Blaze Architecture and Troubleshooting in Informatica BDM

Citrix, a Fortune 1000 multinational software company, needed to migrate massive amounts of data generated by its FileShare platform to Amazon Redshift, where it could perform data analytics to help inform product improvements and drive success. To do this, Citrix needed an Extract, Transform, and Load (ETL) solution. Matillion ETL, easily deployable from Amazon Web Services (AWS) Marketplace, helps Citrix collate and summarize data and augment it with more traditional business data from Microsoft SQL Server for additional context. Join our webinar to learn how organizations of any size can move data to the cloud quickly, accurately, and affordably with Matillion ETL.

The need to monetize data has become a strategic imperative for businesses undergoing digital transformation, whether it’s using data to improve customer engagement, identify compelling opportunities or deliver actionable insight. However, as businesses look to expand revenue-generating, customer-facing applications beyond operational processing to include analytics, they find themselves outgrowing the their transactional database. They need full operational and full analytic capabilities.

If your organization is in a highly-regulated industry or relies on data for competitive advantage data governance is undoubtedly a top priority. Whether you’re focused on “defensive” data governance (supporting regulatory compliance and risk management) or “offensive” data governance (extracting the maximum value from your data assets, and minimizing the cost of bad data), data quality plays a critical role in ensuring success.

Roche, as a leading biopharmaceutical company and member of the Pistoia Alliance, has a diverse and distributed ecosystem of platforms to manage reference data standards used at different parts of the organization. These diverse reference data standards include ontologies and vocabularies to capture specifics of the research environment and also to describe how clinical trial data are collected, tabulated, analyzed, and finally submitted to regulatory authorities. In the context of the EDIS program, Roche has bridged these parts to improve reverse translation from studies into research and also embraced FAIR to emphasize machine-actionability and data-driven processes.