Experience Management

Can You Quantify a Cold?

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IMG_2373How do computers see the world?  Can they tell if we are happy?  Can they predict what we are going to have for dinner next Tuesday?  Can they recognize when we are sick?
Well, my iPhone can.  Sort of.
I was (finally) recovering from a nasty cold last week and I opened the Health app on my phone and noticed that the step-counter had fairly accurately captured my cold, from start to finish.  My normally active body had flatlined for over three weeks, except for one day where I thought I was getting better and went snowshoeing for 8 miles in the Mount Evans Wilderness.  Bad idea.
So why didn’t my phone send me a sympathy card?  Well, that’s the problem with data.  It’s just data.  It takes skillful, and contextual, interpretation to make sense of the data and to draw useful conclusions.  With the step data, a computer might notice a trend or anomaly, but it doesn’t understand that it was caused by working out more or being sick in bed.  That is the art of data science and knowledge discovery.  A graph is just a graph until someone interprets it.
At Perficient, we are exploring the relationship between enterprise search and knowledge discovery.  The search engines that we deploy can process and index terabytes of data, so it seems like a good idea to use the information found in those documents to cure diseases faster, manufacture more widgets, or answer customer inquiries more accurately.  But turning raw data into solutions like those is tricky.  Tools such as IBM Watson Explorer can slice and dice the data found in documents and databases, but it still takes a human to know which slices to make and which dices to explore. Solutions such as monitoring the velocity of inventory or spotting a disease outbreak in a hospital are possible, absolutely, but they require skillful programming to inform the software what to look for, what to do, and how to react.
This issue is the real predicament of knowledge discovery.  We interact with technology as if you do just throw terabytes of data into some software and wait for the answer to life, the universe and everything (hint: it’s somewhere between 40 and 50).  In reality, there is much more nuance and sophistication to creating these solutions.  Data scientists and analysts will not suddenly be out of jobs when the next new piece of software hits the marketplace.  They will be more important than ever — with ever more powerful tools, they can mine deeper into datasets, faster and more nimbly.  And that is exactly what it will take to figure out if I’m sick or not.

About the Author

Chad is a Principal of Search and Knowledge Discovery at Perficient. He was previously the Director of Perficient's national Google for Work practice.

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