Data Management’s Next Frontier is Machine Learning-Based Data Quality

Regardless of how accurate a data system is, it yields poor results if the quality of data is bad. As part of their data strategy, a number of companies have begun to deploy machine learning solutions.

In a recent study, AI and machine learning were named as the top data priorities for 2021, by 61% of respondents. This is hardly surprising, given the number of unknowns that data management systems must cope with, as well as the problems posed by big data.

Why Data Quality is Important

Even though big data is expanding to zettabytes, poor data quality prevents enterprises from reaching their full potential. According to Gartner’s Data Quality Market Survey, the financial effect of data quality concerns alone cost enterprises around $15 million in 2017. Clearly, this is a major issue that must be addressed.

To combat this problem, businesses have traditionally utilized a combination of manual and automated solutions. In today’s big data era, the problem’s complexity has increased, prompting out-of-the-box solutions. Here are some attributes that may define data quality.

  • Correctness: Data correctness is an important feature of high-quality data; even a single incorrect data point can cause chaos throughout the system. Executives can’t trust the data or make informed judgments if it isn’t accurate and reliable. Analysts end up relying on poor-quality reports and drawing erroneous conclusions based on them. End-user productivity can suffer as a result of the ineffective standards and practices in place.
  • Timeliness: Data that isn’t kept up to date might lead to a slew of other issues. Out-of-date customer information, for example, might lead to missed possibilities for up and cross-selling products and services.
  • Consistency: With a poorly designed system, updates to data might not propagate to all users. This may result in different users looking at different views of data. For instance, an e-commerce store may ship products to incorrect addresses, resulting in reduced customer satisfaction, fewer repeat purchases, and increased costs due to reshipment.
  • Availability: In some use cases, data that needs to be available at all times. Unavailability may result in the company being fined or regulatory compliance reporting in more heavily regulated industries.

Problems with the Current Approach

Data management specialists have always been involved in fine-tuning data analysis and reporting platforms while ignoring data quality. Traditional data quality management systems rely on the experience of users or established business requirements. It is not only a time-consuming activity, but it also limits performance and has a low level of precision. [1]

The majority of businesses have addressed data quality challenges by defining strict rules in their databases, developing in-house data cleansing systems, and relying on manual processes. This strategy, however, has a number of drawbacks:

  • The 3Vs of big data—variety, velocity, and volume—have made data quality a tough problem to crack. Multiple sources and types of data require customized approaches. For example, companies have access to data from technologies like IoT sensors, which present the challenge of unforeseen volumes and non-standardized data formats across devices. [2]
  • In processes like data validation, semi-structured and unstructured data types add to complexity.
  • Another problem with the rule-based system is that it can have too many rules for high cardinality, multidimensional data. [3]
  • The Data Quality Framework requires some bespoke implementation for each new defect or anomaly, implying that human interaction is unavoidable in such a solution. [3]

We need to find a fully automated method to avoid human interaction in the rule-based situation. With several recent breakthroughs, machine learning is one of the disciplines that may be able to assist in this case. Let’s explore if machines can assist us in assuring automated data quality, or if we need to look beyond the apparent. But first, let’s talk about why before we talk about how.

Why Machine Learning to Improve Data Quality

When it comes to machine learning for data quality, there’s no need to maintain rules. Machine learning models can also help improve data quality since they can:

  • Learn from massive volumes of data and uncover hidden patterns
  • Handle repetitive duties
  • Change as the data changes


The most significant advantage of machine learning is that it tremendously reduces the time it takes to clean data, allowing tasks that formerly took weeks or months to be completed in hours or days. Plus, volume, which was once a disadvantage in manual data processes, is now a benefit for machine learning systems, as they improve when given more data to train with.

When it comes to data quality, here’s how machine learning can help:

  • Fill data gaps: While many automated systems can purify data using some programming principles, filling in missing data gaps without manual intervention or additional data source feeds is nearly impossible. Machine learning, on the other hand, may make calculated evaluations on missing data based on how it perceives the scenario. [2]
  • Identify duplicate records: Duplicate data entries can result in outdated records, resulting in poor data quality. ML can be used to reduce duplicate records in a database.
  • Detect anomalies: A minor human error can have a significant impact on the usefulness and quality of data in a CRM. Machine learning algorithms are very good at detecting inaccurate patterns, correlations, and infrequent occurrences in a large amount of data.
  • Match and validate data: It can take a long time to come up with rules to match data received from multiple sources. This becomes progressively difficult as the number of sources increases. Machine learning models can be taught to learn the rules and forecast fresh data matches and clean up data inaccuracies effectively.

Parting Thoughts

It’s clear that there is no one-size-fits-all solution for all of your data quality requirements. It may differ from one use case to the next; in some circumstances, a rule-based system may suffice but, as data expands and changes, moving toward a machine learning approach may help you look beyond the obvious.

This new breakthrough will undoubtedly have an impact on a variety of industries, including banking, financial markets, e-commerce, education, health care, manufacturing, and many others. Increased productivity, enhanced customer experience, improved decision-making, and timely planning are all benefits of integrating AI in enterprises.


[1] R.Joseph, The Role of AI and Machine Learning in Data Quality (2019), Intellectyx.

[2] Data Quality and Machine Learning: What’s the Connection? (2012), Talend.

[3] J.Dhiman, Is Machine Learning the Future of Data Quality? (2021), Towards data science.

[4] M.Suer, What Is Data Quality and Why Is It Important (2021), Alation.

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Angsuman Dutta

Angsuman Dutta

Angsuman Dutta is the CTO and co-founder of FirstEigen. He is an entrepreneur, investor, and corporate strategist with experience in building software businesses that scale and drive value. He has provided Information Governance and Data Quality advisory services to numerous Fortune 500 companies for two decades and has successfully launched several businesses, including Pricchaa Inc. He is a recognized thought leader and has published numerous articles on Information Governance.

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