The Future of Data Management: How AI is Revolutionizing Data Quality Processes

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Most people probably know the capabilities of AI. However, only a few companies leverage them in their day-to-day operations. In fact, just about 35 percent of businesses worldwide use AI, with China ranking first as the most AI-driven business landscape at 58 percent.¹

If you want to maintain high data quality across your organization, you have to maximize your resources—use AI to the fullest of its capabilities. In this blog, we’ll share which AI tools can help your business and how these can impact your organization. We will also provide insights into companies already benefiting from this technology.


The Challenges of Manual Data Quality Processes

Everything evolves around us, so why should our processes stay the same?

Data management requires different steps, and each step is prone to human error. It can also be time-consuming, create inconsistencies, and delay information sharing.

According to Parseur, 27 percent of accounting professionals experienced incorrect data input in their companies.² To top it all off, data quality can cost companies an average of $15 million annually if not maintained.

Aside from the cost, data entry can take up most of your resources. More than 40 percent of workers spend almost 10 hours a week on repetitive tasks.


AI-Powered Solutions and Strategies

To improve data quality, you must adopt automated strategies. While using artificial intelligence can be costly, using AI for data quality maintains accurate and reliable information. Here are a few AI models you can leverage:


1. Machine Learning

By the name itself, Machine Learning is based on “experiences” or the data you provide over time. It reduces the need for reprogramming as it can develop its decision-making capabilities. As you input more data into the system, its ability to identify and correct data improves. You can use Machine Learning for:


Data Cleansing: Detect and correct errors through historical patterns.

Anomaly Detection: Identify inconsistencies in data. This allows you to detect errors, fraud, or any unusual activity.

Data Matching: Identify patterns as well. This can help reduce duplicates and maintain data integrity.


2. Natural Language Processing

Natural language processing, or NLP, can transform unstructured data into structured data. This allows you to utilize vast amounts of data and ensure their relevance to your needs. Here are a few ways you can use NLP:


Text Parsing and Extraction: Automate data extraction from unstructured text data.

Data Standardization: Recognizes different formats and converts them into one format.

Sentiment Analysis: Analyze customer feedback and reviews.


3. Predictive Analysis

Predictive analysis also uses historical data. This allows you to predict trends and validate existing data. Predictive analysis enables:


Data Forecasting: Forecast future trends. It allows you to adapt and anticipate changes.

Data Validation: Validate data entries to ensure accuracy.

Trend Analysis: Identify and analyze trends in data, ensuring consistent data quality over time.


4. Robotic Processes Automation

Robotic processes automation, or RPA, can automate repetitive tasks. This ensures accurate, consistent, and efficient data governance.


Automated Data Entry: Used for repetitive data entry tasks to reduce human error and increase efficiency.

Data Migration: Automates the data migration between systems.

Data Verification: Perform rule-based validation checks on data to meet quality standards.


Why Do You Need AI for Data Quality?

There are many types of AI solutions, like generative AI, data mining, and blockchain. If you dig deeper, you will also uncover the power of big data and data science. The world of AI systems is vast, and you need to discover which would suit your company best.

However, most of them have the same purpose—to make things easier for everyone. In your case, to improve data quality management. Here are a few benefits of AI for data quality:


Improved Data Accuracy and Consistency

Maintaining quality data is one of the most critical elements of data handling. Without accurate and reliable data, your business results, decisions, recommendations, and reputation will be at risk.

For example, you recommend a client to transfer to a better program. While the old system doesn’t hold a candle to the new software, it would be ineffective if you missed critical factors. It could be that the new program doesn’t support your client’s file format. This is why it’s vital to remain accurate and consistent with data and all its components.

Related Article: OpenAI and Microsoft: Tech Giants Wrestle for Control


Increased Efficiency and Cost Savings

Employing data quality tools can be costly. The initial investment, training, integration, and migration can be challenging, especially for smaller businesses. However, it’s more stable and reliable in the long run.

With AI, your processes can be done faster with fewer errors. You don’t have to worry about making mistakes. Plus, you’ll have more resources to allocate. Your workforce will also be in a much better state, not doing repetitive tasks.

According to Statista, businesses using AI-powered systems experienced cost reductions.³ They experienced these significant savings across many functions, with supply chain management being the highest.


Enhanced Scalability and Adaptability

AI programs can hold massive amounts of data. This allows you to increase or decrease your capacities at will. It offers flexibility in scaling your business depending on the changing demands. Your data aligns with you, not the other way around.

It also creates more opportunities to future-proof your business. This guarantees that you can quickly adapt to upcoming challenges and requirements.

On the other hand, physical copies take up space. They’re vulnerable to external elements and physical damage. While computer servers are still popular today, other options like cloud systems that can secure your data digitally are available.


Real-World Application of AI-Powered Solutions

Many businesses today are already improving data quality. These are some of the companies that produce high-quality data using artificial intelligence. We’ll also highlight companies that have suffered from poor data quality due to a lack of comprehensive measures.


1. AstraZeneca’s 3-Minute Analysis

AstraZeneca is a globally leading pharmaceutical company. They wanted to reduce drug development for faster distribution and marketing. As drug development relies heavily on data, they had to improve their data management.⁴

“We must balance this desire to speed the process with trusted data. If we don’t have data quality, our drugs will not be approved, affecting the lives of our potential patients.” – Andy McPhee, Data Engineering Director at AstraZeneca

As a solution, they partnered with Talend Data Fabric to source data into a data lake, implementing strict data governance measures. Using automated workflows and machine learning models, they could analyze vast amounts of data in 3 minutes. This saved them significant amounts of time and money.

Related Article: Future Forward: Essential Tech Breakthroughs from CES 2024


2. Uber’s Miscalculation

While Uber’s challenges were unrelated to inaccurate data, their case still cost them $45 million.⁵

In 2017, Uber was found to have miscalculated earnings for New York drivers and had taken 2.6 percent more from their drivers. Uber settled the dispute by returning driver commissions plus 9 percent interest. However, the company had already smeared its public image and lost vast amounts of money.

This is why it’s crucial to maintain data and information integrity. Aside from keeping correct records and maintaining accurate data, you must also use the correct data. With proper automation and validation, Uber could have avoided such an incident.


3. Amazon’s Innovative Approach to AI Use

Amazon is already a billion-dollar company. This makes their operations extremely volatile to errors and inaccuracies.⁶ In their hopes of reducing climate impact, they had to find quick and innovative solutions. Mainly using artificial intelligence and machine learning, they were able to improve core parts of their business.


Packaging Decision Engine

This AI model analyzes the most efficient packaging design using:

  • Specific models trained by data scientists
  • Analysis of the item’s shape and durability
  • Customer feedback on packaging preference and performance
  • And it’s still learning and has saved the company two million tons of packaging material since 2015.


Food Waste Monitoring

To maximize food shelf life and use, Amazon monitors their fresh food. They use automated shelf monitoring systems for fruits and vegetables to detect visual imperfections like damage, cracks, and cuts. This helps ensure low-grade food can be recycled.

These foods are then sold to local contractors at a lower price. Typically, these are used to feed livestock, ensuring less food goes to waste.


4. Samsung’s Data Entry Mistake

A simple data entry error cost Samsung $187 million in just under 37 minutes.⁵ This happened when an employee accidentally distributed 2.8 billion shares to employees. It took the company half an hour to realize the mistake. However, 16 employees had already sold 5 million shares.

As a result, their stock shares dropped by 12%, effectively losing $300 million in market value. They also lost major customers due to safety measures, prompting CEO Koo Sung-Hoon to resign.

Related Article: 7 Essential Traits for the Next AI Tech Leaders



Data quality is a critical aspect of a business’s success. While some of these errors can be avoided, there’s no saying what could happen. Simple mistakes in data entry or using the wrong data are valid human errors.

However, when significant amounts of money, stakeholders, partner trusts, and your company image are at stake, protecting your data with AI is a no-brainer.

AI is here to stay; you might as well take advantage of it.



  1. Cardillo, Anthony. “How Many Companies Use AI? (New Data).” Exploding Topics, 6 May 2024,
  2. Gunnoo, Neha. “Manual Data Entry – Challenges and Solutions in 2024.” Parseur, 26 Dec. 2023,
  3. “Primary artificial intelligence (AI) cost decrease in businesses globally in 2021, by function.” Statista, 10 May 2024,
  4. Prakash, Aditi. “Augmented Data Management: How AI is Transforming Data Engineering.” Airbyte, 21, Jul. 2023,
  5. Gates, Sara. “5 Examples of Bad Data Quality in Business — And How to Avoid Them.” Monte Carlo Data, 27 Sep. 2023,
  6. Hurst, Kara.“7 ways Amazon is using AI to build a more sustainable future.” Amazon, 12 Feb. 2024,


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