There’s lots of hype around Big Data but every now and then something finds its way through the buzz and confusion. In today’s TechCrunch, there was an excellent read by Dr. Michael Wu, Principal Scientist at Lithium, an organization that describes its role as “helping companies unlock the passion of their customers”. Wu explains very well why data is not the same as information and information is not the same as insight.
While this may not seem so difficult to grasp, it gets lost in the shuffle.
In The Big Data Fallacy And Why We Need To Collect Even Bigger Data, Wu shatters the myth that data and information are interchangeable terms, a common mistake made by those starting the Big Data walk or those being fed marketing material by startups and big software vendors.
Signal to noise, relevant to irrelevant
The model is about cutting through the layers that hide what’s in our data. Determining what’s interpretable and what’s not, what’s duplicated and what’s not, and what’s relevant and irrelevant. The reality looks more like the graphic below, found in the article:
The graph looks different for every problem being solved, but this is a great illustration of just how enormous amounts of data can produce only a small amount of insight.
It’s an inconvenient truth that Big Data is fed by enormous data that requires powerful technology to connect, filter, analyze, anticipate and act. It only starts with data but its where it goes from there that becomes really valuable. Getting to this understanding…grasping this challenge…is key to soberly taking a system approach for how to work with Big Data.
Looking at Hadoop as the key to Big Data makes little sense for the vast majority of companies that don’t need to make the investment that Google, Facebook and others have made. The ecosystem of Big Data is the key to not having an elephant riding a bicycle.
Picture credit: The Big Data Fallacy And Why We Need To Collect Even Bigger Data