Make Data Big, Ideas Small

Innovation requires effort.

Innovation is usually messy.

Innovation requires things to change.

But what do you do if your innovative idea is too big, too ambitious and too resource consuming to be acted upon right now? Do you wait for the technology to be invented (like James Cameron’s Avatar) or do you create it yourself (Lucasfilm’s Skywalker Ranch)?

Or do we go a third route, one which brought Charles Dickens great success?

Diagramming small ideas using big data

When Dickens first started, he didn’t write his novels all at once. He released them chapter by chapter, which allowed him to both get his ideas out faster AND to make minute adjustments later in his stories.

He succeeded by making his big idea small. In an age when attention spans seemingly shorten daily, this process of presenting a big idea in small chunks fits perfectly into the consumption habits of most potential clients. Instead of having to schedule a weekend to get through a report, and possibly keeling over from having to read yet another summary of a memo about a report, these chunks allow your audience to better absorb specific lessons that could otherwise get lost in a larger report.

But doing this isn’t all that simple. While great writers such as Dickens can more easily than most break his grand plans into digestible parts, that doesn’t mean we have the same ability. But we can learn to do this by taking all those little data points from our idea and making them big. How big?

Big enough to see trends, big enough to make connections, and big enough to be able to tell a story that matters. New York first did this with CompStat back in the 80s and 90s. The program, the first of its kind on such a scale, took a big idea (reducing crime), broke it into little data points (they tracked where and what kind of crime occurred when) and then laid it out so they could see patterns.

It Worked
Their big idea, at first too daunting to be accomplished, became small enough to handle. Those data points, seemingly too small to make a difference, added up to one heckuva result.

What can we learn from this? No idea is too big to be broken down and no data point is too small to make a difference.

Happy researching!