Data & Knowledge

Big Data and Analytics are extremely popular terms these days. There is even a new breed of researcher: the Data Scientist. In a world where both data storage and bandwidth is cheap, this makes perfect sense. We are in a situation today in which we have access to tons and tons of raw data and the question is: What to do with it? If one thinks about it, the situation is similar to that of oil in the 20th century. We had this fantastic raw material and it was only our creativity that limited the application of it.

Data is very much like oil. It forms the basic building block for something completely different and much more valuable. The fact that we can look at historic data and predict how a person will behave in a certain situation is really, really interesting. Or the notion that we can accurately (even though it does not always seem that way) predict if it is going to rain tomorrow is to me spectacular – especially since I am a golfer. Those are just a couple of examples, but they rely on the same principle, by systematically looking at data, one will find (hidden) patterns that will tell us something about what has been, what is and what will be. Analytics is the modern form of a magic mirror.

As with petroleum products, in Data Science we need good raw material and various methods – or tools – to store it, clean it, and transform it into something more useful. This is where Analytics and the Data Scientist come into play. Analytics is precisely that; the method of discovering meaningful patterns in data. Unfortunately Analytics is not a black box where you can pour in data to produce knowledge – at least not in the general case. What you need is someone to wield the power of Analytics, someone that is skilled in the underlying analytics methods and has the domain knowledge of the data itself. Take weather prediction for example. First one needs to identify a pattern then one needs to understand if that pattern is actually something real and interesting. In order to make that judgment, one obviously needs to have some understanding of how the weather system works.

To a large extent, Ericsson’s products form the backbone of this data rich world. They offer the means to shuffle the bits of data around to anywhere someone needs them to go. Thus, it should come as no surprise that we think data is exciting. The huge amount of data that flows through the networks offers a challenge that any research has to tackle: What to do with all this data?? In a series of blog posts we want to explore some of the things that we find interesting with you. We want to address privacy, look at the boundaries of online analytics, how much data can one store, and so on.

Oh, I almost forgot! I'm supposed to talk about Big Data. Well, for now I think it’s sufficient to say that Big is just a matter of scaling things up…

-- Martin Svensson, Ericsson Research