Big data is more crucial to businesses than ever, especially with the rise of artificial intelligence (AI) and machine learning technologies across multiple industries. The expanding internet of things (IoT) plus the multiple digital engagement points between businesses and customers create huge swathes of data and new challenges in how to manage this glut of information.

The rise of generative AI and the massive popularity of OpenAI’s ChatGPT has led to widespread recognition that software applications are about to fundamentally change. Generative AI offers the potential to both deliver breakthrough new application capabilities and transform the way people interact with software.

Helping companies leverage their data to make better decisions and improve their performance is something we strive for at Databox. Until recently, this type of analysis has only been reserved for larger enterprises with dedicated teams and budgets for complex tools. Fortunately, recent innovations are reshaping this landscape.

Automated testing has emerged as a prominent practice that leverages machines (compute power, scripts, machine learning, genAI) to validate the quality of a software application. With the promise of increased efficiency, broadened test coverage, and accelerated software delivery, automated testing has become a cornerstone initiative for modern software engineering teams.

Next year, finance teams utilizing SAP will face unparalleled pressure due to a confluence of economic challenges. The demand for actionable insights will grow amidst a landscape marred by economic recession and escalating inflation rates. These adversities heighten the necessity for finance professionals to adeptly translate raw data into strategic guidance.

Visionaries from Capgemini, Databricks and Fivetran lay out the data quality imperative for implementing enterprise AI applications.

When WebSockets were first announced in 2011, they had some teething problems. As a result of these teething problems, only the most daring and experimental developers would rely solely on WebSockets for realtime communication. For most of us, it became a necessity to implement a fallback protocol in case the WebSocket connection can’t be established.

With its simplicity, flexibility, and cost-efficient characteristics, Amazon Simple Storage Service (Amazon S3) cloud object storage has become the preferred platform for collecting, analyzing, and retaining today’s growing mountain of diverse and disjointed enterprise data. And as Amazon Web Services (AWS) continues to grab market share in the hyperscale IaaS/PaaS/SaaS marketplace, organizations of every size are leveraging Amazon S3 to underpin a variety of use cases, such as.

Growing any business is difficult, but scaling a software as a service (SaaS) company is on a whole other level. Most SaaS companies struggle to achieve predictable revenue growth, while even public SaaS companies struggle to achieve profitability. To make a SaaS company successful, you can’t just change your software delivery model to the web and expect it all to work. You have to make thoughtful, data-driven decisions when it comes to your marketing, sales, and customer success operations.

The rapid development and deployment of software applications largely rely on the power of APIs. These Application Programming Interfaces act as the glue holding together various components, enabling efficient communication and data exchange between them. From mobile apps to web services and enterprise systems, APIs are ubiquitous, playing a critical role in the seamless functioning of modern software architecture – but its important to take internal API security risks seriously.