Data validation is the process of checking the accuracy and quality of data before it is entered into a database or a spreadsheet. Data validation can help you avoid errors, inconsistencies, and duplicates that can affect your analysis and reporting. In this article, you will learn some data validation techniques and tools that you can use in your data entry projects.
Data validation matters because it ensures that your data is reliable and trustworthy. If you enter data without validating it, you may end up with inaccurate, incomplete, or irrelevant information that can lead to wrong decisions, wasted resources, or missed opportunities. Data validation can also help you comply with data standards, regulations, and policies that apply to your industry or organization.
Data validation is imperative because verifying the information allows us to create a clear picture of the information we are collecting and entering. When we fail to validate, we run the risk of spreading Misinformation. Something I find helpful is giving my eyes a break and coming back. Often times looking at the same thing for a extended period of time, our eyes begin to miss fine details, also when available a second pair of eyes is always helpful.
Data validation goes beyond data entry. It also a part of the data science and analysis methodology. It’s inaccurate to frame data validation as only about data entry.
Depending on the source, format, and purpose of your data, there are various types of data validation that can be applied to your data entry tasks. Format validation verifies if the data corresponds to a particular pattern, for instance a date, a phone number, or an email address. Range validation ascertains if the data is within a certain boundary, like a minimum or maximum value, or a predefined list of options. Consistency validation ensures that the data is compatible with other data sources, like a database, a spreadsheet, or a website. Lastly, logic validation checks if the data is reasonable, such as a positive balance, a valid calculation, or a reasonable assumption.
Quantitative and qualitative data validation are also crucial to ensuring the completeness and accuracy of data. Quantitative analysis is essential in determining that complete data fields and statistics have been entered or rendered. Qualitative analysis is also important in determining the accuracy of data in terms of the validity of the data reflected.
Data validation is an important process for verifying the accuracy of your data, and there are various techniques you can use depending on the type of validation you need. Manual validation, which involves checking the data yourself or with the help of another person, is useful for small or simple data sets that require human judgment or interpretation. Automated validation, on the other hand, involves using a software or tool to check the format, range, consistency, or logic of your data, and is ideal for large or complex data sets that require speed, accuracy, or standardization. Combination validation, which combines both manual and automated validation techniques, is best for data that requires both human and machine input, or for data that has different levels of importance or sensitivity.
Data validation is an important task that can be automated or simplified with the use of various tools. For example, data validation features are built-in functions or options available in software such as Excel, Google Sheets, or Access, which can help set rules, criteria, or formats for data and alert you to any errors or violations. Additionally, data validation software is a standalone program or application designed to perform data validation tasks, such as data cleansing, data profiling, data quality, or data governance. Furthermore, data validation services are online platforms or websites that offer data verification, data enrichment, data matching, or data deduplication services to help access external data sources or databases and compare, validate, or enhance data.
Data validation is a critical component of data entry, as it can help guarantee that your data is precise, comprehensive, and pertinent. To make the most of your data validation efforts, you should consider some best practices, such as planning your data validation strategy, selecting the right data validation tools, and testing and reviewing your data validation results. This will enable you to meet your data validation objectives, standards, and criteria, while also improving the quality and dependability of your data. Additionally, it can help you avoid errors, inconsistencies, and duplicates that can have a negative impact on your analysis and reporting.
I find it crucial to create a barrier between data entry and data manipulation. Whether your intended goal is to extract specific data, create a visualization, or something else, there should be a limited number of experienced users owning data manipulation and this should be built in a way where it is rarely modified because all requested changes can be controlled through the data entry. One example of this is leveraging Google's cloud platform to create dynamic queries in BigQuery, while using Google Forms or Google Sheets for data entry. The BigQuery logic performs all the data manipulation while sourcing from Forms or Sheets for specific inputs, which can be a date range, a list of customers, among many others.