Setting Up a Data Integration Pipeline for Repeatable Analytics Delivery

Metadata is more relevant than ever. It continues to be a powerful enabler for high-value data-driven business activities across operations, analytics, and compliance. However, to maintain this relevance, metadata management must deal with the increasing complexity of today’s business use cases and hybrid data environments. Metadata management is notoriously manual which makes it slow in development and error-prone in maintenance. Metadata management needs better tool automation. Metadata management is typically siloed due to users managing metadata per tool or platform. Metadata management needs a unified solution that is suited to sharing, reuse, governance, and comprehensive views of distributed data.

The ever-growing data landscape drives initiatives to automate many aspects of the analytics lifecycle; such as data access, enablement of semantics, BI and others. Automation has become an integral part of our daily lives in the enterprise data fabric. The AI-driven initiative to automate the data access and provide guidance to the right data assets, correlates with the initiatives of the data scientists to get access to more curated data.

A great comfort with cloud deployment has emerged. Businesses are migrating databases to the cloud or building databases there as a result of scale challenges with the on-premises model, the cloud becoming the “center of gravity”, on-premises databases reaching capacity or emerging uses cases that are specific to the cloud. But not all organizations! And some struggle mightily with the move!

Equifax analysis indicates that approximately 91.5 million consumers in the US either have no credit file, or have insufficient information in the file to generate a traditional credit score. Your visibility into credit applicants determines whether you will offer more loans or miss profitable opportunities. By giving you incre