Beim ersten #InspirationLunch von Weber Shandwick haben wir uns mit verschiedenen Unternehmen zum aktuellen Trendthema #BigData ausgetauscht. Hier gibt es zusammengefasst vier grundlegende Hypothesen zur Entmystifizierung von (Big) Data - why small data is beautiful (too)!

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Short review of 1st Inspiration Lunch
29th of January, 2014, Berlin
Hammer & nails Be a team, not a tool Cultural change
More (data-) quality than
quantity

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Data and its analyses are not an end unto themselves. So it
should be outlined very clearly which questions could be
answered and how high are the costs compared to the
benefits.
Many companies have a lot of data and often think: There
will be something valuable about the results.
However, this is not for certain. There is a famous German
metaphor of the hammer and the nail, making clear, that it’s
not the most important thing in the world that there is data
but that it depends on which problems they might track
down and which solutions they can create when there are
non yet.
#1 hammer & nails

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#2 more (data) quality
than quantity
It’s not only the amount of data but it’s about the quality of
the collected data so that you can filter reliable information
out of them.
Nowadays it's quite easy to get information that would have
been hard to get about ten years ago. However, the
reliability of data has not increased necessarily. Everyday
we need to respond to polls constantly, fill in forms for
newsletters etc. We know that the data, we entrust
Facebook etc., live their own life - not just since the NSA
scandal.
Therefore we aren’t completely honest sometimes – when
we respond at all. It’s our choice to say anything and if we
do, what we want to say. This raises the question: Is the
data we collect actually qualitative high and how solid are
the conclusions you’re drawing?

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#3 be a team, not a tool
GSMA
Even with a sufficient data quality it’s not guaranteed that a
lot of data automatically mean success. It’s rather about the
talents ensuring that data also lead to useful insights that
can be used for their business.
Of course, this includes a dedicated data scientist, someone
who is able to deal with large amounts of data and who can
“edit” them with statistical techniques. It’s not wrong to have
a data professional up your sleeve, but he works best within
a dedicated team consisting of IT experts and other
classical business intelligence professionals.
Or else the data analysis will be rather complicated and
hardly communicable. We need efficient solutions in order to
shape the data collection und its analysis and to find data-
based solutions and improvements for existing and new
processes within the company.

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#4 cultural change
It seems like all these insights make a cultural change within
the companies necessary. Although requirements like these
are often made vaguely, the expectations of the outcome
are enormous. But it's less a revolution rather than an
evolution.
Data is often still seen as the solution, not as the road to
success. Especially in the field of communications data
never is the solution but merely a tool to find exactly that.
Unlike in physics the laws that regulate human behavior are
very difficult to predict. A vast number of factors are
important for any answers. Data will help us to work and
become more accurate and better.
The more we know about our target audience the more can
we communicate. This requires a new mindset. Data won’t
lead us to that one perfect solution but it can help to select
and to continuously improve the communication. In the age
of content marketing, it is important to remember how to
optimize content. Improving the communication is the heart
of data analysis and that means to do more experiments
and to test different contents and formats. It’s not big data
making the difference but the mindset. It’s the data basis
that allows us to become better and better.

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