I mentioned yesterday that I was in the Finger Lakes last
week. Well, we stopped by some wineries while we were there and it is always
interesting to read the tasting notes on the back of the bottle, or on the
tasting sheet that the winery gives to you.
I was thinking last night about this and how great it would
be if all of the data we, as an organization, create or consume came with its
own “tasting notes”. Just imagine a new
set of transactions arriving from your OLTP systems, and they come with a tag that
says something like “This data shows a correlation between the purchase of
steaks and seasoning salt.” Wouldn’t that make the job of the data scientist /
data analyst so much easier?
We also went to one winery in particular, and noted that the
wine maker did not have any tasting notes, and only described his wine as “Like
your favorite pair of slippers” or something like that. After talking about
this for a while, we found that we actually liked this approach better. Rather
than tasting and searching for what the wine maker told us he or she tasted, we
were able to develop our own impression, and detect tastes on our own. Without
being directed to a particular smell or taste, we used our own nose and palette
to decide what we tasted, and what we liked. In the end we bought more bottles
from this winery than we did from any of the other wineries that we visited.
You might be scratching your head and wonder where I am
going here, so let me explain. I believe that analytics needs to be more like
the second case above. You should not start with any preconceived notions on
your data based on what others tell you. Analyze the data, detect
correlations/patterns/trends on your own, and then check the tasting notes if
you want to.
The goal of analytics
should be to find NEW information that you can act upon, not simply find the
same thing that someone else already found.
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