Receiving the highest scores possible in 14 criteria, Confluent was recognised for strengths in messaging, processing, and event-driven enterprise applications.
In an era of economic uncertainty and rapid digital transformation, finance and accounting professionals face a paradox: their data has never been more abundant, yet access to it has never been more challenging. Kevin Gibson, CPA and Principal Solutions Engineer at insightsoftware, recently sat down with Scott Taylor on an episode of the Don’t Panic!
Artificial intelligence is changing how every team, from startups to global brands, builds, learns, and competes. But AI is only as powerful as the data it’s built on. That’s why we’ve rebuilt Countly from the ground up, to make data faster, smarter, and truly AI-ready.With Countly 26.01, we’re introducing our next-generation data engine: This release marks a turning point for Countly: from analytics to intelligence.
Ready to unlock hidden insights in your Jira data? This quick tutorial shows you exactly how to use Analyst Studio’s Python Notebook to go from raw Jira API data to a live, queryable ThoughtSpot Model!
Enterprise leaders are racing to deploy generative AI, but most overlook a critical foundation: data readiness. Despite bold ambitions, less than one-third of technology leaders believe their data is prepared to support AI at scale. Marcela Vairo, IBM’s Vice President of Data and AI for the Americas, joins The AI Forecast to explore one of the most persistent challenges in enterprise AI: why strong data foundations remain the exception, not the rule.
In 2023, the EU introduced measures to ensure there will be focus on the gender pay gap and at the same time strengthen employee rights, especially for companies that have more than 100 employees. For businesses and specifically HR Professionals, this directive coming into force in the mid-point of 2026 creates several challenges that require both operational and strategic responses.
In today's hyper-competitive digital world, software is no longer a basic, single thing. We live in a world with distributed architectures, microservices, cloud-native environments, and complicated API ecosystems. This complexity has changed how we produce things, but it has also broken down conventional, walled-off ways of ensuring quality, pushing the boundaries of traditional software testing services.
Automation has always been at the heart of efficient testing. It speeds up validation, eliminates repetitive steps, and helps teams catch issues earlier. However, even with great automation frameworks in place, one challenge persists: how to quickly and accurately convert manual tests into automated scripts? For most teams, this process still requires time, technical skill, and lots of rewriting.
Data lives everywhere—from flexible Google Sheets to massive cloud data warehouses. How do you bring it all together for unified analysis? This video walks you through the power of ThoughtSpot Analyst Studio to handle complex, multi-source data modeling without leaving the platform. In this tutorial, you will master: Stop choosing between flexibility and scale.