Financial planning and analysis (FP&A) is recognized as an essential tool for driving business success. At a time when agility has taken center stage, though, FP&A is giving way to the new discipline of extended planning and analysis (xP&A). Why? Because in an increasingly digital landscape where agility is paramount, actionable real-time feedback is essential.

The current global landscape, represented by geopolitical tensions, economic instability, supply chain disruptions, and the pandemic’s long-term effects, has highlighted the significance of solid trade financing solutions. These issues have driven the need for financial products that reduce risks, secure payments, and streamline cash flow.

Artificial Intelligence (AI) is transforming industries, enhancing decision-making processes, and creating new opportunities across various domains. However, selecting the right type of AI for your specific needs can be challenging. Broadly, AI can be categorized into predictive (sometimes known as discriminative or traditional) AI and generative AI, each serving different purposes.

What are the differences between generative AI vs. large language models? How are these two buzzworthy technologies related? In this article, we’ll explore their connection. To help explain the concept, I asked ChatGPT to give me some analogies comparing generative AI to large language models (LLMs), and as the stand-in for generative AI, ChatGPT tried to take all the personality for itself.

Data is increasingly becoming critical for the public sector — from guiding decisions in higher education to enhancing citizen services and streamlining government operations. Government agencies are overwhelmed with data, whether it be structured, like incident logs, or unstructured, like satellite images. Harnessing the vast amount of data can become a burden for any organization, yet the insights have the potential to significantly improve quality of life and strengthen national security.

Shopping cart abandonment is the silent killer of eCommerce profits. Despite businesses investing heavily in customer acquisition and optimizing user experience, cart abandonments are still a major issue. And for every abandoned cart, potential revenue slips through the cracks. But what exactly causes shoppers to abandon their carts? Is it the unexpected shipping costs, a complicated checkout process, or something else entirely?

The new contribution analysis model in BigQuery lets you analyze metrics of interest from your dataset across defined test and control subsets.

Pipelines for continuous integration and continuous deployment, or CI/CD, are critical for contemporary software teams that aim for speed and agility. These pipelines, which automate the integration, testing, and deployment of code changes, enable teams to release new features and improvements quickly. There are obstacles involved in creating an effective CI/CD pipeline, though. This article explores common obstacles teams face in CI/CD implementation.

Modernizing legacy systems is often a daunting task, often filled with risks and uncertainties. The Strangler Pattern offers a proven, incremental approach to replace these outdated systems by gradually building new features around them, eventually phasing out the old system. The Strangler Pattern is a software modernization strategy that incrementally replaces parts of a legacy system with new functionality, allowing both systems to coexist until the legacy system is fully phased out.

Did you know? In the past decade, mobile app usage has evolved and grown tremendously all around the world. Not only that, according to a recent study, the SaaS industry has progressed from $31.5 billion to an estimated $171.9 billion in 2022 as per the report published by Exploding Topics. That equates to around 5x growth in only seven years. That’s incredible, isn’t it?