Artificial Intelligence (AI) is dominating the headlines. Rightfully so, because what evolves from the introduction of Large Language Models (LLMs) like OpenAI’s ChatGPT and Google’s Bard will arguably be transformational and disruptive to how we work. However, lost in the buzz is the foundational role of data analytics and business intelligence to AI. Together, they remain the cornerstone of the modern business.
If you listen to organizational leaders across any industry, it’s not uncommon to hear them say: “We are a technology company that does banking…or health care…or manufacturing.” And it’s true. Technology is at the core of how most organizations operate, and data, analytics, and cybersecurity are the three legs that hold it up. When combined effectively, they create a powerful framework with numerous benefits, from improved decision making to improved efficiency and an enhanced customer experience.
Data lakes play a crucial role in feeding business intelligence (BI) systems. It’s where data is collected, stored, and processed before being transformed into insights and information that can drive business decisions. Here are some best practices to consider when establishing a data lake.
Employee onboarding is a traditional human resource activity. It takes people and time. But what if you drew a “chance” card that shortcuts the process? Would you play it? Especially if there wasn’t much to chance and the reward could be reducing the time to onboard an employee from 128 minutes to 3 minutes? With RPA, you can do just that.
Data is a valued asset for most organizations. Most have more than they realize. Few know where it all is. Even fewer know how to leverage it to create opportunity. Then there’s the challenge of making sure it’s all secure. Here are four benefits of cloud data migration and a checklist to help you navigate your way to the cloud with minimal disruption and maximum success.
Data is a valued asset that can propel growth. How you collect and store it can create a risk profile you don’t fully understand—with potentially negative consequences. That’s why you need to assume you have toxic data until you can prove you can’t.