Systems | Development | Analytics | API | Testing

For data analysts and engineers, the journey from raw data to actionable business insights for business users is never as simple as it sounds. The semantic layer is a critical component in this process, serving as the bridge between complex data sources and the business logic required for informed decision-making. However, not all semantic layers are created equal, and the evolving landscape of AI-powered analytics demands a new approach.

The demand for AI-driven applications is surging, and enterprises have reached an inflection point where they can no longer afford fragmented, siloed development. Traditionally, AI development is done by data scientists or machine learning experts with deep expertise in multiple tools and frameworks.

How does GDPR impact AI innovation, and what affects might AI have on regulations like GDPR? According to McKinsey, 78% of companies now use AI in at least one area of their business as of July 2024. But this quick adoption brings challenges for organisations handling data from the European Union and the UK. The main challenge for InfoSec and other enterprise leaders is clear. Using AI effectively means being able to develop faster.

When you think of artificial intelligence in transportation, what’s the first image that comes to mind? Is it self-driving cars smoothly cruising city streets, or maybe delivery drones zipping through the skies? While the dream of fully autonomous vehicles might still be on the horizon, the reality is that AI is already reshaping how we move, every single day. Look how our Co-founder is enjoying the view in a self-driving car in the USA! ‍ Yes, you saw that right, it’s fully autonomous.

Agriculture has long been the backbone of human survival, proof of our deep connection with and dependence on nature. But as the world evolves, so do the challenges in farming. From shrinking arable land to unpredictable weather patterns, how can farmers keep up? How do we ensure there's enough food for future generations? There’s no doubt that farming has come a long way, but let’s be honest, it’s getting harder every year.

At ThoughtSpot, we’re on a mission to empower every business user to become a data champion. Over the past year, I’ve witnessed firsthand how organizations across Australia and New Zealand are embracing this vision, transforming the way they work, make decisions, and serve their customers. Today, I’m excited to share some of the incredible momentum we’re seeing in the region and to celebrate the forward-thinking organizations leading the charge.

AI is everywhere. Depending on who you ask, it’s either making developers obsolete, or it’s just hype. But for those of us who’ve actually used AI tools in real-world engineering workflows, especially in complex distributed systems, the truth lies somewhere in between. At Multiplayer, we’ve spent the past few years exploring how AI can—and can’t—help solve two of the most persistent challenges in distributed systems: documentation and debugging.

Gen AI call center co-pilots can provide enterprises with operational visibility and insights while automating repetitive tasks, to improve the customer experience. In this session, we’ll show how a large health insurance provider implemented an agentic co-pilot designed scale across multiple call centers and environments. To dive deep into the architecture and see a demo of the co-pilot, you can watch the webinar this blog is based on.