“The $64,000 Question” began as a radio show in
1940, and went through a series of transformations and ultimately, adoption to
television until it finally left the air in 1952. Contestants
were asked questions of progressive difficulty as the stakes of the game
increased
. I still hear people who weren’t even alive at the time of the
show refer to the “$64,000 question” when they talk about the art of
asking the right question and getting the answer that they’re looking for. I
dare say getting both questions and answers right hasn’t gotten any easier with
big data
.

That is the question

The point was driven home this week in a conversation I had
with Srikanth Velamakanni, CEO and founder of Fractal Analytics, a big data
analytics provider. “What we see with companies and how they are using their
big data is actually a bigger issue than the answers that they are deriving
from their big data,” said Velamakanni. “We believe that the big
issue is whether they are asking the right questions in the first place.”

It’s a challenge for organizations which now find they are
swimming in data, yet struggling to identify the really relevant questions they
should be asking.

Some organizations try to harness their big data by structuring
analytics questions around specific business cases that they want to solve or
better understand. The thinking behind this is that if you have specific objectives
and a tightly constricted focus, you won’t get “off course” in your
big data probes and questions asking, and you will likely arrive at results faster.

Yet, most of us only have to go through a current report catalogue
in the average company to see that there already is information on company business
line and product profitability, quarterly and annual financial results that can
be compared with financial performance one year ago, statements of inventory
surpluses and shortages, and even reports that show how many new accounts the
company has gained (and how many it has lost). These reports are the outcomes
of historical business cases that companies have already identified in areas
like finance, operations and sales – so if a big data analytics project gets
focused too tightly around one of these existing business cases (or something
similar) the chances are high that the answer to a big data query (or at last
part of it) already exists in legacy reports somewhere.

Art of the question

Velamakanni talks about this when he discusses the art of
asking the right questions and getting the most out of big data.

“We have scientists in our data lab who tell us
something we were not expecting to get,” he said. “It comes down to
the ability to explore data. If you don’t do this data exploration well enough,
you might find that you’re too narrowly focused on solving a particular business
problem with your queries. On the other hand, when you look at the data itself
without initially limiting it with a focus that could be too narrow, you might
come away with something different.”

The exploratory approach works well in academia, where the
emphasis is on research and there is also an understanding that not every data
exploration will be successful, However, in enterprises where bottom line results
are measured and bottom line answers to questions are expected, it isn’t always
easy to adopt a more open and exploratory approach to big data queries – especially
if they don’t end up yielding results.

Of course, if you don’t dare to experiment with big data,
you’re not likely to get the ground-breaking answers you want to get, either.

This is the crossroads that many organizations find themselves
at now in their big data analytics. How they achieve a proper balance between
business case-driven results and pure data experimentation that yields
innovative, unanticipated and actionable information – might well be the next “$64,000
Question.”