The Role of ML and AI in Data Quality Management

Ensuring high-quality data is imperative for every organization, but did you know the role of ML and AI in data quality management? That’s right, many of today’s sophisticated data quality management tools utilize advanced machine language (ML) and artificial intelligence (AI) technology to identify poor-quality data and make it cleaner. ML and AI help to automate previously manual processes and can clean thousands of records in mere seconds. 

In an era where 77% of IT decision-makers don’t trust the quality of their organizations’ data, improving data quality is a mission-critical task. How ML and AI work together to automate data quality management is a fascinating use of new technologies—and one that can benefit your organization.

Quick Takeaways

• Machine learning uses data and algorithms to emulate the way that humans learn
• Artificial intelligence attempts to develop intelligent machines and computer programs
• ML and AI can work together to improve the process of data quality monitoring
• ML/AI-based systems can automate data capture, reduce errors, identify duplicate data, complete missing data, and validate data accuracy

Using ML in Data Quality Management

What is machine learning? IBM defines machine learning (ML) as a branch of computer science that uses data and algorithms to emulate the way that human beings learn. ML is closely related to artificial intelligence in that by “learning” with repeated use, it gradually improves its accuracy. 

Unlike traditional computer software that is programmed to function in a very specific fashion, ML software learns and adapts based on the data it receives. As it gains exposure to and experience with a given activity, such as monitoring data quality (DQ), it adapts the way it “thinks,” getting “smarter” over time. In essence, ML learns how human beings learn through trial and error and many experiences. 

Image Source

Because ML learns as it goes, it’s quite useful for monitoring and improving DQ. In particular, DQ management tools employ ML models to:

• Learn from and find hidden patterns in large volumes of data
• Automatically edit nonstandard data to conform to specific formats or standards
• Evolve and create new DQ rules as the data evolves

ML, in conjunction with AI, also enables autonomous data quality monitoring. ML and AI technologies work together to identify data errors without human supervision. An ML/AI-driven solution is also capable of establishing new DQ rules and performing sophisticated validation checks, all without manual intervention. 

(The following video explains the differences between ML and AI.)

Using AI in Data Quality Management

Artificial intelligence (AI) is a close relative of ML and often works in tandem with that technology. IBM defines artificial intelligence as the science of making “intelligent machines.” It isn’t necessarily making machines that think like humans because humans don’t always think or behave logically. Instead, it’s about making machines or computer programs that think and act rationally, without human direction, in conjunction with ML. 

AI is used in a growing number of applications today. DQ management tools employ AI and ML in several different ways. It’s all in intending to improve data quality because poor DQ affects data analytics and the ability of companies to make informed decisions. 

Image Source

Automating Data Capture

Gartner estimates that the average enterprise loses $12.9 million annually because of poor quality data. Much of this problem occurs at the data capture stage. 

AI-automated data entry and ingestion can improve data quality. Using intelligent data capture, AI systems identify and ingest data without manual intervention, ensuring that all necessary data inputs have no missing fields. 

Reducing Errors

When human beings enter or edit data, they risk introducing human errors. However, AI-mediated data activities virtually eliminate these errors. AI-based systems do not make mistakes, so no new errors are introduced into your data.

Detecting Data Errors

Even the smallest error in a data set can affect that data’s overall quality and usability. AI is quite effective at identifying data errors. Unlike manual data monitoring, which relies on error-prone human beings to find every error (which they often don’t), AI systems don’t let any errors slip by. 

Identifying Duplicate Records

AI is also effective at identifying duplicate records. Duplicative data is an issue when data comes from multiple sources. You might, for example, have the same customer in multiple databases. AI quickly identifies duplicate records and intelligently deduplicates them by either merging or deleting the duplicates while keeping unique information from each record—all without manual intervention. 

Validating Data

You can validate much of the data in your system for accuracy by comparing it to existing data sources. For example, you can compare customer addresses to the same addresses in the USPS database. AI makes this task easier by automatically validating all known data. 

Even better, AI and ML systems can learn existing data rules and predict matches for new data entered. When a given record doesn’t match the predicted value, AI automatically flags it for evaluation, editing, or deletion.

Filling in Missing Data

While many automation systems can cleanse data based on explicit programming rules, it’s almost impossible for them to fill in missing data gaps without manual intervention or plugging in additional data source feeds. However, machine learning can make calculated assessments of missing data based on its reading of the situation.

Supplementing Existing Data

AI can sometimes improve data quality by adding to the original data. AI does this by evaluating the data and identifying additional data sets that can expand on the original data. AI is particularly effective at identifying patterns and building connections between data points.

Accessing Relevance 

Just as AI can suggest supplemental data relevant to the original data set, it can also identify data within the data set that is no longer relevant or useful. By identifying irrelevant data points, AI can help revamp the data collection process, simplifying it and making it more efficient. 

Scaling DQ Operations

Finally, AI/ML-based systems can easily scale as your data increases over time. An AI-based DQ management system won’t slow down as you ingest more data. Unlike traditional systems that bog down with increased data loads, an AI system can easily handle all the data you can throw at it without a corresponding increase in cost or resources. 

Turn to DataBuck for AI/ML-Based Data Quality Monitoring

AI and ML technologies can dramatically improve the quality of your organization’s data. FirstEigen’s DataBuck solution uses AI and ML to automate more than 70% of the data monitoring process. You don’t have to create any manual data quality rules; our AI-based system does the work for you—and ensures that your company’s data will be of the highest possible quality. Contact FirstEigen today to learn about the role of ML and AI in data quality management.

Check out these articles on Data Trustability, Observability, and Data Quality. 

• 6 Key Data Quality Metrics You Should Be Tracking (https://firsteigen.com/blog/6-key-data-quality-metrics-you-should-be-tracking/)
• How to Scale Your Data Quality Operations with AI and ML (https://firsteigen.com/blog/how-to-scale-your-data-quality-operations-with-ai-and-ml/)
• 12 Things You Can Do to Improve Data Quality (https://firsteigen.com/blog/12-things-you-can-do-to-improve-data-quality/)
• How to Ensure Data Integrity During Cloud Migrations (https://firsteigen.com/blog/how-to-ensure-data-integrity-during-cloud-migrations/)