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Artificial intelligence is quickly moving from a futuristic concept to a practical tool for modern business. For entrepreneurs and investors, AI offers a powerful way to cut through the noise and identify genuine investment opportunities. Instead of relying solely on intuition and manual research, you can now use AI to analyze complex data sets, predict market shifts, and pinpoint ventures with the highest potential for success.

Enterprise data teams are under pressure from two directions at once. Business stakeholders expect faster, more reliable data products, from clean dashboards to trustworthy metrics feeding into AI systems, while the talent market for people who can build and maintain that infrastructure remains tight. Hiring has not solved this on its own, since experienced analytics engineers are expensive, hard to find, and often just as hard to retain once they are trained up on a specific stack.

An interview with Marcin Chudeusz, co-founder and CEO of digna Two decades ago, enterprise data quality looked very different. Organizations were building centralized data warehouses, business intelligence projects revolved around structured reporting, and most data quality initiatives relied on thousands of manually created validation rules. The objective was simple: ensure the data entering reports was accurate enough for decision-making.