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Cleaning data is often the primary job of the data scientist, and not necessarily the one they signed up for.  While getting the "words" right with computers is important, it is not fraught with the nuance that getting words right with humans is. Across disciplines the same word can have entirely different meanings and this miscommunication is only one aspect of team building that can go awry.

At some point in your life, you’ve found yourself describing a project you’ve worked on to a friend. They interject, “I’ve done something similar to this before,” and go on to describe a field or skill you didn’t know they were familiar with. You’ve just uncovered some dark assets about your friend: a set of skills or knowledge that were only discovered due to an accidental trigger.

When the United Nations released a report earlier this year that a catastrophic two-degree Celsius (3.6-degree Fahrenheit) rise in global average temperatures is expected to occur in the next decade, there was a media firestorm about the dire predictions. You know who wasn’t surprised? Climate scientists. (Read about the difference a half-a-degree can make.)

If your job is to get your company, team, or community to innovate, you know how organizational forces can make it hard to even try something new. Visualizing the resources available is an effective first step in overcoming some of those organizational forces. Simply being able to see, and show, what you have allows you to make a compelling case for marshaling resources and even spark some initial interactions in that direction.

We recently had to prototype a data application over a supercomputer tuned for graph analysis. We built a proof-of-concept leveraging multiple APIs, Cray’s Urika-GX and Graph Engine (CGE), and a handful of programming languages in less than a week.