10 AI Tools to Improve Data Quality (and How to Use Them)

Key takeaways

Artificial intelligence (AI) tools for data quality allow you to identify patterns, spot irregularities, and handle data more efficiently. Here are some key facts to know:

• Only 67 percent of leaders trust in their company’s data, casting doubt on their ability to make data-driven decisions [1].

• Hallmarks of quality data include consistency, conformity, completeness, and currency, also known as the “4 Cs.”

• You can use AI tools in various ways to increase workplace productivity, including improving data quality for better results.

Learn more about how to use AI tools to improve data quality, including what they are, the benefits of using them, and some examples to help you find the right options for your needs. If you’re ready to learn more, consider enrolling in the IBM Data Analyst Professional Certificate. During this beginner-friendly, 11-course series, you'll have a chance to become familiar with the latest techniques and tools for handling data, learn how to present your findings after completing data analysis, and complete projects you can use to add to your portfolio.

What are AI tools for data quality? 

AI tools for improving data quality use technology, including machine learning to identify patterns and any irregularities, natural language processing to handle unstructured data like that you’d find in emails or posts on social media, AI-driven automation to handle cleaning, validation, and other critical tasks. These tools help streamline data processes and increase quality, something that could potentially save businesses significant amounts of money over time. 

After all, the results you get from using data are typically only as reliable as the data you use, which is why data quality matters. Using high-quality data drives critical decisions, insights, and accurate analysis. Incomplete, inaccurate, and outdated data can hinder decision-making, decrease operational efficiency, and adversely impact a company’s bottom line. 

What are the four Cs of data quality?

The four Cs of data quality refer to consistency (statistical validity), conformity (adheres to standards with matching values), completeness (no values are missing), and currency (updated frequently). You might also hear a slightly expanded version referred to as the six “pillars.” They include:

• Accuracy: Representative of real-world values

• Completeness: Contains all required data values

• Timeliness and currency: Up-to-date at the time of use

• Consistency: Formatted for compatibility across data sets

• Uniqueness: Free of duplicates

• Data granularity: Has the appropriate level of detail for the intended use

Benefits of using AI tools for data quality

According to 2025 Outlook: Data Integrity Trends and Insights, 67 percent of professionals surveyed don’t completely trust their organization’s data for decision-making, with 64 percent citing data quality as their primary challenge [1]. This doubt can erode confidence in data-driven insights and decisions, which using AI tools for data quality improvements can help restore. 

Additional benefits include: 

• Spotting data trends human eyes may miss

• Providing more dependable, accurate data to drive better decision-making

• Learning over time for continual data quality management improvement

• Ensuring all departments have access to relevant quality data for operational efficiency

• Evolving with the data and emerging data quality rules

• Improving marketing, customer segmentation, and personalization abilities for greater customer satisfaction

10 AI tools to improve data quality

Using AI-powered tools to improve data quality helps speed the process up while reducing errors. It improves scalability, enhances accuracy, and restores trust in data, ultimately helping to drive data-driven decisions and strategies. To begin, you need to choose the right tools for the job, which may include tools for data cleaning, validation, deduplication, and more. 

Explore 10 powerful AI tools for data analysis, better insights, and superior data quality.

Data cleaning tools

Starting with clean data is essential for data science applications of all types because it improves the quality and helps ensure you’ll be able to convert it into usable formats. AI can aid in the process, often performing actions like analyzing source data to ensure consistency, standardizing formatting, and enforcing relevant data cleansing rules to aid data management professionals in their data cleansing efforts. Tableau Prep, for example, harnesses the power of generative AI in helping with data cleaning with the integrated Tableau Agent. 

Data validation tools

Data validation is a critical process to ensure that data is correct and meets the necessary standards. These tools use AI to check incoming data to make sure it’s the correct type, follows formatting rules, falls within a specified range, is consistent, and the system hasn’t entered it more than once. AI-powered data validation tools like Numerous and Informatica help reduce errors, increase data security, and support consistency across systems and sources. 

Data deduplication tools

Data deduplication helps optimize organizations’ storage space by reducing redundant data and removing extra copies, leaving only one instance of the data in the system. Data deduplication tools like Sisense that integrate AI and machine learning help with outlier detection, keeping query response times swift for faster decision-making, standardizing data in a unified format, while minimizing the odds of false results and other data errors. 

Data enrichment tools

Data enrichment refers to the practice of making data more usable by providing additional context for improved understanding. You typically achieve this through a process of adding more internal or external data to the mix, helping existing data points become even more powerful. Breeze Intelligence, which integrates with HubSpot CRM or Pipl, which works well with social media data, help enhance data accuracy, integrate with other platforms, and make fast work of the data enrichment process. 

Data monitoring and anomaly detection tools

Identifying anything that doesn’t belong in a data set with AI-powered data monitoring and anomaly detection tools can help keep digital systems performing efficiently, It can also reduce the chances of system errors, and maintain more robust security measures. 

Given the volume of data that organizations typically handle, traditional methods of monitoring and detection rarely provide the superior results you can achieve with AI. For example, tools like Nile Secure or Azure’s AI Anomaly Detector can support real-time processing of vast data sets, use predictive analytics to anticipate anomalies based on data trends, and use pattern recognition for efficient detection of any deviations from the norm. 

Master data management (MDM) tools with AI

Master data is one of several types of data businesses handle, and refers to the data surrounding the organization’s critical entities, including customers and locations. It often stems from customer relationship management (CRM) and enterprise resource planning (ERP) applications, for example, and without proper management, can become fragmented and outdated. 

MDM tools help ensure more uniform, accurate, consistent master data. Tools like Informatica Intelligent MDM use AI to help correctly merge records and prevent duplicates or inaccurate information. It can also help you locate data, resolve irregularities, and automate modeling quickly and efficiently while enhancing scalability and your ability to handle large amounts of data.

Data governance and compliance tools 

The policies and procedures that a company puts into place to create standards for how they collect, process, store, and use data are essential for overall data integrity. Data governance helps ensure the data is safe and adheres to quality standards, which supports and enhances usability, using AI to handle the ever-increasing volumes of data businesses take in every year. Tools like LifeBit.AI help enforce policies automatically, use predictive compliance to anticipate violations before they happen, and use real-time monitoring to spot data quality issues efficiently. 

Data integration tools with AI

When completed efficiently, data integration helps bolster data quality, reduce information technology (IT) costs, and enhance your ability to handle and interpret data. Manual data integration can limit scalability, use intensive resources, and silo data, which can inhibit your ability to gain a unified view of data across the organization. AI-driven integration tools like Rivery and IBM DataStage increase efficiency, minimize errors, and automate processes like discovery, ingestion, and mapping.

Data preparation for machine learning

As much as 80 percent of the time you spend on a machine learning project could be devoted to data preparation [2]. Optimizing the process is essential for saving time while still making sure you have high-quality data to use for building machine learning models. Tools like Amazon SageMaker can simplify data preparation, reduce the costs of labeling data, and minimize time spent preparing data for machine learning, using AI to collect and clean, as well as label raw data, and transforming it into machine learning-friendly formats. 

AI-powered data catalogs

AI-powered data catalogs use a combination of automation and AI to manage data in one collaborative space, helping to streamline data management and improve accessibility. It’s like the hub of your data universe, bringing it all together for a unified view. You can choose from various options, each with its own unique strengths. For example, you might select IBM Watson Catalog for data lineage, Google Dataplex Universal Catalog for seamless integration with Google’s cloud-based services, or Alation for user behavior analysis, or erwin for data modeling. 

How to use AI tools to improve data quality

Before you can begin using AI tools, it’s essential to choose options that meet your organization’s needs. Consider the challenges you need to overcome. For example, if you’ve noticed bottlenecks and excessive time taken in the data cleaning stage, you might choose an AI-powered data cleaning tool. 

From that point, consider the tool’s features and how it addresses various challenges. Real-time monitoring, scalability, and accuracy are a few of the standout features to look for. Some additional tips for getting started include the following:

• Assess your current data framework: Identify the pain points and infrastructure you already have in place. Then, think about the integration requirements AI tools will need to meet and your organization’s compliance requirements. 

• Evaluate key criteria: As you compare your options, look at scalability, technical requirements, and costs to identify AI tools that will meet your needs. 

• Establish key performance indicators: Once you’ve implemented AI tools, measuring their performance can help ensure you get the intended results and a positive return on investment (ROI). Look for accuracy, timeliness, consistency, and completeness, which are just a few of the indicators that the tool is producing results. 

• Maintain some human oversight: Get feedback from end users and monitor data quality consistently to reduce the chances of bias or incorrect data-driven insights.

Explore free resources to continue learning about AI and data quality

AI and other emerging technologies are evolving quickly. Keep up with trends and industry changes, along with how AI can shape your professional goals, by subscribing to Career Chat, our weekly LinkedIn newsletter. You might also consider exploring our other free resources:

• Watch a YouTube video: What is Data Wrangling? The Key to Clean Data

• Hear from an expert: Reimagining Work and Learning with AI: Expert Insights from Dr. Jules White

• Sharpen your vocabulary: Artificial Intelligence (AI) Terms and Definitions

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