Data

As organizations strive to compete in a dynamic environment, they are looking to modernize their data and analytics environment to help. This modernization includes implementing new technologies such as scalable cloud platforms and unified approaches. It includes utilizing more advanced analytics such as geospatial analytics and machine learning. It includes new paradigms such as the data fabric and the data mesh. Moreover, as part of this, modernization may include new organizational constructs such as the data office and new teams such as DataOps, MLOps, and data literacy enablement teams.

Are organizations ready for the real-time revolution? Somewhat - but they are only just beginning to embrace it. Typically, business decision makers have had to analyze historical data to assemble patterns that might predict upcoming events. However, with the advent of real-time data and analytics, insights on present events, and the use of leading-edge AI technologies, inferences may be drawn about future events.

The demand for analytics continues to grow. More organizations want to advance beyond dashboards to self-service analytics and more sophisticated algorithms such as machine learning.To support this trend, enterprises are moving towards a unified environment for data and analytics. This platform is often cloud-based to take advantage of scale and flexibility and support massive amounts of data and compute-intensive workloads. It is automated and augmented to help address data and analytics complexity.This TDWI Best Practices Report examines the adoption, use, challenges, architectures, and...