Think BIG

In last week's lecture we discussed a very interesting topic that affects us all a lot in our daily lives and will continue to gain even more influence in our daily business lives. 

We are talking about big data. Big data describes the systematic use of the increasing amount of data gathered from all areas to define certain processes and predictable developments. The goal of big data is to provide relevant in-time information to the user.

Important aspects of big data are the data volume, the velocity of data being generated, the variety of the data, and finally the veracity of data insuring data quality.
The biggest issue concerning big data is the data protection of private information.

But what kind of data do we create every single day? To whom and why are these data of interest?

Our own interests can be derived very easily form what we do on the internet. If we for example read the news using our smartphones and are surveyed over a period of time, the data we create will soon show in which topics we are interested the most.

If we are on the road and check the news or look at advertisement which is tailored according to our interests or even make a purchase, our data reveals where we are at the moment we did this, what time it was, and if we make a purchase the data will also show our credit card history, which reveals what we have bought with it before.

If we google something, no matter if with or without an account, Google is able to analyse the things we search for and then adapt the advertisement according to our search. Spotify or Amazon are further examples of companies that use data we actively searched for to make new suggestions for us according to our past preferences. Therefore they do not only use our past data, but also data of other users. This way Amazon can for example suggest to buy the matching phone case for our new mobile phone we ordered, because other people normally ordered these products simultaneously.

Informations about our body are very popular data. There are many different apps that can help us measure different things. For example our blood pressure, or heartbeat, if we are allergic to some products, our weight, or how active we are during a normal day.
All these kind of informations can be for example of interest for an insurance company, because they can see the needs of their customers to adapt their complementary insurances.

There are a lot of movement data, with which it is possible to locate us all day long. Our smartphones for example leave an indirect trace when they connect form one mobile communications antenna to the next, or when we use a GPS function, and we are even traced over security cameras that are installed in a lot of places. These data can be especially useful for predicting traffic jams on the roads or other related information to the movement of people. (Einstein 2014)

All these informations are helpful for businesses to better direct their marketing efforts to our needs and interests. There are several big companies that collect all kind of data about us. Such as Facebook, Google, Twitter, and Youtube. The amount of data that is uploaded to these platforms every second is insane and seems very unreal to us. On the website “Internet in Real-Time” you can see the extend of data uploaded on the internet. For example, in the first 5 seconds on this website Facebook already has 2’315 new posts, Youtube has 10 hours of new video material, and Amazon sold 255 items. All together 128’600 GB have been created in only 5 seconds! By the time we reach 1 minute, the amount of data has already reached 1'354'440 GB. 

Although so many companies can take advantage of big data, there are also some issues arising.

The problem that seems rather obvious is that big data also needs a lot of space and capacity to analyse the data. The company generating big data needs to think of where they want to store the data, how they can be used, and how they should be analysed.

Another issue we experience with big data is that the data can be biased for some firms, as it may lead us to interpret the data so that we get the solution we want to, as in any other statistical measure. We may also make our decisions too fast and before finishing analysing all the data we collected. This can lead to a false conclusion. Too much data can therefore misguide us and make it more difficult for us to find the right solution to a problem.

(Network World 2015)