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How embracing complexity can lead to simple answers

How embracing complexity can lead to simple answers Becca Bleznak

When the book "Data Smog" by David Shenk was published in 1997, mining the internet for information was a daunting task. But just one year later, Google was created, and we were faced with endless possibilities in search. At the iMedia Commerce Summit in Nashville, Tennessee, Eric Berlow, complexity scientist and TED senior fellow, describes how today's technology has transformed all of us into data scientists, and how certain brands have used this to transform user experience.

Data scientists themselves are a hot commodity in 2016: It's ranked as the No. 1 job, there is a growth in the number of degrees in this line of work -- and a predicted 190,000 data scientist jobs will be available by 2018. But where a degree was once needed, says Berlow, this world is now open to all of us with one simple piece of technology -- our smartphones. 

Berlow shows in just seconds information that once took much longer to acquire: Your map application is more than just a map, it's a place to search for local restaurants, plan out bike or driving paths -- and one can query this massive data base in real time.

As an ecologist at Yosemite National Park, Berlow studied relationships in order to protect endangered species -- which animals help one another? Which depend upon others? Which eat one another? By putting this information into an interface, he gets a visual representation across various ecosystems, and finds patterns in the data. The interface adds simplicity, as opposed to a giant chart of information one must comb through and make sense of.

Berlow shows us another example of this, with a collection of music recordings from TED Talks that have been uploaded to a Spotify playlist. The songs are linked if their profiles are similar, which are based upon things like musical style, tempo, and mood. And you can sort them depending on whichever factor you choose.

Another issue the interface solves is one that current algorithms don't address: If you don't know what you're looking for, you can't find it -- for example, on YouTube. On channels like Red Bull's, you see two main categories: the most popular videos, and the most recent. It becomes a cycle of the same videos being seen over and over, and the rest are buried. If the interface is applied, you can sort by things like shared tags and similar descriptions. So if you're interested in alpine skiing videos, you can find all of those grouped together.

How does this apply to commerce? When sorting customer profiles, you can determine who is similar to whom based on what they've purchased. It shows a brand patterns they've never seen, and can allow you to determine who is your direct competitor (meaning a brand that sells the same thing to the same people) and who might make a good marketing partner (a brand that sells something different, but to the same people). It also allows you to see the flow of money from one brand to another -- which company the user purchased from in the past, and which one they most recently purchased from. 

This opens up a whole new world of marketing. An individual relationship is one thing, but when you see how they're all connected? You can leverage this information for decisions you didn't even know were possible, in the simplest way. 

Becca is currently an editor at iMedia Connection, as well as a freelance entertainment writer for ScreenPicks.com and The Televixen. In the past, she has worked as a social media/community manager at SEO Savvy, Empower Digital, and Mahalo. ...

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