AI Data Quality in 2026: Challenges & Best Practices
Poor data quality hinders the successful deployment of AI and ML projects. 1 Even the most advanced AI algorithms can yield flawed results if the underlying data is of low quality. We explain the importance of data quality in AI, the challenges organizations encounter, and the best practices for ensuring high-quality data.
What is the importance of data quality in AI?
Data quality is essential for artificial intelligence, as it directly influences the performance, accuracy, and reliability of AI models. High-quality data allows models to make better predictions and yield more reliable outcomes. The impact of poor data quality in AI is illustrated in Figure 1.
Source: SnapLogic2
Figure 1: Impact of poor quality data and analytics
Addressing data biases is crucial to ensuring data quality. This prevents the perpetuation and amplification of biases in AI-generated outputs, helping minimize unfair treatment of specific groups or individuals.
Furthermore, a diverse and representative dataset enhances an AI model’s ability to generalize well across different situations and inputs, ensuring its performance and relevance across various contexts and user groups.
As Andrew Ng, Professor of AI at Stanford University and founder of DeepLearning.AI, states, “If 80 percent of our work is data preparation, then ensuring data quality is the most critical task for a machine learning team.”
Why is getting rid of the “garbage in and garbage out” concept crucial for data quality?
“Garbage in, garbage out” (GIGO) is a simple yet effective principle that underscores the importance of input quality in data quality. It means that if the input data to a system, such as an AI model or algorithm, is poor-quality, inaccurate, or irrelevant, the system’s output will also be poor-quality, inaccurate, or irrelevant. (See Figure 2).
Source: Shakoor et al. 3
Figure 2: Data quality and standards: “garbage in” data, “garbage out” results.
This concept is particularly significant in the context of AI, as AI models, including machine learning and deep learning models, rely heavily on the data used for training and validation. The AI model will likely produce unreliable or biased results if the training data is biased, incomplete, or contains errors.
To avoid the GIGO problem, it is crucial to ensure that the data used in AI systems is accurate, representative, and high-quality. This often involves data cleaning, preprocessing, and augmentation, along with the use of robust evaluation metrics to assess AI model performance.
What are the key components of quality data in AI?
Accuracy: Accurate data is crucial for AI algorithms, enabling them to produce correct and reliable outcomes. Errors in data input can lead to incorrect decisions or misguided insights, potentially harming organizations and individuals.
Consistency: Ensures that data follows a standard format and structure, facilitating efficient processing and analysis. Inconsistent data can lead to confusion and misinterpretation, impairing the performance of AI systems.
Completeness: Incomplete data sets can cause AI algorithms to miss essential patterns and correlations, leading to incomplete or biased results. Ensuring data completeness is vital for training AI models accurately and comprehensively.
Timeliness: Data freshness plays a significant role in AI performance. Outdated data may not reflect the current environment or trends, leading to outputs that are irrelevant or misleading.
Relevance: Relevant data directly contributes to the problem at hand, helping AI systems focus on the most important variables and relationships. Irrelevant data can clutter models and lead to inefficiencies.
What are the challenges of ensuring data quality in AI?
1-Data collection
As developments in AI benefit industries such as finance, healthcare, manufacturing, and entertainment, organizations face the challenge of collecting data from various sources while maintaining quality. Many turn to web scrapers to automate and ensure all data points follow the same standards.
2-Data labeling
AI algorithms rely on labeled data for training, but manual labeling is both time-consuming and prone to errors. Obtaining accurate labels that reflect real-world conditions is often challenging.
3-Data storage and security
Ensuring data quality involves safeguarding it from unauthorized access and potential corruption. It is essential for organizations to have secure, reliable data storage, but this can be difficult.
4-Data governance
Organizations often struggle with implementing data governance frameworks that effectively address data quality issues. A lack of proper data governance can lead to siloed data, inconsistency, and errors.
5- Data poisoning
Data poisoning is a targeted attack on AI systems in which attackers introduce malicious or misleading information into the dataset. This poisoned data can distort the model’s training, leading to unreliable or even harmful outcomes. To mitigate this risk, it is crucial to maintain data integrity through regular audits and anomaly detection.
6-Synthetic data feedback loops
Feeding AI-generated data back into AI models can create feedback loops that degrade model quality over time. For example, when synthetic data is repeatedly used, the model might learn patterns that are too artificial and diverge from real-world conditions. This can cause models to perform poorly on actual data, potentially amplifying biases or errors. Balancing synthetic and real data is essential to maintain model robustness.
Real-world case studies
Case Study 1: Mayo Clinic – Medical Imaging Data Quality
Mayo Clinic has developed one of the most sophisticated approaches to data quality in healthcare AI, particularly for diagnostic imaging systems. The organization processes millions of medical images annually, and maintaining data quality is critical for accurate diagnoses. 4
The Challenge: Medical imaging data presented unique quality issues, including inconsistent image formats, varying resolution standards across different scanners, incomplete patient metadata, and the need to maintain HIPAA compliance while ensuring the data’s usefulness for AI training.
The Solution: Mayo Clinic implemented a comprehensive data quality framework that includes automated image standardization protocols, metadata validation systems that flag incomplete or inconsistent patient information, and a federated learning approach that allows AI model training without centralizing sensitive patient data.
Case Study 2: JPMorgan Chase – Fraud Detection Data Quality
JPMorgan Chase processes billions of transactions annually and relies heavily on AI for fraud detection. The quality of transaction data directly impacts the effectiveness of their fraud prevention systems. 5
The Challenge: The bank faced challenges with real-time data quality at a massive scale, handling structured and unstructured data from multiple channels, including credit cards, wire transfers, and mobile banking. They also needed to balance fraud detection sensitivity with customer experience while adapting to constantly evolving fraud patterns.
The Solution: JPMorgan developed a multi-layered data quality approach that includes real-time data validation, which checks transaction data against quality rules within milliseconds; anomaly detection systems that identify data quality issues before they affect fraud models; and continuous model monitoring that tracks data and concept drift in fraud patterns.
Case Study 3: Walmart – Recommendation Engine Data Quality
Walmart operates one of the largest e-commerce platforms globally, with its recommendation system driving significant revenue. Data quality in customer behavior, product catalogs, and inventory systems is crucial for relevant recommendations. 6
The Challenge: Walmart needed to integrate data from over 4,700 physical stores with online customer behavior, manage product catalog data with millions of SKUs that frequently change, handle seasonal variations and rapid inventory fluctuations, and merge data from acquired companies like Jet.com with different data standards.
The Solution: The retail giant implemented a unified data quality framework with automated product catalog cleaning to standardize product attributes, descriptions, and categorizations. They built real-time inventory data validation to ensure recommendations reflect actual product availability and created customer data deduplication systems to create unified customer profiles across channels.
Best practices for ensuring data quality in AI
1-Implement data governance policies
A data governance framework should define data quality standards, processes, and roles. This will help create a culture of data quality and ensure that data management practices align with organizational goals.
Real-life example: Airbnb
Airbnb launched “Data University” to enhance data literacy across its workforce by offering customized courses that integrate Airbnb’s specific data and tools. Since its inception in Q3 2016, Data University has increased engagement with Airbnb’s internal data science tools, raising weekly active users from 30% to 45%.
With over 500 employees participating, the initiative underscores the importance of aligning data governance efforts with organizational objectives, promoting a company-wide culture of data quality and informed decision-making. The program exemplifies how customized data governance frameworks can drive data competency and foster alignment with business goals.
2-Utilize data quality tools
Data quality tools can automate data cleansing, validation, and monitoring processes, ensuring that AI models have consistent access to high-quality data.
Real-life example: General Electric
A relevant real-life example of utilizing data quality tools is General Electric’s (GE) implementation of its data governance and quality management strategy, particularly within its Predix platform for industrial data analytics. To support its digital transformation and AI initiatives, GE invested in a robust data quality toolset to maintain high data standards across its industrial IoT ecosystem.
GE deployed automated tools for data cleansing, validation, and continuous monitoring to manage the massive volumes of data generated by its industrial equipment, such as turbines and jet engines. These tools helped GE ensure that the data feeding its AI models was accurate, consistent, and reliable, reducing the need for manual intervention and enabling real-time data-driven insights.
3-Develop a data quality team
Developing a dedicated team responsible for data quality will ensure continuous monitoring and improvement of data-related processes. The team can also educate and train other employees on the importance of data quality.
4-Collaborate with data providers
Establishing strong relationships with data providers and ensuring their commitment to data quality can minimize the risk of receiving low-quality data.
5-Continuously monitor data quality metrics
Regularly measuring and monitoring data quality metrics can help organizations identify and address potential issues before they impact AI performance.
What Is AI Data?
AI data broadly refers to any data used in the development or operation of artificial intelligence systems. Consequently, this includes, but is not limited to, datasets used to train models, real-time input data used for predictions, and synthetic data generated to augment real-world examples, among others. While not a formal technical term, “AI data” is commonly used to describe the information that powers machine learning and deep learning systems.
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Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE and NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and resources that referenced AIMultiple.
Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised enterprises on their technology decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.
He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.
Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.
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