Chapter 7. Decorating Your Data - Adding External Datasets to Innovate

"What if rich women like their houses colder than poor women?"

Oh no, you think. Your boss just asked an odd question and this usually means one thing. He has been assigned a SEWOTI, which means you have been assigned a SEWOTI (Stupid Executive Waste Of Time Idea). How would you even answer a question like that? Would that not require some extensive marketing surveys?

"Interesting question, how did it come up?" you ask.

"John, the Senior Vice President of Sales, was talking about how cold his wife likes to keep the house in the summer," he answered, "So we got to thinking that maybe wealthy women just like it colder. And if that was the case, maybe we should market the new top of the line thermostat to women instead of men."

You nod your head like it is a great idea. But you do not think it is a great idea, certainly not without the marketing data that backs it up.

"We were even talking about changing the color scheme on the...

Adding internal datasets

IoT data by itself is only part of the story. There is a multitude of useful data already available to you that could be a store of hidden value. Internal datasets can be overlooked as a way to quickly enhance IoT data. They are an excellent place to start. IoT data should not be viewed in isolation; think of it as a continuation of data already stored about your company's products, customers, and processes.

Data should be combined into a 360-degree view of your business to maximize the opportunity of finding new value in it. The fastest and easiest place to start is usually (but not always) the internal datasets that you already have available.

The reason it may not always be the fastest and easiest comes from internal data security and legacy system hurdles; which can sometimes make it difficult to extract internal data to combine with IoT data. This may be a stumbling block for internal datasets, which, perhaps counter-intuitively, is not the case with external...

Adding external datasets

There is a wide variety of useful external datasets available to download and integrate into your IoT analytics, often for free. There are far too many of these to do an exhaustive tour, but we will touch on some useful examples. Using these datasets is a great low-cost way to enhance the value of your IoT data.

External datasets - geography

You have IoT devices distributed across many locations. Location is now an attribute that can allow you to find patterns in the data, which you can then monetize. The environment, the demographics in the area, the local economic conditions, and the weather can all be tied to the geographic location of the device. Use geographic datasets to enhance the potential value of your IoT data.

Elevation

Elevation can be an important attribute in many situations as it is tied to the operating conditions of the device and the surrounding environment. Even if your device does not move, it can still be valuable to know its elevation. The maker...

Summary

In this chapter, we discussed how to enhance your IoT data with both internal and external datasets. This can greatly enhance the opportunity to find value. By adding this additional data, you can develop attributes to develop and test theories about your data.

All theories should be tested with data. IoT data allows you to have a giant lab where you can run virtual experiments by segmenting and comparing the data you already have. By adding in external datasets, you can magnify the ability to find valuable patterns with IoT analytics.