Systems | Development | Analytics | API | Testing

Data analytics and science professions have undergone a dramatic transformation in the last decade. Demand for data talent has risen steadily, but the skills required to succeed in the field have evolved right alongside it. And now AI is a defining variable shaping what the next decade of analytics work will look like. It speeds up routine tasks, gives data access to more business users, and raises new questions about what skills will matter most in the not-so-distant future.

Software defects don’t just drain budgets; they cost businesses customer loyalty. Poor software quality can lead to poor customer experience and drive customer churn, resulting in missed revenue and increased expenses. Learn how better processes, such as test data management, can reduce the cost of software defects and protect your business outcomes.

I worked in observability for years, and the same pattern showed up across teams. An alert fired, the on-call rotation scrambled, and everyone did what they had to do to stabilize production. Then came the retrospective. Once the immediate pressure was gone, the conversation shifted to one question: how do we make sure this never happens again? My friend Jade Rubick coined a name for that principle: DRI, “don’t repeat the incident”.

The quality tax isn't primarily a resourcing problem. It's a paradigm problem. And paradigm problems don't yield to incremental solutions.

Katalon is introducing a new MSP Program designed for our official solution and service partners. Built for partners delivering QA services across multiple customer engagements, the True Platform MSP Program offers a more flexible way to scale delivery with Katalon’s all-in-one testing platform.

In 2026, APIs have moved far beyond simple integration points. They’re now strategic business assets powering AI transformation, microservices architectures, and multi-cloud ecosystems. But a critical challenge threatens to undermine digital initiatives: the fragmentation of API testing. As organizations rush to deliver faster, they’re discovering that their testing infrastructure – cobbled together from disparate tools and disconnected processes – has become the bottleneck.

With the rise of AI use in vehicle software development, concerns arise around its presence in safety-critical applications, especially when it comes to functional safety and regulatory compliance. ISO 26262, the essential standard for automotive development that requires processes for managing, designing, and verifying safety-critical systems, still applies. However, it can fall short when applied to AI models, which are inherently non-deterministic and continuously evolving.