Welcome back to our series on dealing with imperfect data. Last week, we discussed how to turn bare-bones datasets into directional recommendations that help stakeholders generally understand where opportunities and issues lie. But those tips and tricks will only take you so far. Relying on thin datasets is a great way to begin an analysis […]

Imagine stepping into a room full of executives eager to hear the results of the past quarter’s marketing efforts. The marketing team steps up first, proudly presenting a glowing dashboard with positive brand perception numbers. Customer satisfaction scores are high, clients return more often than not, and existing customers constantly recommend your company. As the […]

Picture this: It’s 4:45 PM on a Thursday when a stakeholder messages you with an urgent request for Friday morning’s board meeting. You don’t have time to do an in-depth analysis, build a trustworthy dataset, or write tested code. Instead, you write a quick-and-dirty SQL query, paste the results into Excel, manually check for mistakes, […]

Recently, we explored the ethical frameworks required to govern artificial intelligence safely. This week, we pull back the curtain on the machine itself. To use these tools well, you don’t need a PhD in machine learning, but you do need to understand the handful of mathematical processes that determine what an LLM says. Opening up […]

In our recent discussions on AI ethics, we established that automated systems lack human judgment and need strict boundaries. Yet across industries, tech teams routinely roll out artificial intelligence tools without understanding how the human brain actually interacts with them. This gap leads to embarrassing and costly failures—like an airline chatbot making up refund policies […]