As organizations look to revolutionize how they analyze and utilize data, modernizing the data-centric technology stack is critical to success. Today, the traditional stack poses several challenges—too many steps, too many tools, and too many integrations—all leading to operational complexity, time delays, and high cost. Simplifying the data pipeline, data lifecycle, and data stack offer organizations improved efficiency as well as cost savings and provide them more value by freeing up resources to focus on deriving insight through the analysis of data.
As organizations begin to transform their approach to analytics, modernizing the analytics stack is a top priority. To address both the data supply and data demand, data teams must look for ways to simplify and optimize the data pipeline.
That means transitioning traditional solutions, like data warehouses and business intelligence tools, to modern architectures built with scalability, agility, and availability in mind. And what follows with a modernized stack is an ideal data experience rich in actionable insight, API-driven application integration, and the ability to address the real-time needs of a dynamic business.