White Papers The Evolution of Smart Commodity Management | Page 5
Smart
Commodity
Management
A
ComTech
Advisory
Whitepaper
Of
course,
while
all
of
this
evolution
and
change
was
going
on
in
the
commodities
markets,
significant
change
was
also
occurring
in
information
processing
and
technologies.
Vendors
were
able
to
leverage
these
newer
technologies,
increasing
processing
speeds,
improving
“The
use
of
big
data
will
become
a
key
basis
of
the
user
experience,
and
introducing
greater
competition
and
growth
for
individual
firms.
From
the
modularization
of
the
applications
-‐
allowing
buyers
standpoint
of
competitiveness
and
the
potential
capture
to
pick
and
choose
just
what
they
needed
within
the
of
value,
all
companies
need
to
take
big
data
seriously.
In
common
CM
footprint.
The
use
of
flexible
tools
meant
most
industries,
established
competitors
and
new
that
the
software
could
be
more
configurable
and
entrants
alike
will
leverage
data-‐driven
strategies
to
personalizable,
reducing
implementation
timescales
innovate,
compete,
and
capture
value
from
deep
and
up-‐
and
improving
the
users’
interaction
experience
with
to-‐real-‐time
information.”
McKinsey
&
Co.,
Big
data:
The
next
frontier
for
innovation,
competition,
and
productivity.
the
software.
These
new
technologies
have
also
allowed
improved
data
visualization,
increasing
productivity
and
enhanci ng
risk
management
capabilities.
Finally,
newer
architectures
also
improved
the
connectivity
of
the
CM
solutions
making
it
easier
to
interface
or
integrate
with
third-‐party
software.
Underlying
all
of
this
however
is
data.
Back
in
the
early
days
of
natural
gas
marketing
systems,
all
that
was
really
needed
was
daily
volume
and
price
data.
As
the
industry
evolved,
more
and
more
data
was
generated,
stored
and
processed.
Simple
data
sets
grew
to
incorporate
massive
time
series
data
sets
(and
multiple
versions
of
the
same
data)
for
forward
curves
and
expanded
details
for
deals
needed
to
be
captured
such
as
contract
terms,
complex
price
structures,
timelines,
quality
requirements,
weights,
and
so
on.
Document
images
and
other
types
of
non-‐
standard
data
were
increasingly
required
to
be
stored,
especially
associated
with
scheduling
and
supply
chains.
On
the
trading
side,
storage
of
phone
recordings,
IM
logs
and
other
evidence
of
negotiations
became
more
important
as
regulations
and
regulatory
oversight
increased.
These
regulations,
such
as
Dodd
Frank
in
the
US
and
EMIR
and
REMIT
in
Europe,
have
placed
increased
demands
on
the
technologies
of
trading,
requiring
high
levels
of
transparency
and
reporting,
including
trade
reporting
to
central
repositories.
Essentially,
as
the
CM
software
category
expanded,
evolved
and
matured,
so
did
the
data
management
requirements
-‐
Big
Data,
both
structured
and
unstructured,
needed
to
be
better
stored,
manipulated
and
displayed.
Smart
Commodity
Management
A
key
requirement
of
Commodity
Management
is
that
it
needs
to
assist
users
in
making
informed
decisions
by
transforming
large
amounts
of
data
into
insights
by
providing
predictive
and
user
controllable
analytics.
Smart
commodity
management
solutions
must
provide
real-‐time,
meaningful
and
actionable
information
to
traders,
risk
managers
and
executives
in
order
to
enable
them
to
view,
analyze
and
simulate
positions,
market
movements
and
risk
metrics.
Users
need
to
be
able
to
monitor
metrics
like
position,
exposure,
inventory,
in-‐transit
movements,
scheduled
movements,
counterparty
risk
limits,
P&L,
and
so
on
by
both
commodity
and
instrument
type,
across
both
physicals
and
derivatives,
enterprise-‐wide
or
via
drill-‐down
to
trading
unit
and
individual
trader
levels.
Smart
Commodity
Management
must
provide
a
wide
range
of
tools
to
mine,
analyze
and
generate
insights
from
transactional
data,
for
use
not
only
in
strategic
decision-‐making
by
C-‐level
executives,
but
also
by
risk
managers
and
traders
for
tactical
purposes.
Unfortunately,
most
of
today’s
CM
software
systems
have
a
legacy
of
being
built
as
client/server
solutions,
focused
primarily
on
capturing
and
storing
transaction
data.
While
they
do
provide
some
tools
to
analyze
and
report
that
transactional
data,
these
capabilities
are
by
and
large
standard
risk
calculations
(such
as
VAR)
and
©
Commodity
Technology
Advisory
LLC,
2014
5