In May, I had an opportunity to join a small discussion group on the Internet of Things (IoT) and Wearables during lunch at the MIT CIO Symposium. It’s a hot topic these days given that the number of connected devices could reach 30 billion by 2020. The discussion bounced around, from the benefits of wearables, to industry use cases and barriers to adoption, and even to the speculative rise of wearables’ crazy cousin, “embeddables”. As a quick aside, does anyone else envision a day when you can buy an embeddable device at your local Best Buy and then bring it over to the Piercing Pagoda kiosk for a quick implant? No one else? Okay… moving on.
Are “Connected” Devices a Misnomer?
What fascinated me most about this discussion is that we kept circling back to a central problem not just with wearables, but with the IoT movement at large. These connected devices and sensors aren’t always all that well connected to upstream systems or downstream applications. Even more widely adopted IoT technologies like wearable devices and health apps require a lot of personal time and attention to make them valuable, and the data is rarely connected to a broader network of electronic health records, insurance providers, or other applications. Creating such an ecosystem of connected solutions and technologies will be critical for any industry attempting to take this vast amount of new sensor and machine data and turn it into real, tangible value for customers.
The Need for a Platform and Ecosystem
As my colleague Chris Selland highlighted in a recent piece on the value of platforms, high-quality ecosystems that promote interoperable products and services create value for everyone, most important of which are the customers that use them. This is precisely why Chris and the rest of the team are working hard to ensure the HPE Vertica Analytics Platform interacts with the broadest possible ecosystem of BI, visualization, predictive analytics & machine learning, data prep, ETL, cloud, open source, security and other solutions.
Imagine if all IoT-driven businesses and use cases had this sort of ecosystem in place? How much value would that create for customers? A lot, I presume.
The Rise and Promise of the Smart Building
Take a market that’s near and dear to yours truly: energy efficiency and smart building systems. A smarter, more connected energy ecosystem can unearth tremendous value, such as:
More competitive manufacturing
Lower utility expenses
Reduced greenhouse gas emissions
But despite the sea of companies, products and services addressing this market, the level of fragmentation and the lack of interoperability that exists across the value chain is hindering the adoption of these technologies and the realization of this value.
To use a single smart building as an example, it could have an electric meter, a natural gas meter, sub-meters, a building management system, smart lighting controls, and maybe even plug load sensors. The likelihood that of all of these IoT devices and sensors are talking to each other is low, and the likelihood that they also connect to an ecosystem of business applications like accounts payable software, work order management systems, and carbon reporting tools is even lower. And that’s just for one building. Imagine if you broaden this example to include hundreds of buildings with variably aged infrastructure, different utility suppliers, meter providers, building management vendors, and lighting controls. That not-so-uncommon situation gets hairy pretty quickly.
Not all is lost, however. There is a tremendous amount of human and business capital working to create a more interoperable ecosystem of devices, data, and products in the smart building space. Industry behemoths like Schneider Electric as well as venture-backed startups like Lucid are creating platforms to connect the vast amount of energy and building infrastructure that has historically operated in siloes.
Powering Predictive Analytics in Smart Buildings
As these platforms take hold and the volume of smart building data continues to evolve, there’s a greater need to operationalize large-scale machine learning and statistical analysis to gain deeper insights into how buildings operate, where equipment is likely to fail, or when new electricity demand will spike. This is where an advanced analytics platform like HPE Vertica can play a role in the Smart Building ecosystem. With a range of built-in analytic functions and tight integration with R, Python, and Apache Spark, HPE Vertica enables petabyte-scale workloads and accelerates large-scale machine learning, statistical analysis, and graph processing to power the most challenging predictive analytics initiatives.
Given the amount of value that can be unlocked from this emerging ecosystem, coupled with the power of advanced big data analytics, I for one am optimistic on the future of connected smart buildings.