Applications of Data Science and Machine Learning in Business

Your data isn’t lacking infrastructure. It’s lacking impact.
Product thinking brings the focus back to business outcomes—by treating data like a product, not a project.

Blood platelets present a unique healthcare supply chain challenge with their critical importance and extremely short shelf life. By precisely forecasting platelet demand, healthcare providers can better balance patient needs with resource optimization, resulting in reduced waste, significant cost savings, and more reliable care. This case study explores how advanced analytics transforms healthcare logistics while maximizing the impact of every blood donation.

AI applications have taken off across industries. From chatbots generating creative content to AI-driven automation in finance and customer service, businesses are seeing real impact. But as AI adoption grows, so do the challenges—bias in decision-making, transparency concerns, and even security risks like deepfake fraud. How can organizations ensure AI delivers value while staying responsible?

Data lineage is more than a technical feature; it is a cornerstone for understanding how data flows, transforms and integrates across systems.