Systems | Development | Analytics | API | Testing

A report by Bloomberg Intelligence projects the AI industry will reach $1.3 trillion by 2032, with proprietary data fueling much of this growth. As businesses increasingly adopt generative AI (genAI) to enhance efficiency, data is rapidly becoming one of the most valuable assets in the digital economy. Foundational AI models require vast amounts of data for training, and many AI products are now leveraging proprietary datasets alongside these models to power innovative applications and AI agents.

With the advent of modern Large Language Models (LLMs), Retrieval Augmented Generation (RAG) has become a de-facto technology choice, employed to extract insights from a variety of data sources using natural language queries. RAG combined with LLMs presents many new possibilities for integrating Generative AI capabilities within existing business applications, specifically opening up many new use cases within the data streaming and analytics space.

Imagine workflows so intelligent they can adapt to changing conditions, solve problems autonomously, and collaborate seamlessly across teams – all while freeing up your time for the tasks that truly matter. This isn’t science fiction; it’s the promise of agentic workflows. As the software development world races to keep up with evolving demands, agentic workflows represent a revolutionary leap, offering a smarter, faster, and more adaptive approach to managing complexity.

LLM Data Gateways are specialized tools that prepare and secure data for AI systems, ensuring better performance, compliance, and cost efficiency. They act as a bridge between raw data and large language models (LLMs), solving common challenges in AI like poor data quality and security risks.

Some 70% of organizations are actively exploring or implementing large language model (LLM) use cases, but fewer than a third of generative AI experiments have made it into production. A common hurdle? The inability to access and leverage the data crucial for running AI applications effectively. Snowflake’s Accelerate 2025 virtual events dive into the challenges and myriad opportunities offered by AI.

Artificial Intelligence (AI) is transforming the way businesses manage, process, and analyze data. AI Data Management involves the use of machine learning (ML), automation, and intelligent data pipelines to enhance data storage, governance, integration, and security. As organizations deal with ever-growing datasets, AI-driven data management solutions ensure efficiency, scalability, and accuracy.

I recently saw a post on LinkedIn that said, “Qlik isn’t the same company it was in 2016.” I’m pretty sure that it wasn’t meant as a compliment. But here’s the thing: they’re right. And that’s a good thing. Because if we were the same company we were in 2016, we wouldn’t be prepared for the challenges businesses are facing today. The world of data and AI has changed. Businesses have changed. So, of course, Qlik has changed too.

Lineos reduces manual tasks and empowers finance teams to boost productivity and uncover hidden potential within their data RALEIGH, N.C. – Feb. 26, 2025 – insightsoftware, the most comprehensive provider of solutions for the Office of the CFO, today announced the launch of Lineos, a suite of AI-driven capabilities designed to enhance insightsoftware’s financial planning and analysis (FP&A), accounting, and operations products.

Generative AI has the potential to add up to $4.4 trillion annually to the global economy. But most organizations won’t see that value—not because of their models or infrastructure, but because of their data. Despite years of investment in data lakes, warehouses, and analytics tools, organizations are drowning in complexity. Data is scattered across siloed systems, riddled with duplication, and locked behind outdated batch processes.

Penetration testing is like a virtual security guard for your organization’s cybersecurity. It detects vulnerabilities before malicious attackers can exploit them. Traditionally, this process relied on skilled professionals manually probing systems for weak spots. However, with the rapid evolution of technology and the surge in cyber threats, the need for smarter, faster, and more adaptive testing methods has never been clearer.