The software testing market continues to grow, with a projected CAGR of five percent through 2027. With growth comes change, and the software testing landscape is poised to see significant changes this year. New technologies, evolving customer expectations, a global audience, and diverse devices are shaping a challenging environment for software quality assurance (QA) teams, who must maintain high quality without slowing down release rollouts or affecting the customer experience.

In a business environment where automation is becoming increasingly essential, understanding the trajectory of robotic process automation (RPA) is more important than ever. But what exactly can we expect from the future of RPA in 2024? How will the bots themselves change? How will the market change? And what will these changes mean for buyers? In this post, we’ll look at the changes you can expect to see in RPA this year.

2023 was a big year for developers, with technology taking huge leaps forward in new and exciting areas like AI. With this new unlocked potential, advanced analytics jumped back to the top of the capability wish list for many technology buyers. With customers now expecting more than ever from analytics, many development teams invested in embedded analytics solutions to reduce the workload and time to value for their applications. Here are some of the top trends from last year in embedded analytics.

Recently at re:Invent, Amazon unveiled S3 Express One Zone for AWS. Express Zone for S3 responds to the demand for faster analytical query speeds, with the convenience of centrally storing all of your application telemetry data in cloud object storage. In the past, for data-intensive applications, data access speeds were slower than desired.

The heightened demand for agile scaling across enterprises in contemporary times is underscored by several key factors, including the rapid pace of product changes within organizations, evolving customer expectations and market dynamics, the imperative of digital transformation and innovation, the risks associated with falling behind in competition, the inherent advantages of adopting agile methodologies, and the pressing need to adapt or face the consequences of obsolescence.

The traditional approach to private or internal Application Programming Interface (API) integrations, characterized by labor-intensive and error-prone manual processes, is rapidly becoming obsolete. This method, limited by its time-consuming nature and lack of scalability, is giving way to an era of automation. Automated API integrations marks a significant leap forward, offering unparalleled efficiency, accuracy, and the ability to evolve.

Integrating Artificial Intelligence (AI) in software testing is a major advancement in software development, enhancing efficiency and accuracy while handling complex scenarios. This technological leap introduces significant ethical challenges, such as concerns over data misuse and the need for algorithmic transparency. Understanding and addressing these issues is crucial for fostering responsible innovation in AI.

In the previous blog, the Yellowfin Japan team used Yellowfin's Data Transformation Flow feature to create a database of the CSV file of order history data. In this article, the team will demonstrate how to leverage the database for analysis by processing and formatting said data using native Yellowfin features, including View and Report Builder.

Advanced analytics help manufacturers extract insights from their data and improve operations and decision-making. But for manufacturers, it’s often challenging to perform analytics with ERP data. Because of the high rate of M&A activity in the industry, manufacturing enterprises often struggle with multiple ERP instances. A fragmented resource planning system causes data silos, making enterprise-wide visibility virtually impossible.

Quality Assurance (QA) professionals, comprising QA managers, testers, and developers, form the backbone of the software development lifecycle. Their software testing role is pivotal in ensuring the delivery of robust and error-free software. However, this journey is rife with challenges that demand innovative solutions and collaborative efforts. In this blog post, we'll delve deeper into the distinctive pain points experienced by each group within the QA ecosystem.