What Exactly is Retail Big Data?
Big Data is a term that
has been thrown around a lot as of late. It seems like
the more we hear about it the more confused we get
because everyone has their own answers to what big data
is. It’s about time someone cleared the air and
explained what big data is, and what it has to do with
the retail industry. To get us started, let’s take a
look at the proposed definition of Retail Big Data by
our very own Derek Rodner:
Retail Big Data (n.) – The collection of large
amounts of (structured and unstructured) data from
multiple, disparate, and often unrelated systems into a
single repository to enable retailers to more
efficiently and effectively understand their business
and their customers in a timely manner.
This is an excellent starting point, and if broken down
part by part it starts to make sense.
Size of Retail Big Data
Retail big data is a collection of large amounts of
data. This may seem obvious, it is called BIG data after
all. But this is actually the least important factor of
retail big data. If this comes as a surprise to you
consider this: it can be argued that every retailer, no
matter how big or small, collects large amounts of data.
This is where most of the confusion comes in, everyone
says that big data is big and no one says why it’s big.
If you ask me, big data has nothing to do with the size
of the data collected and everything to do with the type
of data collected.
Sources of Retail Big Data
In Derek’s definition he says that the data comes “from
multiple, disparate, and often unrelated systems” and
must go into a “single repository.” This is what makes
retail big data important. It’s all about the type of
data you collect, and where it goes. Most retailers
already collect data from all possible inputs, but few
send it to the same place. It may seem cumbersome at
first to collect marketing data and inventory data in
the same place, but think of the possibilities.
Collecting all of your data in one place will allow you
to see trends you would have never thought possible.
Collecting all of this information in one place also
gives you a much better view of how your stores are
performing as a whole.
To show just how important collecting all of your data
in one place is, here’s an example: I was watching a
show last week that described how UPS is able to get a
package overnight from their Louisville, Kentucky hub to
a home in New Jersey. They collect data on all of their
trucks, all of their planes, road conditions, and routes
with the most right turns among other things. If this
data were collected and analyzed in a standard siloed
fashion it would take a few lifetimes to produce the
shortest route. In other words the data would be
useless. Their solution? Collect all of that data in a
single place where the data can be analyzed
simultaneously to produce the shortest route in seconds,
Imagine all the time you would have on your hands if you
were able to generate reports in mere seconds instead of
weeks. This brings me to the final part of the retail
big data definition.
Speed of Retail Big Data
The definition of Retail Big Data says that in order for
all of your data to be useful, it must be collected in a
“timely manner.” This may not be a defining feature of
big data, but it is needed for the data to work (just
look at the UPS example above). It’s not enough to
collect all of your data in one place, the software must
be powerful enough to give feedback in seconds. Being
able to catch an issue as it’s developing means you can
fix it before it affects your bottom line. That gives
your store a competitive advantage that most others
Big Picture for Retail Big Data
Big Data isn’t about the size of the data you collect,
it’s about the type of data you collect. Now knowing
what retail big data is and how it can affect you, you
may be thinking a solution is too good to be true. Well,
it’s not. Here at Agilence we developed a solution that
collects all of your data in one spot and is powerful
enough to provide feedback and trends in seconds. Retail
20/20 gives you unparalleled insight into your stores.
It’s time to stop working for the data, and have the
data work for you.