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.
So here’s some irony for you. We’re getting ready to make a major announcement next week about some new products that will help our customers manage massive unstructured data growth. We were at a studio last week filming our keynote, and it dawned on me that it was a good example of exactly the type of problem we’re helping customers solve. By the way, if you want to watch the keynote this Wednesday, October 12 at 8:30am PT, click here.