Not every company needs machine learning. In fact, most companies I’ve worked with don’t have an ML problem. They have a collaboration problem. We talk a lot about model performance, feature engineering, and MLOps. We talk far less about the friction between data engineers and data scientists that quietly derails projects long before a model
Most data failures aren’t technical. They’re relational. Poor data quality rarely destroys a company in one dramatic moment. It erodes trust slowly — in dashboards, forecasts, customer metrics, even in the data team itself. I’ve seen revenue decisions made on flawed attribution, pricing models built on inconsistent definitions, and boards lose confidence because numbers change
Most analytics failures don’t start with big architectural mistakes. They start with one bad data point that quietly flows downstream. By the time leadership notices, dashboards look “off,” trust erodes, and teams start debating numbers instead of decisions. The most common causes I see are: Upstream schema changes with no enforcement Late or partial data
Wherever you sit in your organization, you probably use dashboards to better understand what is happening in the business, and make decisions. But are your dashboards doing all they can to help you? Dashboards don’t create insight. They create VISIBILITY. And those are not the same thing. I’ve seen teams invest months perfecting dashboards, only
