From Big Data to Big Profits: A Lesson from Google’s Nest
Google Nest is a very interesting example of how such a seemingly simple item as thermostat, with the addition to Big Data can transform an industry and lead to significant profits.
By now, most of us have seen or are even using programmable thermostats. One of the recent and more interesting offerings in this space is from Nest. Nest sells a thermostat that captures data about human presence to self learn what heating and cooling decisions are best. Programming is not needed;it learns what is best to maintain your comfort and turns on the system accordingly. In the process, it creates Big Data about the operation of HVAC systems and our human occupancy patterns. That Big Data and its possibilities attracted Google. Nest Labs was purchased by Google in 2014 for some $3.2 billion dollars, forcing many to wonder, could a thermostat (even a really cool one) be worth that much? Yes, and even much more.
Nest is a great example of how measurement and the creation of new digital data will allow Big Data strategies to control what is otherwise a mechanical realm. It is in many ways emblematic of the transformation at work in many industries using Big Data to reach new insights and new levels of control over human-based and mechanical systems. Consider that in each of our homes and workplaces, we rely on heating and cooling systems that are, at their core,advanced mechanical systems, activated by human intervention of some form (changing the temperature, turning the system off, etc). Data on our HVAC systems is rarely digital and if collected, used sparingly to make decisions about the operation, maintenance, or replacement of the system. Even with high-end HVAC systems, you are unlikely to get a digital feedback from the system. The systems are not networked, controlled, and hardly even measured!
The Nest program, as a thermostat “learns” about human patterns, cooling more when needed and heating more when needed. It also creates a digital record of its operations, decisions to activate the HVAC system, and the temperature of the house. At its core it is an optimizer of the air conditioner and heater in your house. Reducing cost is its primary goal and benefit to the homeowner. But the Big Data it creates is valuable to others. And Nest can do more and will do more as Google expands its capabilities and builds a bigger network of users. Not only does it create a digital record of operation, it can provide a means to digitally control the heating and cooling systems in a property from afar with other economic concerns in mind.
Imagine every (or nearly) home in a city connected via Nest. It would create a network that allows each heater and air conditioner to be controlled from afar. It can be used to make decisions about the overall power supply network. The decisions might even be used to meet environmental and local power production goals that could not have been reached with manually controlled thermostats.
In many parts of the US, energy consumption stresses the local power plants, especially in warm climates, when air conditioners are turned out. We frequently come home at the same time and demand AC usage at about the same time, resulting in a peak load that is not manageable. This peak loading is a peak problem and is solved by adding more generators at the power plant. However, that is very expensive (on the orders of hundred of millions to billions of dollars) and poses many environmental and political challenges. However, Nest, since it has developed a digital record of performance and can actuate the systems remotely, could be used to identify and execute optimal operations for the whole network of homes, smoothing out peak loads and reducing demands on the power utility. Even turning off a few thousand air conditioners for a few minutes could make the difference in smoothing out the peaks to the power utility and removing the need for rolling brown outs and blackouts. To the power utility, it would mean preventing or delaying the addition of new and costly generators. Do so, is worth big money to the power utility.
Developing proper economic incentives for insulation and HVAC system upgrades are other opportunities made possible by a broad network of Nest devices in many homes. Precise identification of usage, down to the component level is possible. This will create a Big Data set about home HVAC systems like never seen before. Homes and operating decisions can be scored against each other, looking for better practices.
Consider the opportunities on the commercial real estate side. Nest can be the portal to manage the heating and cooling systems in vast numbers of buildings with little to no physical intervention. That savings would be valuable to the property owners.
With the addition of smoke, fire, and carbon monoxide monitors and now cameras, Google is making Nest an in home monitoring system. What is brilliant about this is that Big Data is created that allows for identification and execution of new optimal decisions. Managing and controlling the mechanical realm is attainable with Big Data strategies. It will happen with autonomous vehicles, our homes, and delivery systems to name a few. It is also an example that Big Data strategies to generate big value do not need necessarily need to create new business models, but rather can focus on the data about current systems that need improvement.
Recognizing that Big Data and the network effects of its collection can allow us to answer new economic questions. In the case of HVAC systems, we might ask what is best for the whole network, economically. Such new optimality was not achievable in the past. Big Data will allow us (companies, governments, and others) to pose such questions. It leads to new monetization strategies for Big Data.
These important implications in monetizing Big Data in the digital economy and more are developed in my recent book, From Big Data to Big Profits: Success with Data and Analytics. The book examines the evolving nature of Big Data and how businesses can leverage it to create new monetization opportunities. Using case studies on Apple, Netflix, Google, LinkedIn, Zillow, Amazon, and other leading-edge users of Big Data, the book also explores how digital platforms, including mobile apps and social networks, are changing customer interactions and expectations, as well as the way Big Data is created and managed by companies. Companies looking to develop a Big Data strategy will find great value in the SIGMA framework, which assesses companies for Big Data readiness and provides direction on the steps necessary to get the most from Big Data.
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