What is the reliability of data? Definition and Examples

Reliability of data is one of many data quality metrics you should assess in order to make sure that the data you use will really help you improve the efficiency of your business. Before you establish a process to help you evaluate if your data is reliable, you have to learn what exactly reliability means . Read our article to find out.

By leveraging low quality data, businesses all over the world lose millions. Often this is due to lack of awareness or inadequate knowledge. Many companies adopt new solutions that use business data (analytic tools, automation systems, recommendation engines), but they don’t know much about data quality. The essential truth such organizations should learn before implementing new solutions is that high quality data can boost efficiency, while low quality data can put a company at risk. A very important metric you should assess is the reliability of your data. Do you know how to determine if you can trust your business information?

What are the most important data quality metrics?

Data quality metrics allow an organization to measure the quality of the data used for business purposes. The goal is to assess if collected data are good enough to be used for certain processes performed in the company. Among the data you gather all the time, there are usually some pieces of information that are incorrect or incomplete. Therefore, they can negatively affect your business efficiency. That is why measuring data quality is so crucial for a company’s success.

Various sources mention different relevant data metrics, here are some of them:

• Completeness – with this metric, you measure if datasets contain all required information.
• Accuracy – it is very important to use data that conforms with reality.
• Timeliness – this metric reflects the accuracy of data in a specific period.
• Validity – validity assesses if data has all the right values of particular attributes.
• Consistency – you may store and use your data in different applications. If you move data between different systems, you need to maintain a leveraged data quality metric.
• Uniqueness – the particular data piece shouldn’t be recorded more than once.

Some of these metrics are used for assessing the reliability of data. Information is reliable if it is complete, accurate, and indispensable for building data trust. Data reliability is a key data metric for establishing the best data practices in a business organization and making a company more data-driven. 

A reliability of data – definition

According to the short explanation above, data reliability is quite a complex data quality metric, and it’s fundamental for many companies. Ensuring it is crucial for guaranteeing data quality, integrity, security, and compliance across the organization. The ability to trust in the data and business insights produced with it is required for anyone who wants to take full advantage of their business intelligence. Make sure that your information is reliable. Reduce your risk, and make data-driven decisions. 

But how do you do this? There are, of course, tools, processes and policies you can implement in order to improve reliability of data in your company, but…  Before you start to adopt new solutions, you should find out which pieces of data are reliable and which are not. You can accomplish this by performing a process called a data reliability assessment.

Data reliability assessment

Measuring data reliability can uncover problems with the data in your organization that you haven’t even been aware of. This process usually includes assessment of three different aspects of reliability: validity, completeness and uniqueness. You need to make sure that your business data is being stored properly in the right format. The information in your datasets needs to include all the values required by your system. Also check if your data isn’t repeating itself. There should be no duplicated information in your datasets.

The process of assessing data reliability can differ a bit among various companies. You can take other factors into account when designing the data reliability assessment for your organization. That is why you should study data quality metrics carefully – learn what a high quality of data means and how you can ensure it. One way of doing it is to write your own data quality tests in a preferred language, like Python or SQL. There are also some special tools available that you may find useful. Many advanced and complex data engineering platforms have their features for data testing – good examples are Azure Data Factory or Informatica PowerCenter. Others, like GreatExpectations Python package, were designed especially for the purpose of assessing data reliability. All mentioned solutions can be applied for revealing low quality data. Low quality business information could be fixed in the data cleaning process.  

You can’t make truly data-driven decisions before you make sure that your data are really reliable. Data reliability assessment can also sometimes be referred to as trust assessment. It shows how much you can trust your data, and it is very important for building data trust in your company. Hence, it has a huge impact on an organization’s efficiency. 

Data reliability and process automation

Many companies invest in data reliability – in one way or another. You probably do so by establishing some specific validation rules, hiring data engineers with experience in checking data quality or verifying currently gathered data manually etc. It is difficult to assess the reliability of data without professional tools and methods, though. Every time some unreliable data go unnoticed, and based on this, you produce business insights, your company is at risk of making a bad decision. Wouldn’t you rather select solutions that keep the possibility of missing some low quality data to a minimum?

Today, engineers have access to many advanced tools and can leverage automation to improve the efficiency of data reliability assessment processes or data cleaning. There are also machine learning-based platforms that can be used to achieve data reliability much more easily. If you have never used such solutions, we’ll be happy to advise you on the best systems available and assist you with implementation. Remember that, even though adopting new tools requires investment, it will help you to save much more money by eliminating bad decisions in the future.

Ensuring data reliability in your company

The process of checking the reliability of data may seem simple in theory, but requires creating a good strategy, using the right tools and having relevant experience, especially in the big companies that collect and manage huge amounts of data. There are some steps you have to take before choosing the right approach for your organization, those are:

• Identifying unreliable data.
• Learning about some of the issues that cause your data to be low quality.
• Determining your vision for fixing the problems in your company (what kind of improvements could be made).

While some new practices can be implemented right away and improve the quality of your data after a short period of time, you need to understand that other changes will take more time, but ultimately ensure the long-term success of your company. You can choose to take care of data reliability in-house with your own team. To do so, you have to hire experienced data engineers capable of assessing data reliability. You could also consider managed services – entrusting your data to external IT service providers. 

We can help you make the most of your business data. Contact us for more information about our services. 

Check out our blog for more details on Data Pipeline solutions:

• Data Pipeline Definition and Design
• Data exploration – definition and techniques
• 5 Best Practices for Data Validation