Systems | Development | Analytics | API | Testing

If you are building or scaling digital products, chances are your QA process already includes gig testers. You post a task, someone across the globe picks it up, files a bug, and moves on.

In today’s fast-moving world of digital innovation, where updates happen super fast and users just want everything to work, regression testing quietly but importantly keeps software dependable. In this guide, we’ll dive into all you need to know about regression testing.

Today, mobile apps are downloaded by millions of users. Thousands more people will have made a digital decision by the time you read this, one that might not have been in your best interests (if you have one). In a world where millions of apps are downloaded every day, consider whether your company is leading the way or just keeping up. The rate of change is accelerating as AI, IoT, AR/VR, cybersecurity, automation, and cloud computing transform every aspect of business.

Recently, I had the pleasure of sitting down with Cristiano Caetano, VP of Product at Katalon, to explore his journey in software testing, a path that began with frustration, evolved through innovation, and now looks toward an exciting AI-driven future. Cristiano's insights shed light not only on his personal career path but also on the broader evolution and future of software testing.

Insurers are facing tighter margins, rising costs, and pressure to modernize, which are all challenges that traditional levers alone can no longer solve. Amidst the struggle, Generative AI offers a breakthrough. With the potential to add $2.6 to $4.4 trillion annually to the global economy, which is higher than the UK’s GDP, it can redefine how insurers create value.

Insurers today face intensifying pressure on multiple fronts. Operating margins have compressed by as much as 3 percent over the past five years, driven by deteriorating loss ratios, sustained inflation, and escalating competition from insurtech challengers. At the same time, legacy infrastructure and fragmented delivery models continue to hinder responsiveness, with product development cycles still exceeding 12 months for many carriers.

In February 2024, Kong became the first API platform to launch a dedicated AI gateway, designed to bring production-grade performance, observability, and policy enforcement to GenAI workloads. At its core, Kong’s AI Gateway provides a universal API to enable platform teams to centrally secure and govern traffic to LLMs, AI agents, and MCP servers. Additionally, as AI adoption in your organization begins to skyrocket, so do AI usage costs.

The digital landscape is changing. More and more, consumers are realising the importance of data privacy. This shift in mindset is something businesses must attune to if they hope to build strong relationships with their customers. The phasing out of third-party cookies by Google at the end of 2024 and global regulations like GDPR and CCPA tightening data collection mean companies that embed privacy as a core part of their operations have the most to gain.

Choosing one over the other can come down to a few key differences. Let’s take a look at both to help you make an informed decision.

Data completeness in ETL pipelines refers to whether all expected data has been successfully processed without missing values or records. The Data Completeness Index (DCI) is a metric that quantifies the percentage of complete data fields in your ETL processes, helping organizations identify gaps that could lead to faulty analytics or business decisions. When your data completeness testing in ETL processes reveals a high DCI score, it indicates reliable data that stakeholders can confidently use.