The trust in artificial intelligence systems is being questioned due to concerns over the data used to train these systems, according to a Salesforce survey of 6,000 global knowledge workers. More than half of AI users do not trust the data that trains today’s AI systems, leading to doubts about the usefulness of these systems. In order for AI to be reliable and effective, a well-vetted data foundation is essential, as Sean Knapp, founder and CEO of Ascend.io, points out. Without accurate and reliable data, AI systems may not provide the desired results and answers.
Industry leaders are highlighting the importance of high-quality data for AI systems to be successful. As AI relies on data to function, the quality and reliability of the data being fed into these systems are crucial. Organizations need to understand that AI is only as good as the data backing it and must prioritize data development by creating advanced data pipelines efficiently. A data-driven business is synonymous with an AI-driven business, and without a focus on data, businesses may struggle to compete in today’s fast-paced market, according to Sharad Varshney, CEO of OvalEdge.
Building a solid foundation of trusted and governed datasets is essential for the success of AI initiatives, according to Jonathan Bruce, vice president at Alation. While many organizations are eager to exploit AI models for analytics, it is crucial to invest in high-quality data to support these initiatives. By taking the time to ensure a strong data foundation, organizations can empower their users to apply AI models quickly and effectively, driving innovation and competitiveness. Adopting AI-enabled data management, analytics, and governance technologies from the beginning can put businesses in a strong position to succeed in the AI revolution.
Ram Chakravarti, CTO of BMC, emphasizes the importance of high-quality data for training AI solutions and deriving value from AI systems. AI can provide new insights and automate routine tasks, freeing up time for employees to focus on strategic business initiatives. However, without proper data quality, AI may struggle to provide meaningful results. Organizations need to invest in high-quality data sets to train AI effectively, as the volume of data is not as important as the quality of the data being used. AI and data are interconnected, and businesses must prioritize data quality to drive innovation and success.
In conclusion, the relationship between data quality and AI success is clear: reliable data is essential for AI systems to function effectively. Organizations must prioritize building a solid foundation of trusted and governed datasets to support their AI initiatives and drive innovation. Industry leaders stress the need for a data-driven focus in today’s competitive business environment, as AI and data are intricately linked. By investing in high-quality data and adopting AI-enabled data management technologies, businesses can position themselves for success in the AI revolution and gain a competitive advantage in the market.
