"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 faceplate to make it more feminine. Anna is working on pricing it right now," he continued, "And with the great work that you've done with analyzing and mapping the data from the field, I figured this would be an easy question for you to answer. So I told John we'll take the ball and run with it!"

How are you going to answer the question? You just have the data on the devices, not on the income level of the people pushing the buttons.

This chapter is about dramatically enhancing value by adding additional datasets to the stored IoT data. Valuable additions come from both internal sources, such as manufacturing or Customer Relationship Management (CRM) data, and external data sources, such as economic or demographic datasets. You will learn how to look for valuable datasets and combine them to enhance future analytics in the search for untapped business value.

This chapter covers the following topics:

• General strategies on extracting value from combining datasets
• Internal datasets:
  • Which ones and why?
• External datasets - geography:
  • Elevation
  • Weather
  • Map APIs
  • Transportation
• External datasets - demographic:
  • Census statistics
  • World factbook
• External datasets - economic:
  • Global economic data
  • Federal Reserve US time series
  • Using code and APIs to add data