Dell PowerProtect Data Manager: Deployment Best Practices

whitePaper | July 12, 2022

In many ways, 2022 has been an exercise in dealing with the reeling of the Covid-19 pandemic. All over the world, supply chain issues have led to a myriad of challenges for enterprises, leading to deep shifts in how we do business and unraveling many of our assumptions about how society operates.

whitePaper | May 26, 2022

Clinical trials are becoming more complex and a confluence of forces—including the ongoing COVID-19 pandemic, greater emphasis on patient experience, increasing protocol complexity, and precise value differentiation—is accelerating this complexity. Trials have evolved to include an ever-expanding array of data sources, increased data volume and precision, decentralized clinical trials (DCT), and adaptive designs.

whitePaper | April 7, 2021

Assembling the perfect data stack is impossible. But for most data teams, the path to leveraging rapidly evolving tech and best-in-class tools is even more difficult when it’s impeded by the pitfalls of monolithic legacy applications.

whitePaper | December 9, 2022

In Data Governance Methodology, we described why a modern active data governance methodology is superior to a traditional top-down methodology and how active data governance is supported by continuous improvement. We also discussed how a data governance process based on continuous improvement is a key component of a DataOps process.

whitePaper | February 7, 2020

The problem is that growing volumes of data can lead to data paralysis, because no one knows quite where to begin or how to use it. Siloed information sources, no data management strategy, disconnected spreadsheets or analytical tools and poor data quality all work to compound the issues you’re facing.

whitePaper | October 17, 2022

Today, massive amounts of data are collected, processed, and stored for a range of analytical purposes around the world. Every customer, device, transaction, email, and image leaves a data trail. At present, this data is growing too big, changing too fast, and becoming hyper distributed. The traditional ways of doing integration and analytics are no longer viable or scalable. It is not feasible to create millions of data pipelines and to continue moving large amounts of raw data to a data lake or a centralized data warehouse.