Bridging the hidden gap between data and decisions in the age of AI (www.techradar.com)

A recent discussion emphasizes that while clean and organized data is essential for AI success, it alone is insufficient without robust engineering and operational frameworks. Many organizations struggle with the transition from data collection to actionable AI insights due to a lack of strategic alignment, outdated IT infrastructure, and skill gaps among teams. A survey revealed that 63% of companies lack adequate data management practices for AI, highlighting systemic issues that go beyond mere data quality. To bridge the gap between data and business outcomes, companies must adopt a mindset that prioritizes data operations designed around specific goals. This involves treating data engineering and architecture as essential business functions, establishing governance early, and modernizing infrastructure. Successful organizations can significantly improve efficiency, reduce costs, and bolster cybersecurity by enabling seamless data integration and visibility. Ultimately, the ability to quickly turn insights into action will distinguish leaders in the AI landscape, emphasizing that speed and readiness are critical in the race for AI transformation.