Introduction to Data Quality Validation for Machine Learning

In machine learning (ML), think of data as the fuel that powers your models. If the data you use is bad, even the smartest algorithms won’t perform well. That's why it’s so important to check and validate your data before using it to train a model.

According to McKinsey’s 2023 State of AI report, 55% of organizations have adopted AI overall for one or more functions.

However, the accuracy and effectiveness of ML models are driven primarily by the quality of data used to test, validate, and train the model. This is why data quality validation is important.

In this blog, let’s break down data quality validation — what it is, why it matters, and how to make sure your data is top-notch for machine learning projects.‍

Understanding Data Quality and Validation

Before diving into the specifics, it’s important to understand how data quality relates to data validation. They serve distinct purposes—one focuses on the overall reliability of the data, and the other ensures its suitability for a particular task. Let's start by breaking down data quality.

What is Data Quality?

Data quality is basically how "good" your data is. Good quality data is accurate, complete, and relevant. Here’s what a good quality data should have:

• Accuracy: Is your data correct? For example, if you are recording ages, are they right?
• Consistency: Are things like dates written the same way throughout your data? For instance, is the date format consistent (MM/DD/YYYY vs. DD/MM/YYYY)?
• Completeness: Does the data have missing pieces? Missing data can lead to mistakes in predictions.
• Timeliness: Is the data up-to-date? Using old data for things like sales predictions could make your model give bad results.
• Relevance: Is the data useful for the problem you are solving? For example, if you are predicting home prices, knowing a person’s favorite color won’t help.

Data Quality vs. Data Validation

Think of data quality as checking if your data is overall good, like ensuring the ingredients for a recipe are fresh, clean, and complete. On the other hand, data validation is more about making sure those ingredients are the right ones for the specific dish you are cooking. For example, you wouldn't want salt where sugar is needed.

In simple terms, data quality ensures your data is reliable and accurate, while data validation ensures it's appropriate and usable for a specific purpose. Know that even if the data is of high quality, it might not fit the task unless validated.

For example, let’s say you are creating a customer sales report.

• Data Quality Check: Make sure all customer names are spelled correctly, and sales amounts are not missing.
• Data Validation: Ensure the sales amounts are within a logical range (e.g., no negative numbers unless it's a refund) and that the report only includes customers who made purchases in the last 30 days.

In short, data quality ensures the dataset as a whole is trustworthy, while data validation ensures the data is fit for a specific purpose by catching and fixing errors before use.

Why Data Quality is So Important for Machine Learning

Imagine you are training a model to predict home prices, but your data has missing or incorrect prices. Your model might end up predicting the wrong price for new houses, and that’s a big problem. Bad data leads to biased predictions — meaning your model will just keep getting things wrong.

Good quality data is what makes your machine learning model accurate and reliable. If your data is off, the model’s predictions will be too.

For example, if you are using inconsistent data to train your model, like mixing up "Jan" and "January" in a date field, your model won’t learn correctly.

Effects of Poor Data on ML Model Performance

Here are top 3 reasons why poor-quality data can mess up your machine learning model:

1. Overfitting‍

Overfitting happens when your model memorizes your training data too well, including all the noise and outliers.

For instance, imagine you are training a model to recognize dog images, but your dataset includes photos of dogs wearing hats. If your model gets overfitted, it might learn to associate "hats" with dogs and fail to recognize dogs without hats in new images. This makes the model unreliable in real-world applications.

2. Bias in Predictions‍

If your training data is biased, your model will reflect that bias in its predictions.

For example, if you are building a hiring model but most of your training data comes from resumes of male candidates, the model may unfairly prefer male applicants. This is because it has learned patterns that are skewed by the data it was trained on.

3. Inaccuracy‍

Simple mistakes, like missing values or wrongly labeled data, can cause huge problems.

For example, if you are predicting house prices but the price column has errors, your model will make completely wrong predictions, such as suggesting a $300,000 house priced at $1,000,000.

So, remember: the better the data, the better the machine learning model. Always validate your data’s quality before putting it to work!

Top 5 Data Quality Validation Techniques

Data quality validation techniques ensure that your data is accurate, consistent, and useful for machine learning models, ultimately improving their performance and outcomes. Here are the top 5 data quality validation techniques to follow:

1. Data Profiling

Data profiling is like taking a good look at your data before you start working with it. It helps you see if anything’s out of place or strange.

For example, imagine you are reviewing a customer database and notice some entries have ages like "200" or phone numbers with missing digits. Data profiling catches these issues so you know what needs fixing.

After all, you can’t clean up the mess until you know where it is!

2. Data Cleansing

Once you know where the issues are, it’s time to clean it up. This includes:

• Fixing Missing Data: If some customer info is missing, you might fill it in or just remove those entries if they are too incomplete.
• Getting Rid of Duplicates: If a customer shows up twice, you need to remove the duplicate so it doesn’t mess up your analysis.
• Standardizing Formats: If phone numbers are in different formats, like 555-123-4567 or (555)123-4567, you will want to make them all the same, so everything matches up.
• Correcting Typos or Data Errors: Sometimes, data just doesn’t make sense—like ages listed as 200 or zip codes with too few digits. These errors need to be corrected or removed to ensure accurate results.
• Handling Outliers: Outliers are values that seem too extreme to be realistic, like a product priced at $1 million instead of $10,000. Reviewing and fixing these values is critical for accuracy.

Cleaning your data may seem tedious, but it’s the foundation for trustworthy analysis and decisions!

3. Data Enrichment

This is where you add more information to your data to make it more useful and make better decisions or predictions.

For example:

• ‍Predicting Customer Churn: If you are trying to figure out which customers might stop buying, adding extra data like economic trends, their browsing history, or shopping habits can help you understand their behavior better.‍
• Improving Marketing: If you know basic details about your customers, you can enrich that data with information like age, location, income level, or interests. This helps you send more personalized offers that are likely to grab their attention.‍
• Sales Predictions: Enriching your sales data with outside factors like weather, holidays, or local events can help you predict spikes or drops in sales. For example, if it’s going to rain all week, people might buy more raincoats and umbrellas.‍
• Customer Sentiment: Adding data from customer reviews or support tickets can improve your understanding of how happy your customers are, allowing you to adjust your offerings to meet their needs.

By adding these extra layers of data, you can make smart predictions, optimize marketing efforts, and improve product offerings. This is especially useful in machine learning to create more accurate models and improve overall decision-making.

4. Data Transformation and Standardization

Before feeding data into machine learning models, it needs to be in a consistent format. This might mean scaling numbers (like turning ages into a standard unit) or converting categories like "Male" and "Female" into numbers (0 and 1).

Data transformation and standardization are important steps before using data in machine learning models ensuring your data is in a consistent and usable format.

For example, if one column in your dataset says "Age in years" and another says "Age in months," you would need to convert them all to one unit to avoid confusion.

5. Data Anomaly Detection

Anomalies are outliers or strange data points that could throw off your model.

For example, if you are predicting house prices and one house costs a billion dollars, that’s an anomaly. You need to decide if it should be removed or if it’s really a valid data point.

Best Practices for Data Quality Validation

Data quality is critical in machine learning (ML) because the accuracy and performance of your models depend on the quality of the data you use. Poor-quality data can result in faulty predictions. Here are four key practices to ensure your data remains trustworthy and validated.

1. Automating Data Quality Checks

‍Automating checks means your data will always be monitored for issues without constant manual effort. Tools like Markov's AI Workflows once set can automatically verify things like data consistency and completeness. For example, in an online store, automation could quickly spot missing product details like price or category, so you can fix them before they cause errors.

2. Set Clear Data Quality Metrics‍

Having a clear measure of how good your data is can help you keep track of its quality.For example, maybe you want 95% of customer records to have a valid email address. This gives you something to work towards.‍

3. Perform Regular Data Audits

‍Regular audits are like checking in to make sure your data still holds up over time. Doing this can prevent problems from getting worse. For example, if you have a dataset of online reviews, audits can help you catch fake reviews or duplicates before they skew your analysis.‍

4. Set Data Governance Policies‍

Data governance is about making sure everyone in your organization follows the same rules for handling data. It keeps things consistent. For example, a policy might require that all data entries must be checked for correct format, and any changes need to be documented.

By following these best practices, you will ensure your data stays clean, reliable, and ready for ML models to deliver the best results.

Data Quality Validation Check Examples for Machine Learning

When you are working with machine learning (ML) models, it’s essential to check your data for any problems before using it in your models. Poor-quality data can mess up predictions and cause errors. Here are some examples of data quality validation checks you should perform:

1. ‍Outlier Detection: Outliers can mess with your model. For instance, if your financial data shows a $10,000,000 transaction when most are under $100, that's an outlier that should be flagged.‍
2. Missing Values: If you have missing customer info like age or income, you could either remove those records or fill them in with an average value.‍
3. Data Consistency: Ensure that all dates are in the same format (e.g., YYYY-MM-DD) to avoid confusion.‍
4. Schema Validation: This ensures that the data fits the required structure. For instance, if you expect a "Date of Birth" field to only have dates, schema validation will catch entries like random numbers or text.‍
5. Duplicate Detection: Duplicates can introduce bias and affect the accuracy of your model. If you have identical entries for the same customer, you will want to remove or merge them to avoid skewed results.‍
6. Data Range Check: It’s important to check that your data falls within reasonable limits. For example, if you are collecting age data, you wouldn’t expect a value like "age = -5." Checking for these kinds of errors can help prevent weird data from affecting your model’s accuracy.

No Code Solution for Automating Data Quality Checks

Imagine automating data quality checks without writing a single line of code. Sounds great, right? That’s exactly what MarkovML’s AI Workflows let you do! With its pre-built AI operations, you can create workflows for any task, schedule them for repetitive runs, and ensure your data is always clean and reliable. Let’s walk through how you can use MarkovML to detect outliers in a loan approval dataset—step by step.

Step 1: Add Your Data

Head over to app.markovml.com, log in or sign up, and navigate to Workflows from the left menu. Click the blue "+" button to start a new workflow. From the top-left floating bar, choose Inputs and select how you would like to add your data—upload a file, connect to a database, or use a sample dataset.

For this example, select "Use a sample data", this should add a block to the Workflow builder page, as shown below.

Click on it to add the Loan Approval Dataset. Once you click on Apply changes button, you shall be able to preview the dataset as shown below. Once the data is added to your workflow, you are ready to clean and validate it.

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Step 2: Clean Your Data and Perform Outlier Detection

Data quality validation often starts with cleaning the data. For this example, let’s remove any duplicate entries:

1. From the top-left floating bar, go to Operations > Table Structuring Operations and pick Remove Duplicates. This should add it to the existing flow.
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2. Click on the Remove Duplicates block in your workflow and choose the loan_id column to remove duplicate loan records.
   
   

Next, let’s detect outliers:

1. Go to Operations > ML Operations and select Outlier Detection. Click on the newly added block on the Workflow Builder page.
   

2. Choose the columns you want to flag and detect outliers for, like loan_amount, loan_term, cibil_score, and loan_approved. This will flag any unusual rows and add a column called Outlier Flag with scores for each row.
   

Step 3: Get Results

This is where you will store the results of the above operations. Follow the below steps,

1. Go to Outputs from the top-left floating bar and select Send an Email operation as an output action.
2. Click on it to set the recipient, subject, and email body.

Here's how the final workflow looks like. Now, whenever the workflow runs, you will get an email update.

Step 4: Run and Lock Your Workflow

Run the workflow to test if everything works as planned. Click on the Run button on the top right-corner. This will open a pop-up asking you to choose the type of run to run. Choose Sample run to run the flow on a small sample dataset and see the results, as shown below.

Everytime the workflow runs, you will receive an email notification for it, as shown below, as you have used the Send an email operation to store the results.

Click on the Click here button to open the runs details page. To download the result, click on Download results button on the top-right corner.

The downloaded csv file (result) should look something like the image below.

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Happy with the results? Lock your workflow to prevent accidental changes. Just click the three dots in the top-right corner and select Lock .

Step 5: Schedule Runs

Finally, automate the workflow to keep things running on their own. Schedule it to run hourly, daily, or weekly—whatever works for you. Instead of using the sample dataset, connect your live database (like PostgreSQL) to check for anomalies in real-time data.

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And that’s it! In just a few steps, you have built a powerful workflow to ensure data quality and automate outlier detection. The best part? It’s so simple anyone can do it. Why stop here? Explore more ways to make your data workflows smarter and faster with MarkovML!

Conclusion

Data quality validation is crucial for building reliable machine-learning models. By following best practices like data profiling, cleansing, enrichment, and transformation, you ensure that your model learns from high-quality data.

Regular audits and automation are key to maintaining consistent data quality. Make data validation an ongoing process to guarantee long-term success in machine learning. Using no-code solutions like MarkovML’s AI workflow builder can help you automate these quality checks and send alerts in case of anomalies, all without writing a single line of code!

Want to know what more you can achieve using our no-code, drag-and-drop AI workflow? Talk to our experts today!

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