Think of data engineering as plumbing for the digital world. All those smart dashboards, AI models and analytics reports don’t work unless someone builds the pipelines that bring clean, usable data to them. That someone is a data engineer. Why data engineers are suddenly in the spotlight AI is everywhere today
Financial fraud is a high-stakes issue in banking, where schemes are becoming increasingly sophisticated and costly. As a result, detecting anomalies quickly and accurately is a top priority. But traditional data-driven fraud detection models face challenges such as data scarcity, privacy constraints, and model bias. This is where synthetic data
The demand for public sector efficiency is higher than ever. Civil servants are working with tighter budgets, smaller teams and rising expectations. Despite their efforts, critics and stakeholders believe they could still do more. If there has ever been a time for a miracle, it is now. AI is a promising
Fast-paced technology like AI can have barriers to entry. Whether infrastructure, data limitations, talent gaps or complying with rapidly changing regulations. Organizations from health care to manufacturing and the public sector are often stymied by these obstacles that can slow AI adoption and use. Ready-made AI models can bypass the
AI is at its best when it is used to enhance productivity and improve the lives of those it affects. When used correctly, AI can also save lives. That’s the vision driving a new project at SAS, where applied AI models and cameras create a simulated work environment focused on
