In an enterprise analytics team, different roles exist to fill different needs, and those needs must be met in order to be successful. Launching an analytics program doesn’t necessarily require a massive influx of personnel before producing usable insights from data, yet it’s important that critical roles are filled, whatever the size of the team. Multiple options exist for starting small and scaling up an analytics program, according to Evan…

Failures or lack of visibility in the many-tiered pharmaceutical supply chain have multiple repercussions. Drug shortages have adverse economic and clinical effects on patients — they are more likely to have increased out-of-pocket costs, rates of drug errors, and, yes, mortality. Hospitals and health systems allocate over 8.6 million hours of additional labor hours to manage drug shortages each year at a cost of at least $360 million, with alternative…

Many BI managers, CEOs, and CIOs cannot afford to add more staff, so they are seeking technologies that can help their existing teams operate more accurately and efficiently. They “need to change the physics, as we call it. They can’t just add more people to the team,” said Amnon Drori in a recent DATAVERSITY® interview. To have manual work done on premise using professional services combined with a variety of…

The data warehouse, a relational database technology, makes all enterprise information actionable, and will continue to be prominent as a Data Architecture component. In the 2000s, a typical business would consolidate data from multiple relational databases, centralizing all this information through a data warehouse, and consequently streamlining business tasks. However, the business context has shifted and continues to change from static and centralized to real-time and voluminous. Around the 2010s,…

DataOps (data operations) has its roots in the Agile philosophy. It relies heavily on automation, and focuses on improving the speed and accuracy of computer processing, including analytics, data access, integration, and quality control. DataOps started as a system of best practices, but has gradually matured to a fully functional approach for handling data analytics. Additionally, it relies on, and promotes, good communications between the analytics team and the information…

Data virtualization, in a nutshell, utilizes data integration without replication. In this process, a single "virtual" data layer is created to provide data services to multiple users and applications at the same time. Why Data Virtualization Is a Necessity for Enterprises explains how data virtualization helps tackle data movement challenges by making a virtual dataset available in real-time for analysis or processing, while the actual data remains in the source…