Real-time analytics is one of those areas of analytics that is both intriguing and wildly confusing. It's fascinating to see how your traffic flows, especially during peaks, and yet, how can we use it for anything?
In this article we will explore this very topic. We will talk about how to use real-time analytics to learn the details that you otherwise might miss, and how real-time use of data is much more than just looking at a dashboard.
I will start off with something Neil DeGrasse Tyson wrote back in 2004, in an article called "The Information Trap".
Most people assume that the more information you have about something, the better you understand it.
Up to a point, that's usually true. When you look at this page from across the room, you can see it's in a book, but you probably can't make out the words. Get close enough, and you'll be able to read the chapter. If you put your nose right up against the page, though, your understanding of the chapter's contents does not improve. You may see more detail, but you'll sacrifice crucial information-whole words, entire sentences, complete paragraphs. The old story about the blind men and the elephant makes the same point: if you stand a few inches away and fixate on the hard, pointed projections, or the long rubbery hose, or the thick, wrinkled posts, or the dangling rope with a tassel on the end that you quickly learn not to pull, you won't be able to tell much about the animal as a whole.
One of the challenges of scientific inquiry is knowing when to step back-and how far back to step-and when to move in close. In some contexts, approximation brings clarity; in others it leads to oversimplification. A raft of complications sometimes points to true complexity and sometimes just clutters up the picture. If you want to know the overall properties of an ensemble of molecules under various states of pressure and temperature, for instance, it's irrelevant and sometimes downright misleading to pay attention to what individual molecules are doing. In biochemistry, by contrast, you understand next to nothing unless you pay attention to how one molecule interacts with another.
So, when does a measurement, an observation, or simply a map have the right amount of detail?
Turns out, fractals are ideal for describing "self-similar" patterns, which look much the same at different scales. Broccoli, ferns, and snowflakes are good examples from the natural world, but only certain computer-generated, indefinitely repeating structures can produce the ideal fractal, in which the shape of the macro object is made up of smaller versions of the same shape or pattern, which are in turn formed from even more miniature versions of the very same thing, and so on indefinitely.
As you descend into a pure fractal, however, even though its components multiply, no new information comes your way-because the pattern continues to look the same. By contrast, if you look deeper and deeper into the human body, you eventually encounter a cell, an enormously complex structure endowed with different attributes and operating under different rules than the ones that hold sway at the macro levels of the body. Crossing the boundary into the cell reveals a new universe of information.
Note: You can also find this article in his excellent book "Death by Black Hole: And Other Cosmic Quandaries".
This is an excellent way to explain the importance of scientific inquiry, but also how to look at real-time analytics. There are times when it makes sense to look at your analytics in a highly detailed, very short-term, and extremely specific way, and there are times when it doesn't.
Real-time analytics is not the 'next big thing'. It's simply yet another tool that is part of a growing set of capabilities when it comes to understanding and connecting with our readers.
It's useful for something, but it's not useful for everything.
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