The Importance of Data Validation for Data Science

Data validation is a cost-effective way to prevent costly mistakes and ensure a clean data set for accurate prediction and analysis.

Data validation is essential to catch any mistakes early and avoid data errors. It is crucial for any type of data handling, from information collection to analyzing and presenting data to stakeholders. This becomes even more important when there are millions of inputs in large volumes of data. In order to create the best quality work and data understanding, data validation should be an automatic step throughout the data workflow. 

Data validation can also be called data cleaning – as it cleans up any mistakes. As it is to be expected, inaccurate data leads to inaccurate results and the inability to make correct predictions and analysis. 

It is all too common for data validation to be skipped over. However, this is a mistake, especially considering that it is now possible to incorporate and automate the validation process into the data workflow. It doesn’t even require an additional step. 

Here are the benefits of data validation and how this essential process can reduce costs, improve work output, and ensure high quality data sets.

Benefits of Data Validation 

Inaccurate data leads to everything from incorrect predictions to project defects. Just as contractors say, “measure twice, cut once”, data science would be wise to adopt a similar slogan of “verify twice, predict once”. Here are aa few common issues that data validation helps to mitigate:

  • Data with imperfections will not be accurately representative of the situation.
  • Data with errors will lead to incorrect predictions.
  • Data that is incomplete will skew the results.
  • Data with errors can lead to incorrect conclusions and information given to key stakeholders

In addition to verifying the data, it is important to verify the data model. If it is not built correctly or contains errors, it could have compatibility issues, bugs, or other problems. Data validation mitigates this risk. 

In addition to benefits directly to data functionality, data validation benefits for business include:

  • A cost effective way to mitigate mistakes. 
  • Prevents costly errors later in data analysis.
  • It removes duplicates from an entire dataset.
  • It is easy to use and compatible with different data sets and data models. 
  • It enhances information collection.
  • It improves information utilization.

Consistency of Data Validation 

Some of the most essential rules of data validation include ensuring consistency of data and data integrity. Each company will have unique organizational data standards for compliance and quality control.  These maintain clarity and integrity of the data sets.

Examples of Data Validation Rules:

  • Allow uppercase entries only
  • Prevent duplicate values
  • Allow the entry of weekdays only
  • Allow only numeric or text entries
  • Set a data range
  • Highlight a unique data set
  • Create consistent expressions (such as Street versus St.)
  • No null values

In addition to data standards, it is necessary to also validate formatting standards. Without the appropriate data model, the data will not give accurate results. The data should also be stored in a format that is compatible with the applications where it will be used.

Methods of Data Validation

There are now a variety of methods available for data validation. Each has advantages and disadvantages based on the data scientists’ skills and preferences.

The most common data validation methods are:

  • Scripting — In scripting, the data validation is performed through a scripting language such as python. In scripting, data values and rules are compared to confirm that all information is within the target parameters However, this requires fluency in one or more coding languages, and, depending on the complexity of the data, can be quite time consuming.
  • Validation by programs — This form of validation uses software to perform the validation. There are many programs available now that will be able to understand the data structures and defined rules you are working with. Some of these tools allow built-in validation at every stage of the workflow, creating regular automatic data validation points.   
  • Open Source Tools — Cloud-based open source tools are a cost-effective option for data validation by developers. However, the level of skill in coding required is high and the time required can be more intensive.
  • Enterprise Tools — These include the FME tool. These tools are secure and stable, but are costlier and require infrastructure. They can be used to validate and repair data. 

In Summary

In summary, data validation is a cost-effective way to prevent costly mistakes and ensure a clean data set for accurate prediction and analysis. Data validation can improve accuracy and quality of data sets, improving the workflow and analysis process. 

Each of the methodologies of data validations has advantages and disadvantages. The main target is to ensure regular data validation that is consistent with the data set, goals, and team’s skills for high quality, accurate data prediction and analysis. 


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