One needs to find out how accurate the data they are using is. It also needs to be evaluated in terms of quality. The assessment of data considering these two aspects is called data validity. In layman’s language, the stored or collected data should be logically consistent.
In computers and PCs using automated technology, data is entered with little or no human check. Hence, it needs to be ensured that the information provided is correct. Hence assessing the validity of the data is of immense importance. There will be no use of the data and the automated computer if the data is entered incorrectly or is of poor quality.
Data validation is done to:
- Make effective decisions through the data collected.
- Make the data collection process efficient
- Ensure that the collected data is usable and has practical applicability
- Make profits
- Ensure optimum utilization of resources
Why is the validity of the data important
The validity of the data is checked to ensure that the results are accurate and reliable. This is of immense importance in research and IT applications since most managers and decision-makers rely on factual data. Various theories and statistics interpreted from assessing the data help to conclude.
Problems due to invalid data
If the data turns out to be invalid or not accurate, it can cost a company a significant amount of loss. This can be in terms of costs incurred in collecting, the efforts made, cleaning the data, storing and retrieving it in the computer.
Data validation procedures:
Seven major data validation procedures help you check the accuracy and quality of the data.
Data type check
This type of check tells you if the data entered is factually correct or not. For example, while working in excel, a date and time field accepts only data entered using the date and time format. Hence, it won’t allow for other data types like text, decimals, etc.
Code check
This check ensures that the data being entered follows a set of rules and regulations. It has to adhere to specific formatting rules.
Range type check
This ensures that the data lies within the specified range. Any data that falls above or below the specified range is not accepted. This reduces the chances of errors.
Format type check
This ensures that the data being entered follows a predetermined format for ease and efficiency in checking. This helps to maintain consistency and reduces the chances of errors.
Consistent type check
This checks if the data is logical and consistent while being entered. For instance, it can be checked if the shipping date of the parcel falls before the delivery date.
Uniqueness check
This ensures that the data is unique and is not plagiarized. It has to be collected from such a source so that its integrity is not compromised and such kind of data is not obtained or accessible to anyone else.
Origin check
The data that is collected has to be from a reliable source. While most of the information is collected from secondary sources, it should be from authentic sources like abstracts, journals, or references from bibliographies. It should be verified.
Applications of data validation
Data is used in various industries like machine learning, artificial intelligence, sound engineering, finance, marketing, neuroscience, business analytics, operations management, etc. It helps the management make the right decisions by the existing market conditions. It also helps the top management to raise the required amount of capital through the proper means.
