This article was originally published in an internal Oracle EMEA newsletter but since it does not discloses any sensible information, I thought I could re-publish it to the general audience of this blog. Enjoy.
When in 2006 Malcolm Gladwell introduced the concept of “Rapid Cognition” through his book called “Blink”, people started to take this “glimpse” of thought or first impressions more seriously. This “flurry of thought and images and preconceptions” as Gladwell calls it, make up an unconscious process that happens in the blink of an eye. It’s about the very quick decisions we make, based on lots of different information, sometimes completely unrelated and in an instant: bang! We make a decision, an assumption, a judgement.
Gladwell then adds that “we are innately suspicious of this kind of rapid cognition. We live in a world that assumes that the quality of a decision is directly related to the time and effort that went into making it”. This same world that tends to value the outcome of slow thinking is the same world that is in desperate need of the exact opposite in the information management arena.
The mere classical definition of a Decision Support System (DSS) outlines a slow process, in business terms. The problem with this approach is that the speed at which businesses need to take decisions has grown in a proportional manner when compared with the amount of information needed to be taken into account in the decision making process. Not just the amount but the variety of information. This is also true because businesses have changed, and the challenges of manufacturing are different from those faced by the real survivors of the dotcom era. In a wine producing industry, the impact of new products have to be assessed in a completely different way, as shall we say, the digital businesses. But at the same time classical businesses like pharmaceutical companies, still need to crunch lots of data, in order to assess the potential correlations between meds. So everything points out to a new world of fast decisions, based on a disproportional amount of information, when compared with the speed at with these decisions need to be taken, assessed, corrected and assessed again. A world where businesses mimic the Rapid Cognition in order to be there: at the finger tips of the customer. This is the world that created the Big Data concept.
Big Data means loads of unrelated, unstructured (not necessarily media), non-transactional data that needs to be crunched and transformed into information. From this information it should be possible to withdraw behaviour or value, and visualise patterns.
Why is this not the “normal” DSS chain we all know? First because the source is not transactional data and second because structured data needs a data model, whereas here it’s the data interpretation that sets the model.
The computational challenge is the same though:
- Intensive Load
- Fast Transformation
- Near real time analysis
But with Big Data there’s a new one though: visualise. For each phase you have a new or reborn challenge. Challenges like sessionise (new challenge and verb); data mining (reborn) or visualisation techniques (new and some reborn, like statistical languages) are coming into one single bag called Big Data. Examples of Big Data might be near real time processing of sensor information (Oracle-BMW sailing boat has 250 sensors that take into consideration more than 40,000 variables per second!); log processing systems (network tools, website analysis); image analysis; scientific research that cross various fields of knowledge; the list goes on and on reaching out to all the areas where information that needs “crunching” is popping up like pop-corn!
Once I had a chat with a network admin about the tracing capabilities of network management tools and he said: “I could pop off the lid and start tracing my network to troubleshoot problems, but I can only do it for a small period. Why? Because I don’t have storage space and these sniffing/tracing tools dump “A LOT” of data out”. And then he added: “Even if I had the ability to keep all this data, say, a whole day’s worth of tracing the network, how on earth would I even get the intelligence and processing power to make some sense out of it? Or even to take conclusions on what’s happening, let alone visualise it!”
Should I say to my network admin fellow friend that he could do it if he had a Big Data System in place? Well, I guess he would like to have such system, but in the end you all know what network admins say after tracing and analysing: “Nope! Nothing’s wrong down here at the network level. You should probably talk with the DBAs or app’s guys”.