The beginner’s guide to data validation

Can you trust your data?

Data validation isn't just about accepting data; it's about making sure your data is trustworthy, consistent and aligned with your standards. Having strong data validation processes in place is like having an incredibly meticulous gatekeeper who checks every single piece of data entering your system.

By the end of this article, you’ll know:

What is data validation?

Why is data validation important?

What does data validation do?

What steps are involved in data validation?

Best practices in action

What happens if you skip data validation?

Who uses data validation?

You don't have to do this yourself

What is data validation?

Businesses rely on data to make informed decisions and gain a competitive edge, but the volume of data being generated and processed today presents a challenge: Ensuring that data is trustworthy. Data validation is the process of verifying the accuracy, consistency and reliability of data. It involves checking data against predefined criteria to ensure it meets quality requirements.

Data validation works like a series of checkpoints along a data journey. At each checkpoint, data undergoes scrutiny, with validation rules acting as the criteria for acceptance or rejection. These rules can cover everything from basic data types (like numbers or dates) to complex business logic that dictates what's valid and what isn't.

Why is data validation important?

Why does data validation matter so much? Well, imagine you have sales reports that are riddled with duplicate entries, incorrect figures, formatting problems and missing information. If you went ahead and made decisions based on that kind of flawed data, you’re probably going to be facing missed forecasts, lost opportunities and bad business plans.

It's not just about having data; it's about having the right data — the kind you can trust to steer your business in the right direction. Data validation turns your data into a reliable compass that guides you toward informed decisions and actionable insights.

Data validation ensures that the data you collect, process, and analyze isn't just a jumble of numbers and letters but a coherent, reliable narrative that informs your decisions and actions. 

It helps you ask the right questions of your data, like:

• Is this data accurate and free from errors or discrepancies that could mislead me?

• Does it adhere to the expected format, structure, and standards I've set?

• Is it consistent across different sources?

• Does it align with the business rules and logic that govern operations and strategies?

By using data validation to answer these questions, you can transform your data from a bunch of information into a trustworthy and reliable asset.

Data validation is crucial for:

• Data accuracy: Validating data ensures that it is reliable and free from errors, inconsistencies, and duplicates. Accurate data forms the foundation for reliable analysis and decision-making.

• Data integrity: Validating data helps maintain data integrity by preventing the entry of invalid or inappropriate data, which could compromise the overall quality and reliability of the database or system. It upholds data quality standards, enforces data constraints, and prevents data corruption or loss.

• Compliance: Many industries and organizations have regulatory requirements or standards for data quality and integrity. Data validation helps ensure compliance with these standards and reduces the risk of penalties or legal issues. 

• Decision-making: High-quality data enables organizations to make informed decisions, identify trends, uncover insights and drive business growth.

What does data validation do?

Data validation makes sure your data is accurate and consistent. It’s used in databases, spreadsheets, web forms and other software applications, and it ensures that data entered into a system is accurate, consistent and meets certain standards. 

Data validation can check for:

• Accuracy: Data validation checks for accuracy by verifying that the data is valid and reasonable. For example, it can ensure that a date entered falls within a specific range or that numeric data is in the correct format.

• Completeness: It ensures that all required data fields are filled in and no essential information is missing. This helps prevent incomplete records or data sets.

• Consistency: Data validation ensures that data is consistent across different fields or records. For instance, it can verify that a product code matches its corresponding product name.

• Format: It checks that data is in the correct format, such as validating email addresses, phone numbers, dates or postal codes to ensure they follow the expected structure.

• Rules: Data validation can enforce rules like limits or ranges within a set of defined values. This could include a defined list of colors, for example, which will ensure that values are within acceptable boundaries.

• Duplicates: It can identify and prevent the entry of duplicate records, reducing redundancy and maintaining data integrity.

What steps are involved in data validation?

It’s very straightforward! 

Data validation typically involves several steps:

1. Define validation requirements: Determine the specific validation requirements based on the type of data, business rules, regulatory standards and user expectations. Identify the validation criteria and constraints that data must meet to be considered valid.

2. Data collection: Collect the data that needs to be validated from various sources, like databases, files, forms or external systems. Ensure that the data collection process captures all relevant information and attributes required for validation.

3. Data cleaning and preprocessing: Before validation, perform data cleaning and preprocessing tasks to address issues like missing values, duplicates, inconsistencies and data formatting errors.

4. Select validation methods: Choose appropriate validation methods and techniques based on the nature of the data and validation requirements. 

5. Implement validation rules: Implement validation rules and logic to check the data against predefined criteria and constraints. Validation rules may include checks for data type, format, range, length, uniqueness, referential integrity and business rules. Ensure that validation rules are clear and consistent across the data set.

6. Perform validation checks: Execute validation checks on the data using the selected validation methods and tools. Validate data at different stages, like data entry, data import/export, data processing and data storage to ensure accuracy and consistency throughout the data lifecycle.

7. Handle validation errors: Handle validation errors that occur during the validation process. Provide informative error messages or notifications to users indicating the nature of the validation error and guidance on how to correct the data and implement error-handling mechanisms to prevent invalid data from being processed or stored.

8. Review and verify results: Review the results of data validation checks to identify and resolve any issues or discrepancies. Verify that the validated data meets the validation criteria and requirements specified in the first step and conduct data profiling and analysis to assess data quality and identify areas for improvement.

9. Document validation procedures: Documenting validation procedures helps ensure consistency and transparency in data validation practices. Document the procedures, rules, outcomes and any corrective actions taken and maintain your documentation for future reference, auditing and compliance purposes. 

10. Monitor and maintain data quality: Continuously monitor and maintain data quality by regularly performing data validation checks and quality assessments. Implement data governance practices, data quality controls and best practices to sustain high-quality, reliable data over time.

By following these steps, you can establish effective data validation processes that improve data quality, integrity and usability for decision-making and business operations.

Best practices in action

This doesn't have to be too complicated. When you're planning your data validation process, imagine implementing these best practices:

• Start by understanding your data requirements inside out and setting clear validation goals

• Leverage automation to streamline validation processes

• Handle errors at every step, guiding users toward data accuracy instead of frustration

• Document your validation procedures, creating a trail of accountability and learning

• Regularly monitor your data to keep data quality in check

What happens if you skip data validation?

Without validation processes in place, you risk making decisions that are based on inaccurate or incomplete data. This will probably lead to increased operational costs, compliance issues and reputational damage.

If you skip or make mistakes implementing data validation, you can have problems like:

• Inaccurate data: Without validation, there's a higher risk of inaccurate data entering the system. This can lead to errors in reports and decision-making because they’re based on flawed information

• Data integrity issues: Invalid or inconsistent data can compromise the overall integrity of the database or system. It could cause data conflicts, corruption or other integrity issues that make it unreliable or unusable

• Poor data quality: Data quality deteriorates when validation is lacking. This includes issues like missing values, incorrect formats, duplicates and incomplete records

• Increased errors: Without validation checks, errors are more likely to occur. These errors can be much more time-consuming and costly to fix later in the data lifecycle

• Decreased efficiency: Dealing with invalid or inconsistent data consumes resources and time. It requires manual effort to identify, correct and reconcile data issues

• Compliance risks: Many industries have regulatory requirements or standards regarding data quality and integrity. Failing to validate data can result in non-compliance, which may lead to penalties, legal issues or reputational damage

• Impact on decision-making: Inaccurate or incomplete data undermines the reliability of decision-making processes. At the very least, unreliable data can cause you to make flawed decisions and miss opportunities

Ignoring or neglecting data validation can have far-reaching consequences, affecting data quality, system integrity, operational efficiency, regulatory compliance and ultimately, the ability of an organization to make informed decisions and achieve its goals.

Who uses data validation?

Pretty much anyone who needs to be able to rely on their data! Here are some examples:

• Data analysts: Data analysts rely on data validation techniques to ensure the accuracy and reliability of the data they analyze. Validated data provides a solid foundation for generating meaningful insights and making data-driven decisions

• Database administrators (DBAs): DBAs are responsible for maintaining database systems and ensuring data integrity. They use data validation methods to enforce data quality standards, prevent data corruption and optimize database performance

• Software developers: Developers integrate data validation mechanisms into software applications, databases and web forms to validate user input, enforce data constraints and improve overall data quality

• Business users: Business users, including managers, executives and operational staff, rely on validated data for reporting, performance tracking, forecasting and decision-making. They need accurate and reliable data to support their business activities

• Compliance officers: Compliance officers ensure that organizations adhere to regulatory requirements and industry standards related to data quality, privacy and security. Data validation is essential for demonstrating compliance and mitigating regulatory risks

• Data scientists: Data scientists use data validation techniques in machine learning and data modeling projects as part of the data preprocessing phase. Validated data enhances the accuracy and effectiveness of predictive models and analytical algorithms

• Data entry operators: Data entry operators input data into systems or databases. They rely on data validation checks to ensure that the data they enter meets predefined standards and guidelines, reducing errors and improving data quality from the start

• Quality assurance (QA) teams: QA teams in software development and data management verify that data validation rules and processes function correctly. They conduct testing and validation procedures to identify and address any issues with data integrity and validation logic

Data validation is a collaborative effort that involves multiple stakeholders, including data professionals, developers, business users, compliance officers and quality assurance teams, to ensure that data is fit for use across the organization.

You don't have to do this yourself

If all of this seems overwhelming, remember that you aren't required to implement data validation processes on your own! There are tools and solutions that can handle the data validation process for you, and they often include powerful automations and AI-enhanced features that can streamline your entire data integration process. Flatfile, for example, can even help you ensure that data that enters your systems from external sources via data files (e.g., CSV, XLS, PDF, TXT, XML, etc.) is validated and cleaned before it enters your database. You can rely on a high degree of automation and built-in intelligence, and your users and team members can validate, correct and import data with immediate feedback, ensuring high-quality and up-to-date data is available to everyone. 

With Flatfile, validations, transformations and custom actions are all code-based, giving you complete control of how you want your data. You and your users can review, filter and correct data to your desired format and structure with speed and ease. You don't need to worry about accidental alterations; you're in command.

Data validation isn't just a technical process; it's a mindset. It's about instilling confidence in every stakeholder who relies on your data, whether executives making strategic decisions, analysts uncovering insights or customers interacting with personalized experiences.

By having strong data validation processes in place, embracing automation wherever possible and monitoring data quality metrics, you can fuel innovation and build resilience in the face of ever-evolving challenges.

Flatfile has helped hundreds of enterprise clients (and plenty of scaleups) tackle their data validation challenges, supporting just about any business or data requirement. Reach out and connect with one of our data experts to find out how Flatfile can help you address your data onboarding use case and requirements.