The world of artificial intelligence (AI) is facing a challenge when it comes to handling vast amounts of data. This data often contains inaccuracies, biases, and other issues that can lead to AI systems making errors and creating mistrust among users. Industry experts are now looking for ways to improve data consumption and ensure that AI operates on clean, relevant, and reliable data.
According to Andy Thurai, a principal analyst at Constellation Research, AI is like a “gas guzzler” when it comes to data consumption. However, consuming large volumes of data can result in a loss of quality, leading to trust issues with AI systems. Surveys have shown that many employees do not trust the data that trains the AI they work with, and organizations can lose a significant amount of revenue due to underperforming AI models built on inaccurate or low-quality data.
One of the key challenges is that organizations are producing more than enough data, but it is often fragmented across different business units, domains, and platforms. This makes it difficult to feed AI models with complete and accurate data, resulting in partial views of the enterprise. Data curation and quality assurance are essential to fixing these deficiencies, but they require a significant amount of time and effort.
To address these issues, organizations need to prioritize data governance and data management practices. This involves ensuring that the right data is going to the right people and applications in a timely, relevant, secure, and valuable manner. High-quality data is crucial for training AI models effectively and generating reliable insights. Data governance also helps organizations establish a culture of data integrity, which can drive innovation, operational efficiency, and growth.
In the GenAI era, where data processing is rapid, vast, and complex, robust AI governance frameworks are essential to ensure that AI operates on clean and reliable data. This will enhance the accuracy and fairness of AI decisions, promote effective collaboration through metadata management, and ensure compliance with regulatory demands. Investing in high-quality data and focusing on data quality over volume is key to success in the modern world of AI.
Overall, improving data consumption and data governance is crucial for maximizing the efficiency of AI systems. By addressing underlying data quality issues, organizations can ensure that AI operates on clean, relevant, and reliable data, leading to better decision-making, innovation, and growth.
