Big Data and Analytics in Pharma

The Eckerson Group's Dave Wells and Cloudera's Eva Nahiri share the challenges of legacy data warehousing, the goals of modern data warehousing, and the design patterns and frameworks that help to accelerate modernization efforts.

As technology adoption increases exponentially, organizations are challenged by the proliferation of data that the technology generates. Increasingly, Shared Services Global In-house Centers (GICs) are leading their organizations’ efforts to tame data and derive key insights from it. Based on our recent Pinnacle Model research on data & analytics maturity in SSCs/GICs, this webinar will show executives how they can build capabilities in their SSCs GICs to turn this challenge into a strategic asset, generating value and enhancing service 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.

Analytics have come a long way – from data warehousing and manual report building to centralised data in the cloud and machine learning driven analytics. Business Intelligence (BI) tools were revolutionary when they arrived: they made analytics more accessible to the wider business users. Their speedy and powerful analytics provided insights that businesses only dreamt off in the past. But as technology advances, so has the analytics world evolved.