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Google Cloud Next takes place this week virtually. While there are typically many announcements for their annual event, one of the less highlighted announcements this time is for Google’s Intelligent Product Essentials.
With Intelligent Product Essentials, Google provides essentially all the key components for implementing distributed IoT and edge computing solutions. Components are included for data acquisition, connectivity options for both data collection and IoT device OS / security updates, integration of acquired data in a database suitable for the generated data type (eg Spatial data, text data, etc.), Central and / or edge cloud application processing, AI / ML analysis that enables changes / additions to the processes via open APIs and access to work across a multi-cloud infrastructure (few companies work with a single cloud infrastructure).
It is less a product and more a template or a work table that connects a number of connection and integration components to give companies a head start in a complete IoT and edge-implemented solution. Google offers some direct engagement with its customers, but relies on partnering with specialized system integrators (a list will be expanded over time) to complete a customer’s solution. The SI will typically focus on implementing IoT components and sensors to be managed, while the resulting data can be processed for insight into necessary actions and / or user and device interactions.
Google’s announcement of the offer focuses on consumer issues related to consumer products – such as smart ovens and smart bikes. But I expect more companies and organizations to be interested in how Intelligent Product Essentials can help manufacturers create modern environments for machine monitoring, maintenance and error analysis, delivering up-to-date and secure “things”, and how it can help organizations implement complex IoT solutions for intelligent urban infrastructure, health monitoring, remote inspections, etc. It is clear that the need for these IoT-enabled and edge-driven solutions continues to grow.
Google does not charge this product per se; rather, it generates revenue for the components that customers choose in the GCP products that are the basis of this solution set. Built on a Kubernetes microservice architecture, it uses DataFlow to move data into the cloud environment and various potential databases of the customer’s choice (eg Firebase), which creates a data warehouse that can be analyzed by Google AI / ML tools (eg Vertex AI or Vertex on the edge), and finally manage the various IoT components remotely with Google’s management tools. Interestingly, Google does not require IoT devices to run its Android operating system, as it realizes that many IoT devices run an RTOS or other simple operating system.
Such a basic platform is an attractive way to create and implement industrial devices that often suffer from poor user experience and manageability – from trains to excavators to industrial machines to medical screens, etc. Many organizations can benefit from such capacity, but many also lack resources (both monetary and qualified employees) to implement a modern data-driven environment to improve their operations. Any reference design that brings together the main components of an integrated approach is extremely advantageous.
While this offer from Google may not be an option for companies that require a fully optimized and customized solution, and that can take many months or years to set up, it offers a simplified way to speed up implementation time for many companies, which means real revenue improvement and / or reduced reliance on scarce resources. Although there is a high degree of variation involved based on the specific requirements of a user organization, I estimate that a template-structured solution like this can often achieve a 25% -40% reduction in the efforts involved, resources needed and / or time-for implementation .
GCP as a cloud solution is competitive, and Google has some of the better analytics and AI capabilities available to provide real data insight. But as the number three public cloud provider for businesses, Google needs to try harder. Both AWS and Microsoft have their own IoT and edge computing initiatives and have made some significant progress, especially in key industries such as the automotive industry, smart cities and healthcare. But the market for edge- and IoT-related solutions is still growing, so Google coming up a bit later with a GCP offering is really not a big setback. And Google has a great opportunity to convince potential customers that its analytics and AI capabilities, which have been refined for years for its own product needs, are a huge advantage. But Google still plays catch-up considering the lead and better-known products from its competitors.
Bottom Line: The potential benefits of operational efficiency and security attributed to IoT, edge, and data-driven analysis of the company’s operational and business processes are very attractive to many organizations, but they may not have the right resources to pursue such initiatives. With Google’s Intelligent Product Essentials foundation, many more companies, even smaller companies and / or medium-sized companies, have a way to make IoT and edge a reality. Integrated solution templates like Google’s Intelligent Product Essentials are a great way to achieve advanced IoT and edge-enabled solutions with far less friction than fully customized solutions.
Jack Gold is the founder and chief analyst at J.Gold Associates, LLC., An information technology analyst firm based in Northborough, MA. Covers the many aspects of business and consumer computing and new technologies. Follow him on Twitter @jckgld or LinkedIn at https://www.linkedin.com/in/jckgld.
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