Have we jumped from “Big Data” to AI too quickly?

Thomas Gavaghan, Vice President at Kyriba, shares an article on ERP today that discusses the rush from “Big Data” to Artificial Intelligence (AI) and how companies might be overlooking the foundational work required to leverage these technologies effectively. He stresses the importance of strategic data management in finance and treasury for smarter decision-making, not just adopting AI for its own sake. The five essential dimensions of data management—Streaming, Searching, Data Lakes, Machine Learning, and Generative AI—are crucial for optimizing liquidity performance.

1. Streaming: Real-Time Data Flow. Real-time data streaming breaks down silos and enables quicker, smarter liquidity decisions. APIs help link systems and enhance flexibility, making it crucial for dynamic decision-making in volatile markets.
2. Searching: Efficient Data Discovery. As data centralization grows, the ability to search large datasets quickly is essential. Modern, cloud-native search tools enable finance teams to access and query data efficiently, driving proactive intelligence and better liquidity management.
3. Data Lakes: Centralized and Scalable Data. Data lakes centralize both structured and unstructured data, providing treasury teams with a single source of truth for analysis. This infrastructure supports better liquidity forecasting and decision-making, especially when integrated with APIs and business intelligence tools.
4. Machine Learning: Leveraging Patterns for Insights. Machine learning identifies patterns in data to improve areas like fraud detection and cash forecasting. The quality of ML insights depends on well-organized, accessible data, which is provided by strong data lakes.
5. Generative AI: Transforming Decision-Making. Generative AI automates decision-making by generating insights from complex data. It can predict and execute liquidity decisions, but its effectiveness relies on robust data foundations.

To fully realize AI’s potential, Gavaghan stresses that companies must focus on solid data management. Prioritizing technologies like streaming, searching, and data lakes enables AI-driven decision-making and helps finance teams unlock long-term strategic value from data.

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