Data validation means checking data for correctness and completeness. Data validation can be described as a series of tests and rules on the data to certify its quality and integrity. From a business perspective it’s important to validate your data to ensure data analysis results are accurate. Data validation tests and rules check four characteristics of good validated data. The four characteristics that data validations tests check for are original, consistent, complete, and accurate.
What is Data Validation?
Data validation is the process of ensuring data has gone through a data cleansing process to validate it is or has correct and useful data. Before using, processing, or analyzing data it is crucial to validate/cleanse your data. When validating data your end goal is to ensure/create data that is accurate and consistent. By ensuring your data is accurate and consistent there is a smaller probability that data is lost, or errors occur during a data lifecycle.
The Need For Data Validation
Trusting your data is crucial for business processes and with data validations your company can be comfortable trusting your data. Having bad data and using it to run your processes can have a major impact on your company. A recent survey from Convertr found that 25% of the leads that are processed are invalid. From that 30% are due to an incorrect phone number. Quality data validation processes could find these incorrect data points, so any data used is accurate and correct. In the example above regarding customer leads, without proper data validations processes time and efforts will be used trying to contact invalid leads.
Data Validation Process
The data validation process is a set of testable rules that ensures data integrity. Companies can have various rules they use to validate their data; however, most use a set of rules that test for originality, accuracy, completeness, and consistency.
The rule we use to test for originality is the data all come from an original source. This original source should be saved and stored for future confirmation on originality. All copies and transformations of the data are accurate and complete and can be traced back to the original data. It is recommended to keep track of every transformation or copy made of the data, this makes your data validation process easy and efficient to keep track of.
When testing for accuracy of the data the goal is to make sure the data are correct. If there are any presumptions or rules needed for your data to be correct you will need to address this during your data validations process. Rules to test for accuracy can include data type and range. Data type testes can refer to making sure data in certain columns only contains either integer, floating, or string data. Range tests check to see if data in certain column(s) only ranges between/above/below particular values. An example would be if your data is only supposed to contain positive values, testing to see if the data contains only values greater than zero.
When testing for completeness, the goal is to check that all available data are included. To pass this test and be validated for completeness, there should be no gaps or missing information in the data. Incomplete data can be a result of unsuccessfully collected data or data entry errors. Testing for complete data can be the difference between going through incomplete phone numbers for leads and contacting leads successfully.
Consistency in data refers to constant and non-contradictory terms. If your data contains quarterly and yearly information, it is necessary for each quarter to be represented in each year. Failing to test for consistency can result in inaccurate results during analysis, in turn can affect business decisions.
Using Valid Data for Data Analysis
"Businesses lose as much as 20% of revenue due to poor data quality."
Confidence in the data being analyzed is key for business decision making. The potential loss of revenue from analysis of bad data is more frequent with the amount of available data. Internal processes are more efficient with valid data. Hours are wasted manipulating data during analysis. If manipulations are not documented, it will result in lost hours next time analysis is run with similar data. Companies have different rules for maintaining data, setting validation rules allows companies to uphold standards and make working with data more efficient.
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