The Power of AI-Enabled Data Validation

Many organizations are sinking financial resources into improved solutions for data validation. This alleviates concerns over risks associated with making decisions based on poor data quality that could result in significant losses —  or even potential company failure.

Part of these investments includes innovating in the space of AI (artificial intelligence). The rapid acceleration of AI-enabled tools in today’s marketplace is because of the incredible benefits they represent in saving time, money, and human assets through automation.

Combining the power of AI with data validation systems and tools is now leading the business world. It is an excellent method to ensure the information used for insights, process optimization, and decision-making is reliable every step of the way.

The Role of Data Validation

When you consider the data management lifecycle, many points along the path of data require clean, verifiable assets for use. Data validation actively checks this gathered information for accuracy and quality, starting at the source all the way to when it is used in reporting or some other form of end-user processing.

The data must be validated before being used. This takes time but ensuring the logical consistency of sourced information helps remove the risk of poor-quality assets being introduced into an organization's tools, systems, and user dashboards.

Every organization will likely have its own unique methods of validation. This could involve something as simple as ensuring the data collected is in the correct format or meets a range for a given processing requirement. Even something as simple as ensuring there are no null values in the sourced information can dramatically affect the final outputs being utilized by stakeholders, clients, team members, and more.

These validation rules could change according to the lifecycle stage or data management process. For example:

• Data ingestion could include rules about ensuring all the data extract routines are complete, timely, and within the expected data volume range.
• Data transformation may involve converting file types, translating data based on business rules, and applying conversion logic to the raw data.
• Data protection may need to separate assets, so only specific users can access certain information.
• Data curation is critical for industries with high oversight or regulatory rules and involves sifting data into various locations based on validation rules.

Why do these data validation systems matter? Today's decision relies on accurate, clear, and detailed data. This information needs to be reliable so that managers, users, stakeholders, and anyone leveraging the data can avoid getting pointed in the wrong direction because of syntax errors, timing, or incomplete data.

 That is why it is critical to use data validation in all aspects of the data management lifecycle.

Of course, these operations become significantly more efficient when AI is introduced into the process. This reduces the chance of human error and uncovers insights that may have never been considered before. While some businesses have leaped AI solutions, others are basing their data systems on various validation methods.

Methods of Applying Data Validation

As data validation becomes more common in business operations, a debate is growing around the methods of ensuring quality outcomes. This may be relevant to the size of the business or the capabilities of an internal team versus outsourcing validation need to a third party.

Whatever the argument, the methods of applying different data validation techniques tend to fall into one of three camps: 

1.  Manual Data Validation

This is achieved by selecting samples or extracts of data along the lifecycle or management process and then comparing them to validation rules. The sample sets represent a larger grouping and should inform the business whether validation rules are being appropriately applied.

 Pros:

• Easy to implement in smaller companies with less complex datasets. 
• Allows for deeper levels of control over the rules and validation techniques. 
• Less expensive because there is no need to invest in modern technology. 

 Cons: 

• Extremely time-consuming and relies on human assets.
• Prone to mistakes due to human error because it is a mundane, repetitive task.
• An error means going back and making a fix, causing significant delays.
• May not catch errors until a user or client has been negatively impacted.

2. Automated Data Validation

This does not necessarily mean an AI-based data validation system. It does mean the capabilities of the validation tools scale enormously because the human factor is removed from the system. That way, more data can be moved through the validation tools at a much more rapid pace.

 Pros: 

• Massive capacity of data flow.
• Allows for redirection of human assets to more creative business needs.
• Allows for logical rules to be introduced without human error.
• Can clean data in real-time instead of after the fact.

 Cons: 

• It may take a long time to integrate a new system into current business operations.
• Often involves working with third-party vendors who have complex pricing models.
• Can be expensive.

3. Hybrid Data Validation

Like its name, a hybrid system of data validation combines aspects of both manual and automated tools. It may speed up procedures and data flow while also having specific areas of data collection being double-checked by humans to ensure adaptive modeling.

No matter which system is introduced into a business, the advent of AI has changed the playing field of data validation. Not only through powerful automation tools but using logical frameworks that can learn and grow according to the needs of the business.

How AI-Enabled Data Validation Is Changing Data Management

Data must be reliable for every end user. Otherwise, there will be no trust in the system, and opportunities for greater efficiency, goal achievements, and valuable insights will be missed. 

Active data observability is one of the operational improvements possible through AI-enabled data validation. This helps companies monitor, manage, and track data throughout their various pipelines; instead of relying on humans who may make a mistake, the process is automated through AI technology for greater efficiency. 

AI is a massive advantage for data engineers who must ensure the information presented is organized and high-quality throughout the entire lifestyle, from source to end products. Having a system in place that monitors, captures, and categorizes anomalies or errors for review ensures a real-time check on the data moving through a company naturally improves the quality of the end data. 

The real advantage of AI is not only in observability but also in self-healing and auto-correction. True, there are plenty of instances when a human needs to step in to repair a validation error. Still, there are numerous instances where leveraging an AI-enabled data validation infrastructure through adaptive routines can drastically improve the processes by removing many of the minor issues in data collection or any other stage of the management lifecycle.

Today’s modern AI tools are capable of being broken down into various data validation processes. This allows intelligent software-enabled routines to rectify and prevent errors based on predictive analytics that only improve over time. The more historical data used to design these routines, the more accurate the prediction of potential errors is because these AI systems can interpret patterns humans cannot discern.