Data problems in companies are not due to people's lack of skills, but from the wrong fundamental approach that doesn't align with how businesses actually operate and evolve.
The developments of generative AI in the past few years have been amazing. There are promises of AI agents revolutionizing everything from customer service to software development. Now they're coming for your data infrastructure. The pitch is seductive: AI agents will automatically handle data integration, quality, and governance.
The lakehouse idea springs from a common pain point: warehouses excel at handling structured data and delivering strong analytics performance, but they falter when faced with unstructured data and scalability challenges. On the other hand, lakes shine with unstructured data and flexibility but struggle with governance, consistency, and transactional integrity.
“It’s just a quick ask.” Anyone who has worked with data has heard this phrase—a seemingly simple request: “Can we get a slightly modified report?” “What’s the regional breakdown for this other region?” “Just pull the data.” It sounds like something that should take minutes, maybe hours
The way we handle data is fundamentally broken. Across industries, businesses are trapped in a cycle of centralization—pushing everything into a data lake, warehouse, or some shiny new “lakehouse.” Why? Because getting data out of systems is so painful that the instinct is to store it all in one
Data is the lifeblood of modern business, driving everything from customer insights to operational efficiency. Yet, despite all the shiny tools, tech, and talent, most companies are stumbling. They’ve built impressive systems, but when it comes to making data work, they’re failing. Badly. Let’s be clear: this
