Analytics

Artificial intelligence (AI) is something that, by its very nature, can be surrounded by a sea of skepticism but also excitement and optimism when it comes to harnessing its power. With the arrival of the latest AI-powered technologies like large language models (LLMs) and generative AI (GenAI), there’s a vast amount of opportunities for innovation, growth, and improved business outcomes right around the corner. All of that technology, though, depends on data to be successful.

Regulations often get a bad rap. You may have heard the old idiom “cut the red tape” which means to circumvent obstacles like regulations or bureaucracy. But in many – if not most )– cases the underlying need for regulations outweighs the burden of compliance.

At Qlik, we're witnessing a thrilling shift in the landscape of data analysis, customer engagement, and decision-making processes, all thanks to the advent of generative AI, especially Large Language Models (LLMs). The potential for transformation across all sectors is enormous, but the journey toward integration can be daunting for many businesses with many leaders wondering where to start in integrating the exciting capabilities of AI into their daily workflows.

Data lakes can save your enterprise money but come with unique challenges. These tips will help you overcome three of the biggest issues.

Every company around the globe is trying to get in on the GenAI wave to simplify user experiences with natural language. And this is especially true in the realm of data and analytics. Imagine if you could enable all of your marketers to evaluate the performance of their campaigns with a simple question? Or, if you could provide all of your insurance risk managers with the ability to analyze the risk profile of their claims with the power of search and automated insights?

In today’s data-driven world, developer productivity is essential for organizations to build effective and reliable products, accelerate time to value, and fuel ongoing innovation. To deliver on these goals, developers must have the ability to manipulate and analyze information efficiently. Yet while SQL applications have long served as the gateway to access and manage data, Python has become the language of choice for most data teams, creating a disconnect.