What Is Data Validation?

Data validation is the process of checking and confirming that the data collected for specific purposes is clean, accurate, and reliable. It involves a set of processes and technologies that help verify, clean, and transform raw data into consistent, usable information.

Data validation ensures that the data complies with the necessary standards and quality benchmarks. To make sure it is fit for decision-making, analysis, and other business processes.

Types of Data Validation

Uniqueness Check

This form of validation ensures that each entry in a specific field is unique. It is often used for fields that serve as identifiers like email addresses or user IDs.

Code Check

In this validation, entries are checked against a set of permissible codes. This is common in instances where specific categories or labels must be used.

Look Up

This involves checking the entered data against a predefined list or a set of values to ensure its validity. Look-up validation is often used in dropdown menus.

Presence Check

This ensures that essential fields are not left empty. It verifies that data is actually entered where required.

Length Check

Here, validation ensures that the data entered meets specified length requirements. This could involve either maximum or minimum length constraints.

Range Check

This form of validation confirms that a numerical value falls within a specified range. For example, a temperature sensor reading might need to be between -40 and 140 degrees.

Data Type Check

This type of validation ensures that the data entered matches a predefined data type, be it numerical, text, date, etc.

Consistency Check

This is done to ensure that multiple related fields are consistent with each other. It aims to ensure that the data entered does not contradict itself.

Format Check

Finally, this checks that the data entered is in a predefined format. This is commonly used for fields like date, time, and phone numbers.

Each of these types of data validation plays a specific role in ensuring that the data being used or stored is clean, accurate, and reliable.

What is The Need for Data Validation?

Data validation is no longer optional for businesses; it's a necessity. Poor quality data can lead to flawed business decisions, which can significantly affect an organization's growth. Here's why businesses need data validation:

• Accurate Decision-Making: Validated data provides businesses with accurate information. This accurate information is crucial for decision-making, strategic planning, and forecasting.
• Customer Satisfaction: Accurate data can help businesses better understand their customers' needs, preferences, and behavior. This helps in enhancing customer experience and satisfaction.
• Operational Efficiency: Data validation improves data quality, which in turn enhances operational efficiency by reducing errors and saving time spent on correcting them.
• Compliance: For certain industries, data validation is a regulatory requirement to ensure compliance with standards or laws.

How to Perform Data Validation?

Here's a detailed breakdown of each step involved in the process.

Establish Data Requirements

The first step is critical: outline the rules and characteristics that your data must conform to. These guidelines set the stage for what is considered valid data within your system. For example, if you're handling financial data, you would specify that all transaction amounts must be numerical and that customer IDs should adhere to a particular structure.

Select Validation Techniques

Given your predefined requirements, your next move is to decide on the appropriate validation techniques. You'll need to be precise about what kind of validation is most suitable for your specific types of data. For example, if you're managing a customer database, you might require format checks for email addresses and range checks for age. The selection of these techniques should align with both the nature of the data and the quality level you seek to maintain.

Implement the Validation Process

Here, the theoretical plans you've made need to be translated into action. Utilize specialized software or in-built tools to enact the validation criteria. Accuracy in this phase is paramount; an improperly configured validation process can lead to false positives or, worse, false negatives that permit incorrect data to infiltrate your dataset.

Examine Validation Outcomes

Post-validation, take time to scrutinize the findings. Your software or tools will flag data that doesn't meet the predefined criteria. Each piece of flagged data requires examination to identify whether the issue is a one-off anomaly or indicative of a larger trend. Once understood, remedial action can be taken, such as rectifying the incorrect data or perhaps adjusting the validation criteria for better alignment with real-world scenarios.

Conduct Regular Validation Cycles

Finally, data validation should not be perceived as a one-off task but rather as an ongoing obligation. Your data landscape is not static; it will change over time due to various factors such as updates to regulations, shifts in business operations, or the introduction of new data sources. Therefore, recurring validation cycles are necessary to continually enforce data integrity and quality.

Examples of Data Validation Tools

Several tools can aid in the data validation process, and the choice often depends on the specific needs of your business.

• Trifacta: A more robust tool, Trifacta offers features for data discovery, structuring, cleaning, and validation.
• Talend: Talend provides a broad suite of data integration and management tools, including data validation.
• Informatica: Known for its robust data integration platform, Informatica offers data validation as part of its quality tools.

Conclusion

With the right data validation rules, processes, and tools, businesses can avoid the pitfalls of poor data quality. This in turn enhance decision-making, customer satisfaction, and helps in optimizing time-consuming processes.

You might have heard this before, data is only as valuable as its quality, as it's directly related to time & money. The more time you spend on validating data, the more money you loose. This is the reason organizations need to start treating data validation as an integral part of any data management strategy.

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