Data practitioners are consistently asked to deliver more with less, and although most executives recognize the value of innovating with data, the reality is that most data teams spend the majority of their time responding to support tickets for data access, performance and troubleshooting, and other mundane activities. At the heart of this backlog of requests is this: data is hard to work with, and it’s made even harder when users need to work to get or find what they need.

Broadly defined, the supply chain management process (SCM) refers to the coordination of all activities amongst participants in the supply chain, such as sourcing and procurement of raw materials, manufacturing, distribution center coordination, and sales.

With organizations prioritizing data-driven decision-making, the amount of collected and stored data is reaching historic highs. Meanwhile, organizations are democratizing access across all functions to convert this data into actionable insights. Since more users will work with sensitive data, ensuring secure access is more important than ever. Organizations must regulate and maintain the relationship between their data assets and users. Why?

Ready or not, AI workflow automation is poised to transform business operations from the shop floor to the C-suite in the AI economy. As organizations embrace digital-first initiatives, IT teams will be able to do much more with less. The situation is a byproduct of the generative AI boom. And yet, so many companies have hardly scratched the surface of AI automation’s full potential in their business operations.

In today’s fast-paced business environment, having control over your data can be the difference between success and stagnation. Leaning on Master Data Management (MDM), the creation of a single, reliable source of master data, ensures the uniformity, accuracy, stewardship, and accountability of shared data assets.

Today’s enterprise networks are complex. Potential attackers have a wide variety of access points, particularly in cloud-based or multi-cloud environments. Modern threat hunters have the challenge of wading through vast amounts of data in an effort to separate the signal from the noise. That’s where a security data lake can come into play.