The bicycle was invented in 1817 by Baronvon Drais. He envisioned it as a walking machine to help him get around the royal gardens faster. It featured two same-size in-line wheels,mounted to a frame which he straddled. He propelled the device by pushinghis feet against the ground, thus rolling himself and the device forward in a sort of gliding walk. The bike was made of wood, which made it very heavy. There were no pedals, so forget about going uphill. And without brakes, going downhill was probably challenging. His invention enjoyed a short lived popularity as a fad, not being practical for transportation in any place other than a well maintained pathway such as in a park or garden.
When I read about this first attempt at building bike, I thought about traditional BI tools. The way traditional BI tools enable data analysis is similar to Baron von Drais’s bike; the data can only be analyzed with predefined drill down paths or by running queries one at a time, so business users are limited to a “well maintained pathway” to do analysis. BI was introduced 25 years ago at a time when storage, memory, and computing resources were scarce. In many cases, the same query -or cube- based technologies are still being used today. In these cases, business users do not have freedom to analyze the data in their way.
Image source: Wikipedia. Link here: http://en.wikipedia.org/wiki/File:Draisine_or_Laufmaschine,_around_1820._Archetype_of_the_Bicycle._Pic_01.jpg
When I thought about today’s modern bicycles and their ease of use to go anywhere, I realized that QlikView is the bicycle of business intelligence! With QlikView’s associative experience, business users can enjoy their own journey through the data. They can literally see the relationships in the data, because with every data point selected in their analysis, an entire network of data, and relationships between them, is implied. An associative search returns data that represent things that are related as well as not related, via various forms of associations that exists in the data. An associative search looks through a network of associations for the things that are connected to users’ selections, and guide them quickly home in to the unknown unknowns that are hidden in the data. With cube or query based technologies on the other hand, each additional search term only provides a linear benefit, there is no exponential amplification using networks that exist in the data.
Two centuries later after the first bike attempt, children all around the world learn to ride a bike and have fun. So my question is: shouldn’t business intelligence be easy and be for everyone just like riding a bicycle? I think so!
* Source: About.com. Link here: http://inventors.about.com/library/inventors/blbicycle.htm
