Distressed Data Warehouse
For the past two years, a national leader in agricultural research and development has been rapidly increasing the number of research studies along with improving their predictive analytics capabilities. Over the past two years, the volume of data flowing into the data warehouse has grown dramatically due to increases in the number of research studies sending data to the warehouse. There are also significant increases in the volume of marker data because of advancements in genetic sequencing. This combination has led to instability with ETL processing as well as inability to complete warehouse update processing to meet business service level timeframes. This case study investigates the root of the problem, how we at Lifescale Analytics approached the issue and the results of remedying their distressed data warehouse.
Collaboration in the Cloud
The bioinformatics team at a global donor research organization was tasked with the mission to find new and better ways to identity donors for leukemia patients. They needed to discover a way to collaborate with external researchers using a broad array of analytics tools while complying with platform and tool standards supported by corporate IT. This case study investigates the root of the problem, how we at Lifescale Analytics approached the issue and the results of employing a private cloud solution for this organization.