The article further suggests two main ways to ensure high data quality: data integration and data quality management (DQM). Data integration involves connecting disparate data sources and transforming the data into a unified view, while DQM involves creating a culture that prioritizes quality data, establishing data governance, and adopting technology that assists with data cleansing, validation, and monitoring. The article concludes by emphasizing that maintaining data quality is essential for developing accurate AI models and tools, and skipping this step could result in the failure of 85% of AI models.
Key takeaways:
- Artificial Intelligence (AI) models often fail due to poor data quality or lack of relevant data, with 85% of all AI projects failing for these reasons.
- Data quality is crucial for the success of AI models, and it refers to factors such as accuracy, consistency, completeness, timeliness, relevance, uniqueness, and integrity.
- Organizations can ensure high data quality by implementing data integration methods, which aggregate various information sources, systems, and formats into a unified view.
- Another way to ensure high data quality is by creating a holistic Data Quality Management (DQM) program, which involves establishing data governance, creating a culture that prioritizes quality data, and adopting technology that assists with data cleansing, validation, quality monitoring, and issue resolution.