Valuing Data

• Date: 01 January 2017
• Published date: 01 January 2017
• Author: Tim Chartier
• DOI: http://doi.org/10.1002/jcaf.22241

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© 2017 Wiley Periodicals, Inc.

Published online in Wiley Online Library (wileyonlinelibrary.com).

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,