Data Lake Whitepaper

whitePaper | September 12, 2022

Clinical Data Management (CDM) evolved over the last two decades from managing data entered on paper Case Report Forms (CRFs) to managing data transcribed into Electronic Data Capture (EDC) systems. The Society for Clinical Data Management (SCDM) strongly contributed to this first significant CDM evolution through its Good Clinical Data Management Practice 1 (GCDMP©) Chapters first published in 2000 and its certification program for Clinical Data Managers2 subsequently released in 2004.

whitePaper | December 29, 2022

Every day we are inundated with tables, graphs, charts, and maps explaining everything, including the unemployment rate, COVID-19 vaccination rates, baseball home run launch velocities, and our investment portfolios. When made well, data visualizations can help readers and users find insights and make discoveries. When made poorly, they obfuscate, mislead, or make it difficult for people to use them effectively.

whitePaper | September 8, 2022

Enterprises today have to contend with exponentially increasing volumes of batch and streaming data, comprising a variety of structured, unstructured, and semi structured data types, and originating from an expanding number of disparate sources located on-premises, in the cloud and at the edge.

whitePaper | August 1, 2022

The Graph database is fundamental to driving a much-needed breakthrough in the world of Master Data Management (MDM). Until now, traditional MDM systems have imposed strict rules and structures on how these projects are managed. This has cost enterprises dearly in terms of wasted time and resources and has been a major contributory factor to the failure of many MDM initiatives. Graph has changed all of that, allowing modern MDM platforms to completely redefine how master data is prepared for insight and used to deliver tangible business outcomes.

whitePaper | June 27, 2023

Data mesh is an innovative approach to data management that offers a solution to the most common challenges organizations face when dealing with large-scale data management. Data mesh concept consists of four key principles for large-scale data management in a multi-tiered, multi-domain organizational structure: domainoriented ownership, data as product, federated computational governance and a self-service data platform. These all promote a more agile, efficient and scalable data architecture. However, one of the central challenges to implementing data mesh is designing decentralized governance mechanisms that align with its core principles.

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.