Systems | Development | Analytics | API | Testing

As we assess the technological landscape for the new year, one of the only guarantees is that technology will continue to evolve. Cloud migration and support are top-of-mind for worldwide organizations–this year, cloud computing is forecast to surpass $1 trillion worldwide for the first time. Cloud-based ERPs reduce operating costs, can help automate processes, and provide finance teams with greater autonomy.

Imagine seamlessly integrating your Google Analytics data with Snowflake, allowing you to combine it effortlessly with other key sources like CRM, ERP, social media metrics, email campaign data, and whatever data sources compose the full scope of your data estate. The good news is that it’s possible with the native Snowflake Connector for Google Analytics, now available in public preview.

If you’ve done any work on iOS apps these last few years, you’ll know how concise and expressive the Swift language can be. And you may well have benefited from Swift extensions, a powerful mechanism that lets you, the developer, extend both your functionality and Swift’s, by extending any Objects and Structures you need. However, the variety and versatility of extensions does present teething challenges.

In the rapidly emerging artificial intelligence economy, organizations will split into two groups: those who are good at AI and those who are bad at business. Most experts agree that AI won’t replace humans, but instead augment us in a world of mixed autonomy. You’ll need new structures to harness AI’s transformative potential while managing its very real risks—the biggest of which is data privacy. So how can leaders handle AI and privacy risks?

This year may be the most innovative on record. Recent advances in AI are beginning to transform how we live and work. And the potential impacts of artificial intelligence (AI) on the healthcare and life sciences industries are expected to be far-reaching. It’s essential for organizations to leverage vast amounts of structured and unstructured data for effective generative AI (gen AI) solutions that deliver a clear return on investment.