Four Data Validation Techniques to Improve Data Quality

Bad data is a costly problem—organizations lose around $12.9 million each year because of it.

Beyond the revenue hit, poor-quality data adds layers of complexity to data ecosystems, disrupts processes, and complicates decision-making. When decision-makers can’t trust the data, it hinders planning and impacts everything from customer satisfaction to operational efficiency.

Data validation is what keeps data in check. It catches errors before they spread, so your data stays accurate and useful. With data validation checks, companies sidestep costly mistakes and operate with confidence.

Here, we’ll look at the most effective types of data validation to improve your data’s quality. Let’s look at each method in detail.

1. Schema validation

Schema validation ensures each piece of data conforms to a certain structure before entering your systems. This structure, or schema, outlines what each data field should look like in terms of type, format, and length.

For example, if a database holds customer records, this check would confirm that phone numbers contain only digits, names are text strings, and email addresses follow a pattern (contain “@” and end with a domain).

As you enforce schema validation early, you cut down on data discrepancies and errors, which in turn reduces the costs of later data cleaning and minimizes inaccuracies in analytics. It’s also a great way to keep data governance standards consistent across different teams.

Key elements of schema validation

Schema validation checks several critical elements, especially when working with varied data sources. Here’s what it focuses on:

• Data types. Each field in a dataset has a designated type—integer, float, string, or date. Schema validation enforces these types, so you won’t run into trying to perform arithmetic on a text field.
• Field constraints. Some fields are mandatory. Others have rules. For example, fields may be constrained by length, pattern, or range.
• Structure consistency. This is especially important if you deal with data from multiple sources. This method of data validation keeps every dataset aligned with a set structure, requiring fields to follow the same hierarchy and order.

Implementation techniques

Schema validation relies on a combination of schema definition languages and automated validation tools.

Schema definition languages (Avro, JSON Schema, and Protocol Buffers) provide a formal way to define what valid data should look like. For example, JSON Schema is widely used for API data or JSON files. It lays out the properties, types, and optional or required status of each field. In big data environments, Avro and Protocol Buffers are commonly used with Apache Kafka or Hadoop. These formats define schemas and allow for compact, structured data serialization.

To support this setup, automated validation tools (Great Expectations and Apache Arrow) are also engaged. These tools validate incoming data against predefined schemas, whether in real time or during batch processing. By identifying schema violations as they occur, they streamline ETL workflows, allowing you to catch and flag problematic records immediately.

Schema evolution

Over time, you may need to add new fields, update field formats, or restructure certain data elements. The utmost goal here is to manage these changes so your data pipelines don’t break every time something shifts.

With schema evolution policies in place, you maintain backward and forward compatibility, ensuring new updates won’t disrupt existing data flows.

To make this happen, you may use version-controlled schemas. Take Kafka pipelines, for example. This way, you enable both producers and consumers to handle different schema versions without conflict.

Challenges and limitations

While schema validation method is essential, it’s not without its challenges.

First, dealing with schema evolution is tough. Business needs change constantly, and that means data requirements do, too. Adding new fields, changing data types, or tweaking structures can cause all sorts of compatibility headaches. For example, older data may not fit new rules, or downstream systems might break when they encounter a new schema format.

Also, with data coming from multiple sources, each one often has its own structure, making it tough to create a unified schema. Complex structures (nested JSON or arrays) add another layer of difficulty—each nested level needs specific checks, and that means more maintenance and processing power, especially as data scales.

Handling conditional fields isn’t easy either. Standard schema validation is rigid, so when fields are conditionally required, it doesn’t handle these scenarios well. To get around this, custom logic is often necessary, which can increase both complexity and the chance for errors.

2. Data type checks

Data type checks confirm that every entry in your system aligns with its intended format. They verify that numerical fields hold only numbers, date fields contain valid dates, and text fields don’t mix in symbols or digits where they don’t belong.

Let’s see how it works.

You have a column labeled “Price” in a dataset, where each entry should be a decimal or integer. With this check, you would review this column to ensure every entry is indeed a number. If any entry is found to be text (“free” or “N/A”), the system would flag it as an error.

Data type validation types

When data comes from multiple sources or undergoes complex transformations, enforcing correct data type validation reduces processing errors and improves the quality of insights derived from that data. Let’s break down the main check types that businesses rely on.

• Primitive data type checks. These ensure data entries align with the simplest data types: integers, floats, strings, booleans, and dates. For instance, if a column is designated for numeric entries, this check will flag any non-numeric values.
• Structured data type checks. When data involves more complex structures—arrays, lists, or JSON objects—these checks verify the outer data type is correct and ensure each component within the structure adheres to expected subtypes. For example, in a nested JSON object with customer data, a structured type check will confirm that all fields within that structure—“age” (integer) or “location” (string)—are consistent.
• Format-specific checks. These checks ensure fields meet formatting standards. For example, a date field adheres to a “YYYY-MM-DD” layout or a phone number follows a predefined pattern. 

How to validate data with data type checks

Here’s a look at implementation techniques for enforcing data types.

Automated type validation

Automated type checks within ETL pipelines verify data at the ingestion stage, so you catch issues before data flows into storage.

Here, use Apache Spark, Apache NiFi, Airflow, and AWS Glue to enforce schemas across each field.

For example, in Spark, you define a DataFrame schema that assigns a type (IntegerType or DateType) to each column. Spark enforces these definitions during ingestion, catching any mismatches.

Database-level constraints and type enforcement

In structured databases, type validation happens directly within the schema. When you define a table in PostgreSQL, MySQL, or Oracle, you set types for each column—INTEGER, VARCHAR, or DATE.

This database validation approach ensures strict control, as the database automatically rejects data that doesn’t match the specified type.

For instance, defining a column as DECIMAL will reject any text entry. For transactional data, this level of enforcement guarantees data integrity at the source.

Challenges and limitations of this type of data validation

When managing data type validation, there are some unavoidable challenges that can disrupt even the best-designed systems.

Ambiguous data types can be a real headache. Take a “price” field, for example—it’s expected to be a number, but sometimes it comes in as a string due to formatting quirks, like “$123.” This inconsistency complicates type checks, especially when numeric fields contain non-standard characters—currency symbols or commas—that need to be stripped out before validation.

For more complex data structures—nested JSON objects or multi-dimensional arrays—enforcing type consistency requires a more sophisticated approach. Each nested level, whether an array of objects or multiple fields within a JSON object, needs to match the expected structure and type. This level of complexity quickly outstrips the capabilities of basic type checks, demanding tools with built-in schema support.

3. Cross-field validation

Cross-field validation confirms that related fields have no contradictions. This approach helps catch logical errors that individual field checks can miss.

Here is a data validation example.

You have a dataset with an Order Date and a Delivery Date field. For every order, the Delivery Date should be on or after the Order Date. A cross-field validation check would compare these fields and flag any record where the Delivery Date is earlier than the Order Date.

Types of cross-field validation

Here are some of the most common types of cross-field validations that help keep data consistent and reliable:

• Relational consistency
• Conditional dependencies
• Hierarchical consistency

Let’s look at these database validation types in more detail.

Relational consistency

The most common cross-field checks that involve looking at fields with clear, logical relationships. Here you may verify:

• Date relationships to check date sequences. For instance, an order date should always come before a delivery date, or a contract end date should never be earlier than the contract start date.
• Range comparison for checking fields with minimum and maximum values. For example, if a product’s min price exceeds its max price, it’s clearly incorrect and needs correction.

Conditional dependencies

Some fields are only necessary or meaningful when certain conditions are met. Conditional dependencies help keep data focused and relevant by ensuring fields correlate based on each other’s values.

• Status and date correlation. In status-driven workflows, a completion status should correlate with a completion date. If an order is marked as completed, there must be a completion date provided. Conversely, if an order is still active, a completion date would make no sense and should be left blank.
• Category-dependent fields. Similarly, certain fields only apply to specific categories. For example, a discount field is only relevant for items on sale. If a product is marked on sale, a discount value should be entered. If it’s listed at regular price, the discount field should be empty.

Hierarchical consistency

For data with hierarchical relationships, cross-field validation ensures related fields make sense within the broader data structure. This is especially important for companies managing large inventories or service catalogs.

If a product has both a parent category and a subcategory, for instance, the two need to align within the organization’s established hierarchy. If these fields don’t match, it might indicate an error in data entry or an outdated hierarchy, both of which can mislead analytics and customer-facing applications.

Implementation techniques for cross-field validations

Implementing cross-field validations requires thoughtful planning and the right tools, as these checks often involve complex dependencies between fields. Here are effective techniques for integrating cross-field validations into your data pipeline.

• Rule-based validation embedded in ETL pipelines. Configure Apache Spark, Airflow, or AWS Glue to apply rules that validate field dependencies during data processing.

Example: In Spark, write transformation code that checks if an order date comes before a delivery date. If the rule fails, the system can log the discrepancy or send the record to an error queue.

• Schema validation with data validation frameworks. Great Expectations and Cerberus make it easier to implement cross-field checks by allowing you to define complex relationships in a schema.

Example: Great Expectations provides a way to set “expectations” between fields. For instance, set expect_column_pair_values_A_to_be_greater_than_B for ensuring min values are less than max values. This framework flags inconsistencies during batch processing, generates validation reports, and allows you to customize how errors are handled.

• SQL constraints and queries. For structured data in relational databases, SQL enforces cross-field rules within transactions or in reporting queries.

Example: Use SQL CHECK constraints to enforce logical conditions directly on the database level. For instance, adding a CHECK constraint that ensures end_date >= start_date.

Challenges of this data validation process

Because these validations focus on relationships between fields, they require a more complex setup than simple field-level checks.

One of the primary challenges is the complexity of rule configuration. Each rule needs its own logic, which can make setup more complicated, especially when you’re dealing with large datasets full of dependent fields. And because these rules interact, a mistake in one can affect others, so testing and refining the setup takes extra care.

Then there’s the issue of performance. Cross-field checks need more processing power since the system has to examine multiple fields at once. In real-time environments, this can slow things down. Even in batch processing, which might be less time-sensitive, these checks still demand extra time.

Conditional dependencies add another layer of complexity. For instance, a field might only be relevant when another field meets certain criteria. When conditions vary, it’s easy to end up with false positives or overly complex rules that slow down processing.

4. Data anomaly detection

Data anomaly detection is used to identify patterns or values in a dataset that deviate from the norm. Instead of checking specific rules, as with traditional validation, this one uses algorithms or statistical methods to flag unusual data points. These anomalies indicate anything from data entry errors to potential fraud. There are:

• Point anomalies. Single data points that stand out due to differing values (e.g., a one-time spike in server traffic or a negative price in sales data).
• Contextual anomalies. Data points that seem unusual only within a specific context, often time-related (e.g., a spike in website traffic during non-peak hours).
• Collective anomalies. A sequence of data points that may individually appear normal but, when viewed together, indicate an anomaly (e.g., a pattern of increasing errors over time in a data pipeline).

This technique is commonly used in finance, healthcare, and manufacturing, where small deviations can indicate significant issues or risks.

Imagine this in a healthcare setting. Hospitals monitor patient vitals constantly, tracking heart rate and blood oxygen levels. An anomaly detection system will compare new readings to what’s typical for each patient. So, if a patient’s heart rate suddenly spikes without explanation, the system flags it.

Anomaly detection techniques

Selecting the right anomaly detection technique depends on the dataset, the type of anomalies you’re looking for, and the level of complexity required. Commonly speaking, there are:

• Statistical methods rely on calculating averages, standard deviations, and ranges to identify values that fall outside expected thresholds. This approach works well for datasets with clear, consistent patterns, where anomalies are values that don’t fit the expected range. However, it’s limited for complex datasets that don’t have a straightforward “normal” range or when patterns shift frequently over time.
• Machine learning models bring more flexibility by learning what’s typical in a dataset and adapting over time. Unsupervised models (clustering algorithms) are especially useful. K-means clustering, for example, groups data points based on similarity, allowing the model to flag any data points that don’t fit into a cluster as potential anomalies. Isolation Forests work by isolating each data point, with anomalies being those points that require fewer splits to isolate. Supervised learning models, where the algorithm is trained on labeled data (known normal and anomalous cases), can be even more accurate. However, they require a labeled dataset. Machine learning techniques are ideal for complex, high-volume datasets, like those used in financial fraud detection, where anomalies are dynamic and hard to pinpoint with simple rules.
• Time-series analysis works for data that flows in a sequence—stock prices, website traffic, or patient vitals. Basic techniques (moving averages and seasonal decomposition) help detect sudden shifts, spikes, or drops that don’t fit the expected trend. Advanced time-series models—ARIMA (Auto-Regressive Integrated Moving Average) and LSTM (Long Short-Term Memory) networks—identify patterns over time, predict values based on historical data and flag deviations as anomalies. Time-series analysis is particularly useful for applications where data needs to be continuously monitored.
• Proximity-based techniques calculate the “distance” between data points. In k-nearest neighbors (KNN) anomaly detection, a point is considered an anomaly if it’s too far from its closest neighbors. Similarly, DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is a clustering method that groups dense regions of data points and identifies outliers as those that fall outside these dense clusters. Proximity-based methods are well-suited for datasets where similar data points are expected to cluster together. However, these methods struggle with high-dimensional data or when the dataset is too sparse to form clear clusters.

Challenges of anomaly detection

While spotting outliers prevents bigger problems down the line, setting up and maintaining effective anomaly detection isn’t always that simple.

For starters, not all data behaves the same way. Different datasets need different approaches, and it’s hard to find a one-size-fits-all solution. For example, what works to catch anomalies in time-series data, might not work at all for categorical data or things with complex patterns. 

Another big hurdle is distinguishing between true anomalies and natural variations. Not every spike or deviation is a sign of a problem, and sometimes the algorithms can be too sensitive, flagging “false positives” that are actually just normal fluctuations. For instance, in seasonal data—website traffic around the holidays—patterns change temporarily, and an anomaly detector might flag this as unusual.

Data quality itself can also be an issue. Anomaly detection relies on historical data to recognize what’s normal. But if that historical data has its own inaccuracies, the detection model can learn from the wrong patterns. Poor-quality data skews the baseline, making the system either too relaxed or overly sensitive. For machine learning models, which learn from past data, this means poor data quality can lead to poor predictions.

Wrapping it up

Adopting validation techniques—data type checks, cross-field validations, and anomaly detection—you ensure your data is reliable and ready for smart decision-making.

But at first, take a close look at your current validation processes. What areas need more attention? Think about bringing in automated tools for your ETL pipelines. Don’t overlook the importance of ongoing monitoring. Set up alerts for data mismatches or anomalies. Regularly review and refine your validation rules to adapt to changing data landscapes and business needs.