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.
