Synthetic data – algorithmically generated data that mimics real-world data – has emerged as a cornerstone in modern AI workflows. But its promise comes with persistent myths about its capabilities, limitations and reliability. Synthetic data is being explored across industries, from training machine learning models to helping businesses safeguard customer
The future is already here and somehow, it keeps accelerating. Small to midsize businesses (SMBs), in particular, are feeling all the effects of this in real time. From shifting customer demands to tightening budgets and fiercer competition, SMBs are navigating more change than ever. Technology can be a powerful growth
Every AI success story starts with a single decision: to move beyond experimentation and commit to real-world impact. But moving from idea to enterprise-scale deployment isn’t just about algorithms – it’s about laying the right groundwork. In the first part of this series, we explored three ways to lay the
AI is only as powerful as the data behind it. You’ve probably heard the phrase “garbage in, garbage out.” That’s always been true in analytics, but in today’s AI-driven world, the consequences of poor data are greatly amplified. Flawed models, biased predictions and opaque decision-making all trace back to one
Back when I was in banking, I always looked forward to certain annual vendor meetings and reports. These key partners provided valuable insight into what was happening beyond the four walls of our institution. Their input was essential to helping us assess our market position and refine our strategy. Some
What if integrating your data for AI didn’t take weeks or months – but happened in minutes, without ever moving your data at all? That’s the vision guiding the development of a new data mapping agent at SAS. It’s not just a feature – it’s a potential shift in how
With a packed agenda of sessions, navigating a conference like SAS Innovate can feel overwhelming – especially for first-time attendees. Where to start? What do you mean I'll hear from inspiring and knowledgeable speakers and business leaders? There's hands-on experiences, too? No worries. After combing through the schedule, I’ve identified
Synthetic data has become a valuable resource in data science and machine learning. Superior quality, reliable synthetic data facilitates analysis and iteration at scale while mitigating privacy concerns associated with real data and can fill gaps where real data is scarce. Note, however, that “good” synthetic data is not defined
Where were you when the global banking crisis of 2008 hit? I was in banking – marketing, to be specific – and I supported our mortgage department. I vividly recall our bank’s chief risk officer repeatedly refusing to enter the adjustable-rate mortgage subprime lending business. Many banks were eating our
Learn how insurers can combat AI-driven fraud, secure data and build trust in an increasingly digital world.
