Valuing Data

Date01 January 2017
Published date01 January 2017
AuthorTim Chartier
© 2017 Wiley Periodicals, Inc.
Published online in Wiley Online Library (
DOI 10.1002/jcaf.22241
V alui ng Data
Tim Chartier
We are in an age of data
deluging us like the
rains that precede a
flash flood. The storage of just
over a hundred smartphones
is enough combined memory
to store the contents of every
printed book in the Library
of Congress. Still, we find our
phones near the brink of their
storage capacity as we prepare
to take a picture or record
an audio memo. In under a
minute of the day, YouTube
users upload 72 hours of new
video content. With so much
data, it is clear that tools and
analytical methods that mine
information from such digital
stockpiles are of great value.
Given the insight such methods
can offer,it can be easy to over-
look the value of the data itself.
While businesses might receive
value from the perspectives
gained from analytics, at the
foundation of such work is the
data allowing such technologi-
cal structures to be built.
During my sabbatical
year in the 2015–2016 aca-
demic year, I served as chief
researcher for Tresata, a full
stack predictive analytics
and machine learning soft-
ware company in the big data
space. Tresata pivots around
one bold vision—to enrich
life—accomplishing this
through harnessing the intel-
ligence (from the data), and
power, of customer behavior.
The company offers end-to-
end, fully automated, proven
solutions to enterprise clien-
tele in health care, retail, and
financial services.
As I can, I like to attend
new employee or intern orien-
tation at Tresata, given the new
perspectives such times offer.
During the 2016 summer intern
orientation, the term data
asset arose. Abhishek Mehta,
CEO of Tresata, explained,
“We use the term data asset to
underscore to ourselves and
our clients that the data itself
holds value.” I must admit that
I struggled to pay attention for
the next 5 to 10 minutes. I sat
reflecting on the fundamental
way using such a term can shift
the outlook on the importance
of data in the analytics process.
Gathering data is an
imperative stage of data analy-
sis. Incomplete or inaccurate
data can limit the amount or
degrade the quality of analysis.
One might pose a question,
but without suitable data, it
might remain a query without
an answer. Using the term data
asset underscores the value in
the data and the fundamental
role collecting data plays in
making insights possible.
Data must be collected.
Only then does analysis
becomepossible and questions
answerable. As an example,
let’s turn to the NBA, which
signed a contract in 2013 with
STATS LLC to place SportVU
cameras in every arena. The
cameras are generally perched
in a stadium’s rafters, snapping
25 frames per second.
What exactly does the
SportVU cameras produce?
For each game, the NBA
receives an XML file contain-
ing raw coordinate data for
each player and referees along
with the UNIX time code and
game clock. Three-dimensional
coordinate data is also sup-
plied for the ball. This data
is supplied for each 1/25 of a
second of the game producing
a 40-45 MB file for each game.
Teams receive the data on every
game—everything except the
referee data, which is analyzed
only by the NBA.
Take a moment and
consider the amount of data
and possible insights in such
a file. For example, one can
easily track how far any given
player (or the ball for that
matter) traveled in the dura-
tion of a game. For example,

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