Data quality is a crucial factor in the success of artificial intelligence (AI) systems and their ability to deliver highly performant, accurate, and reliable models and results. AI models heavily rely on large volumes of high-quality data for training, validation, and testing.
In a survey conducted by Gartner, it was found that poor data quality is a primary reason for 40% of all business initiatives failing to achieve their targeted benefits. In addition to Gartner's findings, a report by PwC estimated that poor data quality costs the United States healthcare system around $100 (USD) billion annually, impacting patient outcomes, revenue cycle management, and operational efficiency.
Simply put, improving data quality is crucial for ensuring the effectiveness and accuracy of AI systems. Here are 8 practical steps you can take to enhance data quality for AI.
The 8 Steps
Define clear data requirements: Clearly define the objectives and requirements of your AI system. Determine the specific data attributes, formats, and structures needed to meet those requirements.
Collaborate with domain experts: Work closely with subject matter experts who possess deep knowledge of the problem domain. Their insights can help identify potential data quality issues, improve feature engineering, and enhance the overall performance of the AI system.
Collaborate with data engineers: Collaborate closely with data engineers who specialize in data management, data integration, and data quality assurance. Their expertise can help implement robust data quality frameworks and workflows to ensure consistent and reliable data.
Data cataloging and documentation: Maintain comprehensive documentation that describes the data collection process, data preprocessing steps, data schema, and any modifications made to the data. This documentation helps ensure transparency and reproducibility, making it easier to track and address data quality issues.
Data governance: Ensures accountability, privacy, and compliance. Implement proper data management practices with access controls, security measures, and lifecycle management. Assign data stewards for monitoring and maintaining quality, defining policies, and fostering collaboration. Finally, apply anonymization for sensitive data and follow best practices to safeguard individuals' information and maintain data integrity.
Data preprocessing: Perform thorough data preprocessing to clean and transform the data before using it for AI training. This process may involve removing duplicate records, handling missing values, normalizing data, and dealing with outliers.
Data validation: Implement rigorous validation processes by ensuring the data has undergone cleaning to ensure it is accurate, consistent, has integrity, and adheres to certain predefined rules or standards before it can be used for AI training or business operations.
Regular data monitoring: Define and continuously track data quality metrics specific to your AI system and use them as indicators to assess the overall quality of your data. Metrics could include accuracy, completeness, consistency, and relevance of the data attributes. Implement feedback loops that allow you to identify and correct data quality issues in real time. Monitor data sources, evaluate data drift, and update your data collection and preprocessing processes accordingly.
Final Thoughts
Enhancing data quality for AI can be performed by data analysts, engineers, scientists, managers, and stewards using platforms like Erisna or through fully-automated processes by data engineers and developers using software tools or programming scripts via Erisna API.
By implementing these additional strategies, you can further enhance the quality of your data and consequently improve the performance, fairness, and reliability of your AI systems. Remember that data quality is a multi-faceted and ongoing effort that requires constant vigilance, monitoring, and improvement.
Erisna is a cloud and enterprise data discovery, catalog, and validation platform that enables data analysts, engineers, scientists, and managers to get the most out of their data. Erisna platform and API enables data management projects to drive efficiency and cost savings in data migration and integration, AI model training, and other data-related processes. Start your journey with Erisna today at www.erisna.com.