Everything is Aggregating Data. That’s Both Good, and Bad

Soon, everything you use will be cloud connected. And no one is watching.


In the future, all consumer goods in everyday use will gather performance data, aggregated and transmitted through the cloud back to the products’ manufacturers. For the engineers that design those products, the result is a data set to determine the upper and lower limits of performance for critical components, allowing an iterative design process that lets each generation of component and assembly become optimized for performance. The result is lower cost, and less environmental impact for each component. That’s good, but that data can also be aggregated by marketers to develop a very accurate profile of individuals in a household, to drive advertising. And it’s surrendered to those marketers without compensation. Uploading motor performance data in a washing machine to a design engineer is a good trade for consumers, because they get better machines in return. But surrendering data to an advertising agency without compensation, is bad business.  

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Episode Transcript:

If you’re an engineer working in the consumer goods sector, imagine this scenario: you are designing an electric motor for a washing machine. The pressures to reduce costs are enormous in this highly competitive sector, and you need to design a motor at the absolute minimum of materials, labor and weight. How much can you take out of the previous generation design and still maintain reasonable durability? It’s a tough question, and there are several ways to find the answer. It’s possible to simulate the design extensively, and that’s the technique that’s in use every day around the world. It’s also possible to build prototypes, then test them to destruction to if there tough enough. To achieve the target durability without over engineering the motor. 

But imagine if that engineer could access motor performance data from hundreds of thousands of existing washing machines around the world, then analyse that data statistically for critical parameters like heat rise, current draw, and vibration. With that kind of information, it will be possible to which motor designs were deficient, but also which were overengineered for the application. This would set immediate upper and lower bounds for the motor designer, and reduce the number of iterations needed for perfection. Sounds great, doesn’t it? 

Well, this is the future of all consumer goods, from athletic shoes to pickup trucks, and the combination of smart products with cloud connectivity makes the data not only easy to aggregate, but extremely cheap as well. That’s great for engineers, but what else could be done with all that information? If you’re Google, or Amazon, aggregate enough of that information from enough of the products that we use every day, and it will be possible to build a very clear picture of who you are. How? 

By measuring how far you walk, and where. By geo-locating where you shop, and how far you drive to work, and how fast. By seeing how many times you use your coffee maker, and how many cups you create. By determining how electric razors are in use in the house, as well as dozens of other potential information sources that would allow smart systems to develop a very accurate profile of every individual in a household. That kind of information is in a strict sense, more valuable than the machine performance data that the engineer needs, and we already see a form of it in the directed advertising that we all see only use the Internet. 

But given that this information is highly valuable, why are we, the creators of that data, not compensated for it? I’m prepared to surrender performance data on my car’s transmission, or the heating element in my coffee maker, or the motor on my washing machine, if those things mean that the products that I bought I will cost less, and last longer. But giving a mass-market retailer information to build a profile on me so they can target me for advertising is a different matter. There has to be an economic transaction here. Samsung, or Ford or Nike can have a little bit of information if they use it to give me better products. But the Google’s Amazons of the world, hey, throw me a nickel every once in a while. 

The quid pro quo in the early days of broadcasting was that you endured commercials, and exchange for free entertainment. But in this world, you pay for the goods, and pay again in the form of critical data that you surrender without compensation. Engineers can make the world better with good data. Madison Avenue, not so much.

Written by

James Anderton

Jim Anderton is the Director of Content for ENGINEERING.com. Mr. Anderton was formerly editor of Canadian Metalworking Magazine and has contributed to a wide range of print and on-line publications, including Design Engineering, Canadian Plastics, Service Station and Garage Management, Autovision, and the National Post. He also brings prior industry experience in quality and part design for a Tier One automotive supplier.