Significance of data validation
Validating the clarity, accuracy, and authenticity of your data is important in mitigating any project defects. If data is not validated you risk basing your decision on erroneous information that is not an accurate representation of the situation is resolved. When validating your data, the values and inputs are very important and so is the model of data itself. If the model of your data is not built or structured correctly, you could encounter problems while using the data in various software and applications.
Both the content and structure of your data set will determine exactly what you can do or how you can use your data. Applying validation rules to verify your data before analysis can help solve “garbage in = garbage out” situations. Ensuring data integrity helps ascertain the correctness of your conclusions. For further information on the importance of data validation and verification, liaise with our data validation online tutors.
How to validate your data
There are two ways through which you can effectively perform data validation. These include:- Validation by the script: Depending on how good you are at coding, you can validate your data by writing a script. This could involve comparing your data structure and values against your defined rules to ensure that all the required information meets the set quality parameters. But this technique will depend on the size and complexity of the set of data you are verifying. Sometimes validating large and complex data by scripts can be time-consuming.
- Validating by programs: There are many software applications that you could use to verify your data. Since these programs are developed to perform these kinds of tasks, validating data using this method is easy, quick, and very straightforward. Data validation programs are built to understand the file structures and rules you are working with, making the entire verification process a piece of cake. A good example of such programs includes FME data validation tools.
Steps for data validation
- Identify data sample: If you are working with large, complex data, you will likely only want to verify a small section of your data. So instead of running a check on your entire set, determine a sample you could use and do the verification. To make sure your project is successful, you will need to identify the volume of data you will be sampling as well as the acceptable error rate.
- Validate the database: Before you move your sample data, make sure that all the required information is available in your existing database. Ensure that the number of unique IDs and records are the same as the source data.
- Validate the format of data: Find out the overall health of your data and the number of changes that need to be performed on the source data to match your quality requirements. Look for incomplete counts, null field values, incorrect formats, duplicate data, and anything else that may result in inaccurate outcomes.
Limitations in data validation
There are a couple of reasons that data validation may prove challenging. For instance:
- Sometimes the data may be distributed across many different databases making it difficult to locate. Such data may even be outdated which could make the validation process even harder because one has to obtain the most current data.
- In instances where there are no data validation programs available, the process can be tiring and time-consuming because one has to do it manually.
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