Introduction
AI models face issues in development due to poor-quality data that:
• Drives up operational costs
• Hinders the quality of model accuracy
• Negatively impacts customer satisfaction and experience
• Slows down the digital transformation
• Obstructs compliance with government and industry regulations
If you want to avoid issues with AI model performance, you need to improve the quality of your data. You do this by choosing an annotation partner that can track data quality metrics for you while providing quality training data.
So keep reading. We will explore Essential Metrics for Monitoring AI Data Quality and how to navigate the delays in AI model development caused by bad training data.
Table of contents
The “I” in AI without Data
Technically, no data means no INTELLIGENCE.
The saying “Garbage in, garbage out” is probably familiar to all of us. This is particularly valid for machine learning. If you give inaccurate data, you’ll get inaccurate results.
Such inaccurate data arise in various forms, including duplicate values, missing data points, outliers, variations, invalid values, inconsistencies in data, and unethical data.
The worst part is that problems with data quality continue to arise as data becomes larger and more complicated. In today’s times, it becomes difficult to ensure that all of that data is accurate, clean, and relevant.
Companies must mobilize solutions that evaluate quality metrics and improve data quality before machine learning algorithms begin to perform their tasks.
What does Data Quality mean in Machine Learning?
All types of AI models, including generative AI and machine learning, depend on the data it is fed. Fundamentally, an AI model cannot improve low-quality data. It can find information, summarize it, and draw conclusions from the content, but its output will only reflect the training data it’s given.
Data Quality Metrics for Effective AI Model
According to the Gartner study, data quality is evaluated using nine essential metrics, which is important to understand here because poor quality data costs organizations an average of $12.9 million a year.
Accessibility
Accessibility in data quality ensures that data is readily available, easily retrievable, and seamlessly integrated into AI-driven processes. This enables domain experts to work with up-to-date information and reduces delays in model training which affects the model relevance.
Accuracy
Accuracy is a metric that calculates how frequently a machine learning model correctly predicts the outcome. It is calculated after dividing the correct predictions by the total predictions made by the AI system.
Completeness
Missing data can result in incomplete analysis and hinder model performance, so data completeness shows how fully a dataset covers the topic or scope it’s meant to analyze. It’s key to ensuring data quality, especially in big data and AI applications, where data needs to capture all relevant details of the intended domain.
Consistency
Inconsistent data points can lead to errors in processing and analysis. After deployment, an AI model may encounter new data that differs from its training set. This will cause a decline in model performance, also known as “model drift.” To address this, AI systems need fine-tuning with updated data to maintain accuracy and effectiveness.
Precision
Precision for data quality means preventing ambiguities and misinterpretations. It refers to the quality of predictions made by the model calculated that could compromise the AI model’s predictions as the number of true positives divided by the total number of positive predictions by the model.
Relevancy
Relevance learning in AI is the process by which models learn to identify what’s important in the training data. It means prioritizing components using labeled data and predefined criteria to guide the model’s focus on relevant information.
Timeliness
Outdated data prevents AI models from adapting to real-world changes. Timeliness means using up-to-date data for model training to capture trends, respond to market shifts, and produce relevant insights.
Uniqueness
Tracks duplicate records or entries in the dataset. Redundant data can distort model results, making uniqueness a key factor for maintaining dataset integrity.
Validity
Data validity conforms that values fall within specific formats, ranges, or standards, which is crucial for model input accuracy. Validity checks are crucial for maintaining data credibility, as invalid values can distort analytics, lead to incorrect conclusions, and ultimately harm the organization’s decision-making processes.
In addition to the above nine metrics, there are two important ones worth noticing:
Compliance
AI compliance is the process of ensuring AI-powered systems adhere to all relevant rules and regulations. It also guarantees the ethical and responsible use of AI-powered systems for the good of society.
Representative
AI models must be trained on datasets that accurately reflect the real-world concerns they will be utilized. Biased AI systems that don’t function fairly across various demography, situations, or circumstances result from a lack of representativeness.
After observing the above metrics, it becomes clear that actionable steps are much needed to address bad data issues and ensure that AI models generate reliable outcomes. We have explored this in the next section.
Navigating Bad Data Issues
Because navigating the challenges of poor-quality data in AI model development requires specialized skills and deep field expertise, that’s why partnering with a data annotation provider is essential as they streamline the entire process from model development to deployment.
However, selecting a data annotation company might be difficult due to the abundance of options. Regardless of your final choice, the annotation partner you choose must be able to do the following:
Data Diversity
Meet the diversity needs to scale and adjust while maintaining validity and consistency per industry and demography.
Compliant Data
Ensure data governance guidelines so that AI companies follow the right processes and roles and use data efficiently to develop models. Having certifications, such as FDA and HIPAA, and a CCPA complaint is a plus to meet regulatory standards.
Audit the Data
Perform data audits that reveal issues such as poorly populated fields, data format inconsistencies, duplicated entries, data inaccuracies, missing data, and outdated entries.
Data Curation and Annotation
Acquiring the training data requires an involved process that annotation companies do. In addition to providing clean and organized data adequately prepared for AI model training, check that your annotation partner has the required team size and employs certified professionals, which is especially required in building medical AI models. The company must reflect a commitment to data quality and expertise.
Fine-tuning
Some annotated datasets need final adjustments to ensure accurate outcomes through a process called fine-tuning. AI model fine-tuning is especially relevant in Gen AI models as concentrating the model’s learning on more specific facts reduces the possibility of producing unnecessary or factually wrong information. To achieve faster return on investment (ROI) and model deployment, data labeling partners offer fine-tuning services.
Conclusion
Organizations rely heavily on AI/ML model development in today’s data-driven landscape to automate processes and enhance customer experiences across verticals. However, the effectiveness of these systems is only as strong as the quality of the data they are built on.
With reliable data labeling partners, data scientists can focus on refining models without the burden of managing training data. These companies bring the precision and expertise to ensure high-quality, well-labeled data so you can confidently focus on making business decisions and operations.