Remember the early 2000s, when data was difficult to make sense of? It was like an exclusive domain of a few selected IT experts. Because back then, accessing and understanding data was a monumental task, loaded with delays and complexities. Today, data is a strategic asset for technical experts and business users. This shift is driven by the fact that we need answers NOW.

How are AI governance and data governance related? Better still, what’s more important for an organization to focus on, AI-powered data governance or AI data governance? These are important questions, but before we answer these, let’s understand how AI and data governance are related to each other.

The journey from a great idea for a Generative AI use case to deploying it in a production environment often resembles navigating a maze. Every turn presents new challenges—whether it’s technical hurdles, security concerns, or shifting priorities—that can stall progress or even force you to start over.

According to Stack Overflow’s recent survey, 62% of developers share a common concern – a growing and never-ending technical debt. Arpita Goala , Content Marketing Manager September 26th, 2024 Technical debt or tech debt is often an unintended and unavoidable consequence of software development. With the speed at which technologies and products evolve, technical debt is something that every quality assurance (QA) team must deal with.

While data lakes make it easy to store and analyze a wide variety of data types, they can become data swamps without the proper documentation and governance. Until you solve the biggest data lake challenges — tackling exponential big data growth, costs, and management complexity — efficient and reliable data analytics will remain out of reach.

Computational chemistry relies on large volumes of complex data in order to provide insights into new applications, whether it’s for electric vehicles or new battery development. With the emergence of generative AI (GenAI), the rapid, scalable processing of this data has become possible and critical to investigate previously unexplored areas in catalysis and materials science.

Software applications constantly evolve, with frequent updates, bug fixes, and new feature additions. However, these changes can introduce new issues or disrupt existing functionalities. Testlio September 26th, 2024 Explore the Differences Between Smoke Testing and Sanity Testing According to the Systems Sciences Institute at IBM, in addition to potential damage to your brand’s reputation, the cost of fixing a bug post-release can be up to 100 times more than during the design phase.