foundation for building data trust across the organization. Ensuring data reliability is one of the main objectives of data integrity initiatives, which are also used to maintain data security, data quality, and regulatory compliance.

With reliable data, business leaders can eliminate the guesswork when it comes to making informed decisions. It is fuel that delivers trusted analytics and insights. And it’s one of the most important things to get right when it comes to improving the overall health of an organization’s data.

It can be tempting to jump headfirst into implementing processes and policies that you hope will improve data reliability, but the kinds of issues that cause poor data reliability are numerous, and each cause must be treated differently. The first step is to actually find out which data is reliable and which is not, and this can be determined by a process called data reliability assessment.

Data reliability assessment

Data reliability assessment, also referred to as trust assessment, is an important process that can reveal problem areas about your data that you didn’t even know existed. The assessment will typically measure three different aspects of data reliability:

  1. Validity – is the data correctly formatted and stored in the right way?
  2. Completeness – does the dataset include values for all the fields required by your system?
  3. Uniqueness – is the data free from duplicates and dummy entries?

Data reliability assessment can also take other factors into account and touch on aspects of data quality, such as looking at how many times a dataset has been relied on, where it originated, and how the data has been transformed. Getting to this deeper level of understanding is especially important for data related to sensitive information where complete accuracy is essential. To support a financial audit, for instance, it is vital to be able to prove data reliability.

There are solutions like the Talend Trust Assessor that can assign a quantifiable Trust Score to any dataset, identify reliability issues with that dataset, and highlight areas to focus on for improvement. If the assessment uncovers bad data, there are a number of steps that can be taken to fix it depending on what issues were identified. Invalid data, for example, would likely be put through a data preparation process.

\Until you can actually quantify how reliable your data is, you’ll never be able to make data-driven decisions with absolute confidence. That’s what makes data reliability assessment so valuable. The assessment can either a). show you exactly where to fix data that you know is unreliable, b). reveal hidden issues with data you believed to be reliable, or c). confirm in a quantifiable way that the data you’re assessing is reliable and ready to work with.

The difference between data reliability and data validity

One common misconception when it comes to data is that reliability and validity are the same thing. While both are important for an organization to have trusted data, they actually refer to different aspects of data health.

Valid data refers to data that is correctly formatted and stored. Reliable data, on the other hand, refers to data that can be a trusted basis for analysis and decision-making. Valid data is an important component of reliable data, but validity alone does not guarantee reliability. 

Valid data, for example, can still be incomplete, so relying on validity as the only measure of reliability can still cause issues when it’s time to use that data for analysis or action. For example, you could have a database of customers that you want to send marketing emails to, and you could generate a list of contacts filled with that data which is 100% valid. But if it’s incomplete – if entries are missing details such as email addresses, names, or other pertinent information – then that data is not reliable for its intended purpose. Similarly, depending on how the list was generated, there may be data redundancy issues, where duplicates of the same entry occur in a dataset – with varying degrees of completeness. That’s why it’s important to assess all dimensions of data reliability in order to get the most accurate, complete understanding of your data.

Building a foundation of reliable data

So now that you’ve learned the basics, how can you apply these strategies at your company?

First, you need to assess the reliability of your data based on its validity, completeness, and uniqueness so you can understand exactly what you need to improve. The easiest way to do this is with a solution like the Talend Trust Assessor, which you can use to measure the reliability of any dataset.

Once you know what you need to improve, create a plan to decide which fixes you want to tackle first. Actions like eliminating duplicate data can be easy “quick wins” that will jumpstart your improvement initiatives and put you on the path to success. Next, you might want to identify which improvements will have the largest positive impact on your business and focus on those areas. Some of these initiatives might take longer, such as collecting missing customer details or defining transformation processes to ensure consistency across all your organizational data, but they are essential steps to take for long-term success. Finally, work on any remaining issues the assessment uncovered.

Lastly, remember that improvements to data reliability are not a “one-and-done” exercise. Like all data health practices, consistency is key. Putting preventative measures in place, ideally as part of larger data integrity initiatives, that assess the reliability of new data and fix it before it can propagate across your systems can reduce the risk of your data reliability degrading.

To learn more about data reliability’s role in maintaining a healthy data environment, along with other strategies, tips, and best practices for sustaining data health at your company, read our thoughts on practicing good data health.

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