As companies accelerate their development efforts for self-driving cars and advanced driver assistance systems (ADAS), they are expanding the size of their vehicle test fleets. And with each vehicle generating 2 PB or more of data annually, these companies need a good data management plan to support this large and rapidly growing volume of data.
There’s no doubt that data plays a critical role in life sciences research today. I’m referring to digital data that is stored, transmitted, and analyzed on IT hardware. New research technologies are rapidly changing the discovery process, significantly increasing the volume of digital data and reducing cycle times. These advances in research are straining today’s IT infrastructure with changes in the lab happening so fast that the infrastructure must not only meet today’s needs, but also have flexibility for an uncertain tomorrow.
Self-driving cars must navigate dynamic environments under variable conditions—night and day, summer and winter. To accomplish this task, each car uses multiple devices designed to identify specific object types (i.e., pedestrians, other vehicles, road signs, painted roadway markings, etc.) at specific distances and conditions.
Last week the Active Archive Alliance announced the availability of a report titled “Active Archive and the State of the Industry”. The report is primarily an educational piece, explaining the data growth challenge IT organizations are facing today, and then defining archive characteristics, showing how active archives are implemented and illustrating the resulting benefits.