Analytics: Open Source nimmt an Bedeutung zu

  • Beitrags-Kategorie:Analytics / CASE

Eure Rechtsabteilung muss jetzt ganz stark sein: Open Source Software nimmt an Bedeutung für Analytics-Projekte im Umfeld CASE zu.

The connected car is here, and with it come massive amounts of data. Automakers, shared service providers, rental car companies, car dealers, and other players in the transportation industry all have a vested interest in using this data to transform both the car ownership and car usage experience.

Connected vehicles, for example, now allow original equipment manufacturers (OEMs) to provide predictive maintenance services as large volumes of data can now be collected and reliably assess the telltale signs of parts going bad, keeping the driver informed and out of harm’s way. Shared mobility auto companies, such as Lyft and Uber, need to know about a car’s location, fuel level, occupants, direction, estimated time to complete a trip, how often the car is used and in what traffic and driving scenarios, and so on. And while autonomous vehicles are still in their infancy, the basis for their learning is to digest millions of miles of test data (e.g., video, radar, lidar, and sensor), collected under a wide range of scenarios and conditions. This data is leveraged by machine learning algorithms to “teach” cars how to respond correctly. The common denominator in all of these scenarios is data.

So where does all this data go, and how can it be used to create apps and perform analytics that further the enhancement of the automotive experience? This is where open source technology comes in.

Open source projects in the big data space move their development and feature sets along quickly to harness the latest enhancements in technology, performance, and scalability. New best practices get baked into data platform solutions very quickly, and a huge community of data scientists, scripters, and programmers all works toward the same goal, making best-of-breed technology available to anyone. At the foundational level, innovation occurs so rapidly that it is unrealistic to expect a vendor to encapsulate all these new developments in anything but a proprietary solution layered on top.

Selecting an open source platform for data projects removes any risk of vendor lock-in. When it comes to the data space, like most things, putting all your eggs in one basket is inadvisable. Much of the innovation that is occurring in the open source data space is directly attributable to the best and brightest minds’ aversion to being tied down to a single vendor, making a shared effort much more attractive. Developers’ skills are not locked into one vendor, and employees are more valuable to a wider range of companies due to a broader set of applicable skills.

Automakers and others in the transportation industry choose open source platforms and technologies because they see innovation in the open source community. For example, Apache NiFi and Hortonworks DataFlow (HDF) came out of the National Security Agency (NSA). Those technologies were donated to the open source community, where they have evolved very quickly.

A similar trajectory has developed in terms of storing and processing data. Hadoop, an open source project, is seen as the de facto standard in this space. Whether a company chooses to use pure open source Hadoop as a data lake—or a solution such as a platform to store data, perform analytics, and build applications—it is clear that open source is at the foundation of big data technology.

Ultimately, the objective for all of the data generated by a connected vehicle is to provide value-added services, whether the consumer is a car owner or a business that owns cars as part of a shared pool. Automakers and OEMs can now see how their vehicles are being used. Open source technology has made it much easier to generate the necessary data, to build data lakes to store and process it cost-effectively, and, at the end of the day, to deliver greater value to consumers, users, and OEMs.

The bottom line is that cost-effective, scalable platforms to store and process the data a connected car generates are becoming critical. Smart companies are looking to open source technology and projects to provide those solutions.

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