What drives organizations to make the tough choices between speeding up releases, cutting costs, boosting product quality, and sparing time for innovation? One of the critical reasons is the time consumed in maintaining failed test cases and the inability to leverage AI in testing. Test automation is proving ineffective and outdated due to the high cost and time needed for maintenance.

As we venture deeper into the digital age, cloud infrastructure continues to evolve rapidly, pushing the boundaries of what’s possible for businesses and individuals. By 2025, cloud computing will not just be a tool but a cornerstone of innovation and efficiency, driving significant technological advancements. Let’s explore the critical trends poised to shape the future of cloud infrastructure, including serverless computing, edge computing, and AI-driven management.

As the mobile app market continues to expand, the demand for robust and efficient testing solutions has never been greater. Traditional testing methods are proving inadequate in addressing the complexities and speed requirements of modern mobile applications. This blog explores how cloud mobile testing is transforming quality assurance (QA) in the mobile era, offering scalable, flexible, and cost-effective solutions that meet the demands of today’s fast-paced development environments.

In the field of Natural Language Processing (NLP), two prominent terms frequently arise: Small Language Models (SLMs) and Large Language Models (LLMs). While both play significant roles across various industries, including healthcare and life sciences (HC-LS), their applications and advantages diverge depending on the context and requirements of the task.

In today’s digital age, the internet serves as a critical platform for information, communication, and commerce. The World Health Organization (WHO) estimates that 1.3 billion individuals, or 16% of the global population, have a major impairment. According to the Centers for Disease Control and Prevention (CDC), 27% of people, or approximately one in every four, have a handicap.

Property and casualty insurers are profoundly affected by climate events like hurricanes, wildfires, and floods, which can cause extensive damage and lead to substantial claim payouts. As a result, insurers must continually reevaluate and adjust their risk models, pricing, and technology strategies to adapt to the evolving climate landscape.

The surge in data creation and interactions over the past few decades presents unprecedented challenges for organizations. Many are grappling with widely dispersed applications and data to navigate this data deluge. Gartner’s survey found that organizations with poor data quality would incur losses of an average of $15 million per year. The findings also shared that almost 60% of organizations surveyed were unaware of how much insufficient data costs their business due to a lack of measurement.

As per recent research, within the next five years, 88% of organizations will integrate intelligent services into their offerings. Moreover, 43% of these organizations anticipate that over 20% of their revenue will be derived from intelligent services within the next three years. The two driving forces propelling the transition and growth of intelligent services, fostering a future that is technologically advanced and environmentally conscious, are.

In the highly competitive healthcare industry, optimizing operational efficiency and enhancing patient care are paramount. A leading healthcare provider in the APAC region, known for running one of the largest hospital chains, faced a significant challenge: a drop in inpatient volumes coupled with rising operating costs.

Given its intricate and cross-system nature, effectively managing master data necessitates a robust technological infrastructure. The process begins with data collection, utilizing Extract, Transform, Load (ETL) and Extract, Load, Transform (ELT) processes for normalization and integration.