- BigML: Discoveries, reactions and communication
BigML
is without a doubt one of the most promising startups of those in which
I have some level of involvement (I'm in your strategic advisory
Board). Ideas like "machine learning for Everyone", modeling or big data
are undoubtedly powerful in an era in which data of all kinds
proliferate exponentially (I spoke of BigML earlier in this other post),
and every day, the impression is to be developing a very good tool and
to be just waiting – proactively waiting, of course – for the rest of
the world to discover.
A
model made with statistics published by Kickstarter about the projects
presented in the platform allows to create a model of success/failure in
crowdfunding, which spontaneously calls the attention of Gigaom in "How
To succeed on Kickstarter: Find 35 people and ask for less than
$9.000?" and gives rise to a wave of visits and interest. The same
medium and the same person who, in a previous article, had echoed the
existence of the company and had mentioned it in a more generic article
about machine learning titled "Your Data has a secret, but you — yes,
you — can make it talk". Meanwhile, other completely different but
equally intensive fields in data, such as web analytics or finance and
markets, begin to discover the potential of this type of tools, as in
this article in SeekingAlpha entitled "Dividends: Still the best
all-season investment strategy".
These
moments in a startup are delicious. Total concentration in the constant
improvement of the product, and sowing, constantly sowing with tools
such as blog, newsletter or social networks. Everything is worth to
dynamize the information, to put it in the way of the possible
interested. You never know where an interesting impact is going to come
from, but you know that of all those who receive a message, a certain
percentage, although small, will see applications to his field and will
try the product, to contact you, to write about it or, at least, to
develop some curiosity on the subject. It is about creating content on
diverse topics to reach a broader potential demand: stock market
indices, music, flight delays, sports ... And for each entry, its
corresponding diffusion and potential multiplier effect through social
networks like Twitter or Facebook. A strategy of communication of the
book, absolutely necessary in a complex product that is not sold as
would sell a product of consumption or of simple understanding, but that
has already surpassed the more than two thousand registered users. No,
the product is not sold in the Ark. It is what the research has: that
with each model and every set of data analyzed you have the potential to
contribute knowledge, but also to become viral: probability of injuries
in automobile accident, incidents with firearms in schools, prediction
of the number of extramarital affaires, the amount of tips ... you have
data? Here are answers and solutions.
For
an academic like me, to follow the journey of BigML is being a
rigorously real case of communication of a complex product, of those in
which you really learn. We will continue to report
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