Data Quality in AI: Challenges, Importance & 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, fostering trust and confidence among users. 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 biases in data is crucial for 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. Ultimately, maintaining data quality is key to realizing the full potential of AI systems in delivering value, driving innovation, and ensuring ethical outcomes.

As Andrew Ng, Professor of AI at Stanford University and founder of DeepLearning.AI, emphasized, “If 80 percent of our work is data preparation, then ensuring data quality is the most critical task for a machine learning team.”

Why getting rid of the “garbage in and garbage” concept is crucial for data quality

“Garbage in, garbage out” (GIGO) is a concept in computing and artificial intelligence (AI) that highlights the importance of input data quality. It means that if the input data to a system, such as an AI model or algorithm, is of poor quality, inaccurate, or irrelevant, the system’s output will also be of poor quality, inaccurate, or irrelevant. (See Figure 2).

Source: Shakoor et al., 2019 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, which 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 of high quality. This often involves data cleaning, preprocessing, and augmentation, as well as the use of robust evaluation metrics to assess the performance of AI models.

What are the key components of quality data in AI?

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. Ensuring all data points follow the same standards and eliminating duplicate or conflicting data can be a challenge. Synthetic data can help with overcoming this issue.

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 and reliable data storage, which 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 process, resulting in unreliable or even harmful outcomes. To protect against this risk, it is crucial to ensure data integrity through regular audits and the detection of anomalies.

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, which diverges 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.

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 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

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.

External Links

• 1. Refinitiv. “Smarter Humans. Smarter Machines.” Insights from the Refinitiv 2019 Artificial Intelligence / Machine Learning Global Study. 2019
• 2. The State of Data Management - The Impact of Data Distrust | SnapLogic. SnapLogic
• 3. ResearchGate - Temporarily Unavailable.