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AI Needs Data: Experts Share Secrets to Success
20 Nov
Summary
- Successful AI initiatives critically depend on vast amounts of high-quality data.
- Organizations must develop robust data governance and ownership strategies.
- Focus on current business needs while maintaining flexibility for future AI demands.
The success of AI initiatives, from generative to agentic models, is fundamentally tied to the availability of extensive and high-quality data. Business leaders emphasize that the output of AI directly reflects the data it processes, making a 'rubbish in, rubbish out' scenario a significant risk. Establishing foundational elements like good data practice, governance, and clear ownership is paramount for transforming raw information into actionable insights.
Experts recommend a balanced approach, urging organizations to prioritize data crucial for current business objectives rather than trying to predict every future need. While modern data platforms offer cheap storage, a targeted strategy focusing on essential information for specific use cases, like forecasting or customer service AI, is more efficient. This involves identifying the 20% of data that drives 80% of the value.
Ultimately, forward-thinking and flexible data strategies are essential. Organizations should aim to be data-led, capturing vital information while maintaining the agility to adapt to evolving market demands and emerging AI capabilities. Leveraging external innovations and understanding the data context through effective cataloging and tagging are also key to unlocking AI's true value.
