Best Data Validation Methods: Be a Data Validation Expert With These Tips, Tricks, and Techniques

Data fuels the modern world with applications in every aspect of life. Around 64.2 zettabytes of data were created and consumed globally in 2020, as per Statista reports, and that number is only growing. Whether you are learning data science for business or want to enhance your technical skill set, understanding data validation techniques is highly beneficial.

Data validation techniques remain one of the most significant elements of effective database management. Data validation methods involve the process of testing data to check for its consistency and accuracy. This is critical in data analytics, where a minor degree of error can disturb the end results adversely.

Best Data Validation Techniques Examples

There are both reactive and proactive data validation techniques available. You can use them as per the need, but usually, it is better to be proactive before an imbalanced dataset creates problems for you. Here are some popular and effective data validation techniques that you might use. Knowing them will help you get a job in data analytics.

Scripting

This method of data validation involves writing a script in a programming language, most often Python. Writing a script and doing a detailed comparison as part of your validation rules is a time-consuming process, making scripting a less-common data validation method.

Cross-Validation

Cross-validation techniques deal with identifying how efficient a machine-learning data model is in predicting unseen data. It tests data in the form of different samples or portions. Cross-validation techniques test a machine learning model to access its expected performance with an independent dataset.

K-Fold Cross-Validation

The k-fold cross-validation technique involves splitting data into “k” sections, or folds, to conduct further testing. Each fold or section becomes a test set. The number of folds determines the number of times a dataset would be tested. For instance, five folds mean a dataset would be tested five times. The average of all results is considered the final evaluation of the model, giving a more realistic evaluation matrix.

Nested Cross-Validation

The nested cross-validation technique is popularly used in statistics and machine learning. This technique helps evaluate the performance of training and test datasets. It determines the generalizability of a model to unseen data. The results of this technique reveal whether the model is capable of predicting unseen outcomes or not.

Cross-Reference Validation

Cross-reference validation is used to test the validity of data that is collected from various sources. When data is collected from multiple sources, there is a high risk of inaccuracy. You can use the cross-reference validation method manually or automatically. Manual data validation methods involve more time and effort than automatic programs. The programs are based on algorithms that can quickly run a critical comparison and validate the data.

Holdout Cross-Validation

Holdout cross-validation is another technique used with machine learning models. Under this technique, data is divided into two groups: training and test set. These groups are assigned randomly. The training dataset forms the model while the test set evaluates the performance of the model. It identifies imbalanced data that needs to be rectified.

One-Out Cross-Validation

One-out cross-validation is also known as “leave one out cross-validation.” As the name indicates, the training dataset follows a “minus one” approach. This means that if the dataset has “x” number of instances, the training set will be x-1 of the dataset. The remaining instance becomes the test set. This technique results in highly accurate models leading to unbiased findings.

Stratified K-Fold Cross Validation

This is an advanced data validation version or an expansion of the k-fold cross validation. It also splits data into two sets, but it is not random as in k-fold cross validation. In this technique, you must make sure that each stratum of data is the perfect representation of the remaining dataset.

How to Validate Data

1. Define the plan. The most critical step in data validation techniques is defining a clear action plan. Include all necessary details of any potential issue that might occur during the validation process and a possible action plan to deal with it.
2. Determine the data sample. The second step is to determine the data sample available. If you have a large volume of data, you might want to validate a sample before proceeding to the entire dataset. In this case, determine what amount of data you want to check and determine the correct data type.
3. Determine the error acceptability rate. When working on datasets, you might come across errors that cannot be removed completely. For this, you must determine an error rate acceptable for the particular use of the data.
4. Validate the database. Validate whether the data is present in the given database or not. Cross-check the number of available data fields, IDs, and other records and compare them with the source data. Add an additional step for client-side data validation.
5. Formatting validation. You also need to validate the data formation before beginning your data validation. This involves determining whether the data aligns with the business users’ goals or not, which is useful in helping draw accurate results.

Data Validation Ideas: Top 5 Tips to Master Data Validation

Data validation is a crucial component in data analysis, where even a minor oversight can lead to corrupting entire datasets. Here are some top tips that can help you avoid this and master data validation, making it easier for you to land a job in database administration and relevant data science fields.

Use a Variety of Tools

There are several types of tools with self-validating sensors available for effective data analysis and validation checks. Use multiple tools for better results and consistency. You can find free and paid versions of different data analysis software to help you check the validation accuracy of user input. Employ these tools to get better results.

Double-Check for Outliers

The values that fall outside the predefined range of normal values are termed outliers. These values can disturb the entire dataset if not rectified. Make sure that your database does not have any outliers. You can use different statistical techniques for anomaly detection.

Use Scatter Plotting

Scatter plotting is an effective technique for ensuring the consistent flow of data. Use this technique to analyze your data visually for detecting errors quickly and accurately. You can employ this technique using Excel.

Conduct Spell Check

For effective data validation processing, your data cannot include any spelling errors. Conduct a spell check to make sure that the spellings match the variables in the source data. The spellings must be consistent throughout the database to avoid any discrepancies.

Check for Missing Values

Missing values can create problems in data processing and handling. If you find some values that are missing, try searching for them and then add them to your database. If they are not available, remove the incomplete data from your database.

Are Data Validation Techniques Worth Learning?

Yes, data validation techniques are worth learning. The ratio between unique and replicated data is expected to grow from 1:9 to 1:10 between 2020 and 2024, as predicted by Statista reports. It is crucial to learn data validation methods to have better control over data. Knowing these techniques will help you land a career in database administration or other data science fields. They can also help you strengthen your data scientist portfolio as a beginner.

Data Validation Techniques FAQ