Accurate and reliable data is critical for businesses to make informed decisions and avoid costly mistakes. However, ensuring data validity can be a complex and time-consuming process. This blog post will discuss the importance of data validity for businesses, the difference between data validity and accuracy, and data validation rules.
Nowadays, more companies are beginning to recognize the value of big data and the many different functions it can serve in an organization. Data and insights can help an organization develop solutions and improve its processes so that it can get the edge over its competitors. However, what if the data isn’t valid? In this scenario, the decisions made based on this data will be invalid as well. This can lead to huge losses in both time and money as you are left scrambling to repair the damage of a failed initiative or project.
Data accuracy could also be considered data validity. This is where the idea of the validity of data is significant. What is data validation? Basically, it’s the process of checking the integrity, accuracy, and quality of data before it is used for a business purpose.
Data validity is essential for businesses to make informed decisions based on accurate information. The idea is to compare a data set against certain defined rules to ensure the correctness of the data both in structure and content. These rules or checks vary in style.
There are several different types of data validation checks, including data type checks, code checks, range checks, format checks, consistency checks, and uniqueness checks. These are the main validation checks examples that demonstrate just how many ways there are to validate data. The specific checks to use is up to the business and depends on their goals as well as the nature of the data they are managing.
One area where data validation is most crucial is in the transfer point between the source systems that have collected the raw data and your central data depository. Before the data is loaded into this central location, it’s imperative that you have validated the data and ensured that it is completely consistent in type.
There are many examples of data validation; one of them is the ETL validation script. ETL stands for Extract, Transform, Load. These scripts are often manually created for various data sets and then used by data engineers to import data. Within these scripts are often rules that consist of the kinds of checks we’ve already described to ensure data validity throughout the transition.
information. It is particularly critical in areas such as finance, marketing, and customer service. Poor data quality can lead to uninformed decision-making; inaccurate data, or data that doesn’t account for all available inputs and sources, and other data-related issues give an incomplete picture of the organization. This will undoubtedly have a negative impact on customer satisfaction, revenue, and reputation.
Data-driven initiatives and projects are only as good as the data going into them. If there are defects in the data, there will be defects in the project’s results. For large, expensive endeavors, problems like this can be disastrous. In contrast, accurate data can lead to better customer experiences, improved financial performance, and more efficient operations.
In any discussion of data validity, you’ll probably also hear the words “accuracy” and “reliability” thrown around. Let’s consider validity vs. accuracy vs. reliability.
When looking at data validity vs. accuracy, there are certainly similarities. However, the two terms are not interchangeable. Both data accuracy and validity seek to describe the quality or usability of the data. However, data accuracy refers to how well the data corresponds to the real-world or true value of an entity. Data validity can be defined as a term to refer to how well data values are consistent, based on defined rules.
For example, an accurate data point would be a street address that is actually where the survey respondent lives. Basically, a valid data point could be a correctly formatted street address. As you can see, there is a pretty big difference between the two concepts. Data can be valid but inaccurate. For example, if a real address was given but was not the actual habitation of the respondent.
It’s impossible for data to be both accurate and invalid. Let’s also look at data validity vs. data reliability. Again, both terms are measures of how useful the data is, but there are differences. Reliable data is data that can be reproduced consistently, given the same conditions. Once again, data could be valid but not reliable. It’s important to understand the differences between these terms.
The following are some of the most common data validity issues that data teams must address in order to ensure data quality and reliability:
To address these issues, businesses should establish clear data standards and use data quality management software to detect and correct inconsistencies.
The most crucial step in ensuring data validity is to establish a clear and consistent data collection process. This should include defining data sources, determining data types and formats, and establishing procedures for data entry and verification. It is also essential to ensure that all data is collected in a timely and consistent manner and that any discrepancies or errors are corrected promptly.
Data entry and verification are critical steps in ensuring data validity. It is essential to have trained staff to enter data accurately, consistently, and in a timely manner. Verification should involve checking data for completeness, consistency, and accuracy. This can be done through manual checks or automated validation tools.
Data cleaning and normalization are necessary to ensure that data is consistent and accurate. This involves identifying and correcting errors, removing duplicates, and standardizing data formats. Data cleaning can be time-consuming, but it is essential to ensure that data is accurate and reliable.
Data analysis and reporting involve using data to inform decision-making. It is essential to use appropriate data analysis techniques and tools to ensure that data is accurately interpreted and reported. This can involve statistical analysis, visualization tools, and dashboards that provide real-time data insights.
At this point, you’re probably wondering how to validate data. There are a couple of ways organizations go about it. The most popular way of data validation is scripting.
Scripting is a low-risk, versatile, and popular way to go about validating data. As long as your script complies with data validation best practices, this can be a great way to ensure that your data is valid. However, there are some downsides to a scripting strategy. For one, validation scripts are not capable of validating real-time data streams coming in from complex data pipelines.
Validating real-time data is a growing need in modern cloud-based architectures. Also, validation scripts are not easily scalable. Scripts can take time to be updated and can lead to increased costs whenever technology is changed. Fortunately, there are other data verification and validation methods. One of them is using an enterprise solution to automatically validate data streams. One of the best data validation examples of a tool like this is Acceldata.
Acceldata is a data observability platform that can be used alongside a system like Kafka to automatically validate data streams in real-time. Eliminate repetitive, tedious scripting and free up time for your data engineering team to focus on innovations that can grow your business. The platform can help to attain the benefits of data governance and data quality management.
Data validation rules are the specific controls that define the format of the data. We’ve already listed various data validation rules examples above. Let’s take a closer look at a few data validation examples.
The type check is a data validation rule that looks to confirm the data type (integer, string, or some other format). A code check is a way to ensure that the data comes from a valid list of values or follows certain other formatting rules. For example, a code check could be applied to zip code values to ensure that each one can be found on an official list of zip codes.
When it comes to data validation rules for access control, these are fairly common and consist of things like character limitations and length requirements for passwords. Whatever kinds of data validation rules apply to your data set; it is vital that your data is checked against them so that your organization can confidently make data-driven decisions.
Data validity rules help to ensure whether or not data meets the determined criteria. To automatically monitor data validity issues, you can use the Acceldata data observability platform. It not only helps you to ensure data validity but various data quality dimensions. The platform can help to gain insights into your data stack to enhance data reliability and platform performance.