Published on 11/17/2016 | Strategy
A lot of people think about data as the new gold, but a better analogy is data is the new oil. When oil comes out of the ground it is raw, it has intrinsic value but until that oil is refined into petrol and diesel, its true value is not gained. Data from sensors is very similar to oil. The data that comes from the sensor is raw, to gain insight from it, the data needs to be refined. Refining the data is at the heart of a successful Internet of Things project which leads to business growth and transformation.
There are many examples of how data can be used to gain value in different ways. Let’s use the humble washing machine as a use case. At first glance the washing machine does not look like an interesting use case, but when examined more closely it turns out to be a great way to illustrate how IoT works by showing us how value can be gained from the data in a washing machine; and how its data can be used for business purposes. The use cases for gaining value from washing machine data are not specific to the washing machine, they can be applied across multiple “things” in all sectors and industries.
Consider the washing machine when it is manufactured and goes out the factory door, the manufacturer loses sight of that washing machine. Once the machine goes through the distribution, reseller and sales chain, the manufacturer has lost sight and does not know who ends up owning or using the washing machine.
One of the first benefits of a connected washing machine (which is the definition of an Internet of Things thing) is where data can flow. With a connected machine, a manufacturer now has the ability to communicate with the owner or user of that washing machine. The connection between a manufacturer and the end user of a product is exceedingly valuable for the chief marketing officer of a company. CMOs want to interact with the owner or the user of the thing they produce. The value lies in the fact that the manufacturer has a direct touch point with the consumer – a way to talk to or connect with the owner or the user of the thing. The value of this touch point is used in many of the following ways.
The second benefit becomes evident when a washing machine breaks. Today if a washing machine breaks, typically a consumer will do one of two things. If it is not in warranty, the broken machine might be discarded and replaced with a new model. If it is in warranty, a consumer might call out the service engineer who will then come around to investigate the problem. While on site the service engineer makes a diagnosis, perhaps discovering the pump is at fault. The engineer goes out to the white van only to realize the right pump is not in the van. Returning to the owner they apologize and agree to return once the part is in stock. The engineer leaves, orders the pump, and two weeks later returns to repair the washing machine and gets it going again.
In this scenario, the result is satisfactory as the washing machine ends up working, but the owner or the user is not happy as their machine has been out of action for two weeks. No doubt the absence of a washing machine is a difficult situation for a homeowner; but, for the owner of a commercial washing machine in a hospital or launderette, it’s an even worse situation. And what about the service company? The field engineer had to schedule two trips in order to complete one job, has used two lots of labor, two lots of fuel, plus spent time addressing extra administrative tasks.
In the case of a connected washing machine all these wasted cycles could be avoided. The connected washing machine already has a lot of data in it – temperatures, pressures, cycles, lime scale build up, parts wear and tear, etc. That’s how the washing machine works. It uses the sensor data that is already in the washing machine to control the wash cycles. Until the washing machine is connected the data is siloed, and is only of use for controlling the machine. Once connected, the washing machine’s data can flow and be refined using analytic capability.
The data from the washing machine can be compared to a computer model of the washing machine. If the washing machine data correlates then life is good, the washing machine is working well. However, if trends or anomalies are detected which deviate from the model, predictions can be made about what might happen in the future. If a problem can be predicted, an action can be taken to mitigate the problem. Take the example given earlier. If the pump breaking is predicted preventative actions can be put taken. First, ensure the right pump is in stock and order it; once in stock, contact the owner or user of the machine to arrange a convenient maintenance slot to undertake the repair. The service engineer then visits the site with the right parts at a convenient time, resolves the problem before it occurs, thereby providing a better level of service which in turn goes a long way to improving customer satisfaction because the machine never breaks, and preventive maintenance takes place at a convenient time.
In the new predictive world both the user of the washing machine and the service company benefit. The user’s satisfaction increases as the washing machine is only out of action for a short, convenient maintenance window, rather than days or weeks in the pre-predictive world. The service company benefits as only a single visit is required to fix the problem rather than two visits. The use of a connected washing machine, and the application of the data flowing from that washing machine enables the optimization of multiple processes – call outs, field engineer time, van mileage, inventory management– creating savings in resource, time, money, and asset use. These savings result in more opportunities to grow the service company’s business– using the field engineer and expensive assets to do more jobs with using the same assets, for example, the person, the field engineer and his van.
In the scenario to predict problems, the class of data used – temperatures, pressure, and etcetera– is telemetry data. But there’s another interesting class of data that can be collected once a thing is connected, and that is usage data – how is somebody using something? For example, the average washing machine has ten or more wash cycles. Are all of these wash programs used? Through a simple polling of a number of people visiting the IBM IoT showcase lab around 99.5% of the visitors use one, two or three programs. These findings suggest many of the programs are not used or useful.
In the instance of a connected washing machine, not only is that machine sending telemetry data, but it can also send information about which programs are actually being used. Once that data is received and analyzed (and we don’t know what the result will be), hypothetically if the manufacturer was to find out that 99.9% of users use the same three wash programs, that knowledge is exceedingly valuable. Now when they build the next version of the washing machine, they can get rid of a number of redundant programs, dramatically reducing the design, engineering and test costs. For the consumer, the washing machine is made easier to use – with fewer choices, and a simplified set of settings.
Telemetry data is what most users think about when looking at IoT, but usage data is just as important. Both telemetry and usage data combined can make a difference to continuous engineering cycles when building the next version of a product. It’s interesting to note that it applies just as much to software as ides does hardware.
You can find the original article here.
