Lindsey Coombs is a Senior Editor for data and AI at SAS. She researches and writes on topics covering advanced analytics and evolving tech like generative AI. Lindsey is a seasoned communicator with more than 18 years of experience writing content for a broad range of industries and audiences. She is passionate about the safe and ethical use of technology that benefits humanity.
AI gets the spotlight. Data does the work. Across industries, organizations are racing to adopt large language models (LLMs), build AI assistants and explore autonomous agents. This ambition is understandable. AI promises faster decisions, new efficiencies and entirely new ways of working. But amid the excitement, many leaders are overlooking
Improve AI readiness, governance and trust by establishing a modern data estate.
Leaders are increasingly preparing their organizations for the latest AI technology. The problem? They aren’t ready and struggle to implement and show ROI. Despite widespread recognition of AI’s potential, a critical trust gap has emerged in the enterprise landscape. A report by the Boston Consulting Group (BCG) reveals that 74%
Back in the 80s and 90s, life was simple. Kids roamed the neighborhood. Parents didn’t track them with GPS. Commercials interrupted your TV shows and you watched them. If you wanted to know if Ross and Rachel got back together, you waited – patiently – for the next episode. Research
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. SAS models can bypass the challenges
In 2012, Harvard Business Review declared the data scientist the sexiest job of the 21st century. Here’s what we knew at the time: big data was (and still is to this day) an enormous opportunity to make new discoveries. We were in the boom of user-generated content from social platforms,
Data scarcity, privacy and bias are just a few reasons why synthetic data is becoming increasingly important. In this Q&A, Brett Wujek, Senior Manager of Product Strategy at SAS, explains why synthetic data will redefine data management and speed up the production of AI and machine learning models while cutting
Developers and modelers face challenges when finding and validating data, collaborating across groups, and transferring work to an enterprise platform. Using a self-service, on-demand compute environment for data analysis and machine learning models increases productivity and performance while minimizing IT support and cost. In this Q&A, Joe Madden, Senior Product
Let me ask: did you believe Facebook CEO Mark Zuckerberg really boasted about the company’s power in a 2019 video, saying, “Imagine this for a second: One man, with total control of billions of people’s stolen data, all their secrets, their lives, their futures.” The video of Zuckerberg purportedly saying
