The management of data assets in multiple clouds is introducing new data governance requirements, and it is both useful and instructive to have a view from the TM Forum to help navigate the changes.

We are now well into 2022 and the megatrends that drove the last decade in data—The Apache Software Foundation as a primary innovation vehicle for big data, the arrival of cloud computing, and the debut of cheap distributed storage—have now converged and offer clear patterns for competitive advantage for vendors and value for customers.

In the wake of the disruption caused by the world’s turbulence over the past few years, the telecommunications industry has come out reasonably unscathed. There remain challenges in workforce management, particularly in call centers, and order backlogs for fiber broadband and other physical infrastructure are being worked through. But digital transformation programs are accelerating, services innovation around 5G is continuing apace, and results to the stock market have been robust.

Apache HBase has long been the database of choice for business-critical applications across industries. This is primarily because HBase provides unmatched scale, performance, and fault-tolerance that few other databases can come close to. Think petabytes of data spread across trillions of rows, ready for consumption in real-time.

Data is the fuel that drives government, enables transparency, and powers citizen services. But while state and local governments seek to improve policies, decision making, and the services constituents rely upon, data silos create accessibility and sharing challenges that hinder public sector agencies from transforming their data into a strategic asset and leveraging it for the common good.

Firms are burdened with tech debt and endless regulatory compliance, often leaving innovation last to receive the necessary budgets. Data-fuelled innovation requires a pragmatic strategy. This blog lays out some steps to help you incrementally advance efforts to be a more data-driven, customer-centric organization.

Cloudera Machine Learning (CML) is a cloud-native and hybrid-friendly machine learning platform. It unifies self-service data science and data engineering in a single, portable service as part of an enterprise data cloud for multi-function analytics on data anywhere. CML empowers organizations to build and deploy machine learning and AI capabilities for business at scale, efficiently and securely, anywhere they want.