Big data is still relatively new and it is very exciting. The opportunities, if not necessarily endless, are are at least incredibly rich and varied. Aiming to bridge the link between Big Data as a Technology and Big Data as Business Value, we hope our presentation will help frame some of your thinking on how to use and benefit from this topical development.

1. TRANSLATING TECHNOLOGY INTO
BUSINESS
Let’s make money from Big Data!
JUNE, 2014

2. About
“Transla.ng
Technology
into
Business”
June
2014
2
• B
Spot
helps
clients
transform
technology
ideas
into
business
concepts.
• As
part
of
our
on
going
effort
to
add
value,
we
publish
monthly
content
related
to
this
topic
on
our
website.
“Transla?ng
Technology
into
Business”
is
aimed
at
organisa?ons
and
individuals
who
want
to
understand
some
of
the
changes
and
impact
that
technology
developments
have
on
industries
and
business.
• Our
short
presenta?ons
are
not
deep
technical
documents;
rather,
they
are
business-­‐
orientated,
analy?cal
opinion
pieces
and
perspec?ves
about
the
dynamics
surrounding
technology
developments
and,
most
importantly,
the
opportuni?es
that
these
create.
• B
Spot’s
presenta?ons
are
free
to
download.
• If
you
have
any
further
ques?ons,
sugges?ons
for
new
topics,
or
comments
please
contact
beatrice@bspotconsul?ng.com
Enjoy!
B
Spot

3. Content
June
2014
3
§ Explaining
Big
Data
§ Evolu?on
§ Market
segmenta?on
§ Market
size
and
forecast
§ Demand
analysis
§ Spot
on
…
what
you
need
to
take
away

4. Big
data
technologies
are
just
tools;
the
real
value
comes
from
what
we
make
out
of
it
Explaining
Big
Data
June
2014
4
Big
Data
is
data
that
is
too
large,
complex
and
dynamic
for
any
conven.onal
data
tools
to
capture,
store,
managed
and
analyse.
The
right
use
of
Big
Data
allows
analysts
to
spot
trends
and
gives
niche
insights
that
help
create
value
and
innova.ons
much
faster
than
conven.onal
methods.
Source:
Vipro
Volume
Velocity
Variety
Amount
of
data
stored
worldwide
(in
petabytes)
>
3,500
North
America
>
50
La?n
America
>
2,000
Europe
>
250
China
>
50
India
>
200
Middle
East
>
400
Japan
• People
to
people:
Social
networks,
web
logs,
virtual
communi<es,
etc.
• People
to
machines:
medical
devices,
archives,
digital
TV,
e-­‐commerce,
smart
cards,
bank
cards,
computers,
mobiles,
etc.
• Machines
to
machines:
Sensors,
GPS
devices,
bar
code
scanners,
surveillance
cameras,
scien<fic
research,
etc.
The
speed
at
which
new
data
is
being
created
–
and
the
need
for
real-­‐<me
analy<cs
to
create
business
value
from
it
-­‐-­‐
is
increasing
thanks
to
digi<sa<on
of
transac<ons,
mobile
compu<ng
and
the
sheer
number
of
internet
and
mobile
device
users.

5. Big
data
is
far
from
new
but
has
only
in
recent
.mes
been
recognized
as
an
industry
Evolu.on
June
2014
5
Source:
Bspot
analysis
1989
2005
2011
2012
2013
Tim
Berners-­‐
Lee
invents
the
Web
and
mass
digital
data
collec.on
starts
Steve
Jobs
became
one
of
the
first
people
in
the
world
to
have
his
en.re
DNA
sequenced
as
well
as
that
of
his
tumor
–
first
person
to
use
Big
Data
to
try
to
safe
his
life.
The
open
source
Big
Data
framework
called
Hadoop
has
been
all
about
innova.ve
ways
to
process,
store,
and
eventually
analyze
huge
volumes
of
mul.-­‐
structured
data.
From
the
.me
of
its
incep.on
by
Doug
CuUng
at
Yahoo
un.l
2011
or
so,
the
majority
of
enhancements
to
the
plaZorm
have
been
mostly
focused
on
new
and
be[er
ways
to
accomplish
this
core
func.on.
The
amount
of
data
created
both
inside
corpora.ons
and
outside
the
firewall
via
the
web,
mobile
devices,
IT
infrastructure,
and
other
sources
is
increasing
exponen.ally
each
year.
From
2005
to
2020,
the
digital
universe
will
grow
by
a
factor
of
300,
from
130
exabytes
to
40,000
exabytes,
or
40
trillion.
Google
self-­‐drive
car
based
on
big
data
intelligence
is
being
developed
Further
development
of
visual
techniques
and
technologies
used
for
crea.ng
images,
diagrams,
or
anima.ons
to
communicate,
understand,
and
improve
the
results
of
big
data
analyses,
e.g.
tag
cloud,
clustergram,
history
flow,
spa.al
informa.on
flow,
etc.
Major
IT
vendors
aggressively
entered
the
big
data
space
despite
making
li[le
revenue
from
it
but
recognizing
future
poten.al
and
massive
impact
on
their
hardware,
sobware
and
other
services
impact.
Following
an
example
from
retail
and
stock
exchange
markets
other
industries
have
started
using
big
data
tools
for
their
internal
and
external
purposes.
Mainly
for
customer
segmenta.on
and
product
development.
2014
Ed
Snowden
exposes
mass
surveillance
and
big
data
abuse
by
the
US
and
the
UK
authori.es.
The
issue
of
privacy
and
correct
usage
of
big
data
became
an
urgent
issue.
Major
infrastructure
in
big
data
investments
taking
place.

6. The
market
is
s.ll
generally
very
fragmented
Market
segmenta.on
6
• Storage
• Servers
• Networking
Vendors
include
Dell,
HP,
IBM,
Cisco
Hardware
Big
Data
Distribu.ons
Data
Management
Components
Analy.cs
and
Visualisa.on
Services
• Community
Hadoop
distribu<ons
• Enterprise
Hadoop
distribu<ons
• Non-­‐Hadoop
Big
Data
framework
Vendors
include
Cloudera,
IBM,
MapR,
LexisNexis,
MicrosoW
• NoSQL
databases
• Data
integra<on
• Data
quality
and
governance
Vendors
include
Data
Stax,
IBM,
Informa<ca,
Syncsort
• Analy<c
development
pla[orms
• Advanced
analy<cs
applica<ons
• Data
visualisa<on
tools
• Business
intelligence
applica<ons
Vendors
include
Karmasphere,
Tresata,
Datameer,
SAS
Ins<tute,
Tableau,
Revolu<on
Analy<cs
• Consul<ng
• Training
• SoWware
maintenance
• Hardware
maintenance
• Hos<ng/cloud
Vendors
include
Think
Big
Analy<cs,
Amazon
Web
Services,
Accenture,
as
well
as
services
associated
with
enterprise
distribu<ons
(e.g.
Cloudera).
Next
Genera.on
Data
Warehouse
• MPP,
columnar
data
warehouse
appliances
• In-­‐memory
analy<cs
engines
Vendors
include
EMC
Greenplum,
HP
Ver<ca,
Teradata
Aster
Data,
IBM
Netezza,
SAP,
MicrosoW,
Kognito
Source:
Wikiban
June
2014

7. Almost
40%
of
the
market
is
held
by
8
companies
and
they
supply
mainly
hardware
Market
segmenta.on
7
Big
Data
revenue
split
by
type
compiled
by
Wikibon.org,
2012
Source:
Wikibon,
companies
data
0
500
1,000
1,500
2,000
2,500
IBM
HP
Teradata
Dell
Oracle
SAP
EMC
Cisco
MicrosoW
Accenture
Fusion-­‐io
PwC
SAS
Ins<tute
Splunk
Palan<r
Deloiee
Amazon
NetApp
Hitachi
Opera
Solu<ons
Mu
Sigma
TCS
Intel
MarkLogic
Booz
Allen
Hamilton
Cloudera
Ac<an
SGI
Capgemini
1010data
Orginal
Device
Manufacturers
Others
June
2014
Top
8
players
holding
40%
market
share
but
big
data
revenues
are
s<ll
1%
or
less
of
their
overall
annual
revenues
• Leading
IBM
offers
the
largest
product
and
services
por[olio
and
is
one
of
the
biggest
promoters
of
Big
Data.
• Second
revenue
generator
in
2012,
HP,
made
money
from
from
Big
Data-­‐related
services,
followed
by
sales
of
hardware
to
support
Big
Data
deployments.
HP
by
its
sheer
size
is
in
a
posi<on
to
impact
and
par<cipate
in
a
number
of
Big
Data
deployments.
• Others,
combina<on
of
hundreds
of
exis<ng
and
start-­‐ups,
will
be
the
most
dynamic
contributors
group
to
the
big
data
companies.
• The
mix
of
big
data
technology
developers
and
big
data
service
providers
will
be
changing.
Any
company
involved
in
data
gathering,
and
using
latest
analy<cal
tools
can
call
themselves
big
data
company.
That
will
have
an
impact
on
exis<ng
industry
of
market
research
which
will
be
under
pressure
to
either
transform
or
join
big
data
market.

8. 8
There
are
opportuni.es
for
different
type
of
players,
new
and
exis.ng,
to
make
inroads
into
big
data
Market
segmenta.on
Big
data
produc?on
Big
data
management
Big
data
consump?on
Source.
CM
Research
• Social
media
• Documents
• Databases
• Web
crawlers
• Web
robots
• Sensors
• Voice
• Music
&
video
• Email
• RFID
• Call
records
• Payment
details
• GPS
Volume
Velocity
Variety
Storage
Big
Data
quality
Security
Analy.cs
Databases
Data
mining
Search
Digital
marke.ng
Re-­‐selling
June
2014

9. Big
data
is
the
fastest
growing
market
since
the
discovery
of
the
Internet
Market
size
and
forecast
9
0
10
20
30
40
50
60
2011
2012
2013
2014
2015
2016
2017
Source:
Wikiban,
IDC,
IBM;
Bspot
analysis
Market
revenues
and
forecast
for
Big
Data,
2011-­‐2017
USD
Billion
7.2
11.4
18.2
28.0
37.9
43.7
47.8
31%
growth
CAGR
61%
annual
growth
June
2014
An
es<mated
total
value
of
big
data
including
revenues
coming
from
the
sale
of
hardware,
soWware
and
services
but
also
revenues
coming
from
the
value
big
data
tools
have
been
genera<ng.
An
es<mated
l
value
of
big
data
including
revenues
coming
from
the
sale
of
hardware,
soWware
and
services.
Growth
driven
by
increasingly
more
adopters
beyond
Web
star<ng
using
big
data
tools
not
only
retailers
but
also
pharma,
energy,
financial
services.
More
investment
being
poured
into
big
data
technology
especially
by
larger
companies
like
Google,
Facebook
and
Amazon
driving
the
prices
dawn
and
allowing
the
access
to
big
data
tools
to
wider
customer
base.
The
technology
of
big
data
is
maturing,
especially
soWware
like
Hadoop,
NoSQL
data
stores,
in-­‐memory
analy<c
engines
and
analy<c
databases.

10. Key
growth
factors
include:
matura.on
of
sobware,
growing
awareness
of
benefits,
growth
in
investment
Market
size
and
forecast
10
June
2014
• Increased
awareness
of
the
benefits
of
Big
Data
as
applied
to
industries
beyond
the
Web,
esp.
financial
services,
pharmaceu<cals,
and
retail.
• Matura<on
of
Big
Data
soWware
such
as
Hadoop,
NoSQL
data
stores,
in-­‐memory
analy<c
engines,
and
massively
parallel
processing
analy<c
databases
• Industries
will
start
using
big
data
analy<cs
more
frequently
and
they
will
increase
the
level
of
decision-­‐making
process
on
it
following
beeer
understanding
of
the
services
provided
by
big
data
vendors.
• Following
first
wave
of
big
infrastructure
investments
coming
from
big
companies
and
organisa<ons
there
should
be
a
second
wave
of
investment
boost
coming
from
non-­‐IT
companies.
• Smart
devices
including
computers,
smart
phones
but
also
smart
devices
used
by
industries
e.g.
smart
meters,
sensors,
etc.
will
drive
faster
adop<on
of
big
data
usage.
It
will
help
to
grow:
It
will
con?nue
to
be
a
challenge:
• Data
is
moving
from
structured
to
unstructured
format,
raising
the
costs
of
analysis.
This
creates
a
highly
lucra<ve
market
for
analy<cal
search
engines
that
can
interpret
this
unstructured
data.
• Proprietary
database
standards
are
giving
way
to
new,
open
source
big
data
technology
pla[orms
such
as
Hadoop.
This
means
that
barriers
to
entry
may
remain
low
for
some
<me.
• Many
corpora<ons
are
op<ng
to
use
cloud
services
to
access
big
data
analy<cal
tools
instead
of
building
expensive
data
warehouses
themselves.
This
implies
that
most
of
the
money
in
big
data
will
be
made
from
selling
hybrid
cloud-­‐based
services
rather
than
selling
big
databases.
• In
future,
a
growing
propor<on
of
big
data
will
be
generated
from
machine
to
machine
(M2M)
using
sensors.
M2M
data,
much
of
which
is
business-­‐cri<cal
and
<me-­‐sensi<ve,
could
give
telecom
operators
a
way
to
profit
from
the
big
data
boom.
• Legisla<on
issues
including
privacy
concerns,
data
security
and
intellectual
property
rights
are
s<ll
unresolved
and
it
will
need
to
be
regulated
and
cross-­‐regional
and
global
standards
will
have
to
be
introduced.
Source:
Wikiban,
IDC,
IBM;
Bspot
analysis

11. Currently
hardware
suppliers
are
the
biggest
revenue
generators,
but
sobware
and
services
are
the
future
winners
Market
size
and
forecast
11
34%
22%
16%
8%
8%
5%
3%
2%
2%
Professional
services
Compute
Storage
SQL
Applica<ons
XaaS
Networking
NoSQL
Infrastructure
soWware
39%
41%
20%
Services
Hardware
SoWware
Big
Data
sobware
and
services
revenue
split,
2013
Big
Data
revenue
split
by
type,
2013
Source:
Wikiban,
IDC,
IBM;
2013
June
2014
Hardware
sales
will
con<nue
enjoying
good
market
condi<ons
in
the
short
to
medium
term.
Once
large
players
will
sa<sfied
their
needs
for
inves<ng
in
big
data
infrastructure,
there
will
be
smaller
players
and
companies
from
other
non-­‐IT
industries
needing
hardware
for
building
big
data
internal
capabili<es.
At
the
same
<me
soWware
and
services
providers
will
con<nue
to
grow
and
in
the
long
term
they
will
increase
in
its
significance
over
hardware
which
will
eventually
commodi<zed.
According
to
Wikibon
analysis,
vendors
will
con<nue
using
NoSQL
and
in-­‐memory
database
soWware,
streaming
analy<c
pla[orms,
ver<cally
focused
analy<cal
and
transac<onal
applica<ons
and
applica<on
development
pla[orms
(both
on-­‐
premise
and
Cloud-­‐based)
and
associated
consul<ng
and
professional
services
to
address
specific,
high-­‐value
business
problems
and
opportuni<es.

12. Industries
focusing
on
consumer
needs
like
retail,
banking,
telecoms
are
the
first
to
use
big
data
tools
Demand
analysis
12
1
10
5
2018
2012
2015
year
Electronics
and
computers
Telecommunica.on
Healthcare
U.li.es
Media
On-­‐line
services
Retail
Public
services
Professional
services
Financial
services
Defense
and
Police
Manufacturing
Transporta.on
Automo.ve
Educa.on
Travel
First
adopters
Laggards
Source:
Bspot
analysis
Natural
resources
Construc.on
Sport
Airline
June
2014
Level
of
adop.on

13. In
the
future,
it
will
be
industries
driving
the
big
data
development,
not
IT
companies
(1/3)
Demand
analysis
13
Financial
services
Healthcare
Retail
June
2014
• About
70%
of
the
industry
is
already
using
big
data
and
analy<cs.
For
example
big
data
has
been
used
for
a
long
<me
in
the
trading
industry.
In
fact,
using
mathema<cal
algorithms
for
lots
of
data
analy<cs
is
traders
specialism
but
also
great
trading
secret.
• Banks
and
financial
services
firms
are
also
turning
to
big
data,
using
insights
pulled
out
of
daily
transac<ons,
market
feeds,
customer
service
records,
loca<on
data,
and
click
streams
to
carve
out
new
business
models
and
services
and
transform
how
they
go
to
market.
They
also
using
big
data
to
focus
on
opera<onal
issues
–
risk,
efficiency,
compliance,
security
and
making
beeer
decisions.
Some
of
the
ideas
financial
services
firms
can
use
big
data
for:
personalised
services,
loan
decisions
support,
improve
customer
loyalty,
op<mize
return
on
equity,
combat
fraud
and
mi<gate
opera<onal
risk,
iden<fy
new
revenue
streams.
• Walmart
pioneered
the
use
of
big
data
to
improve
opera<onal
efficiency
in
the
retail
industry
well
before
the
term
big
data
even
existed.
The
company
streamlined
its
complex
supply
chain
to
take
advantage
of
economies
of
scale,
thus
limi<ng
excess
inventory
and
reducing
associated
costs.
Than,
the
retailer
passed
on
some
of
these
big
data-­‐enabled
savings
to
customers
in
the
form
of
low
prices
undercut
the
retailer's
compe<<on.
• Retailers,
service
companies
and
consumer
goods
producers
are
the
most
hungry
of
big
data
intelligence
on
their
customers.
Big
data
analysis
are
used
for
customers’
segmenta<on,
marke<ng
to
enhance
customers
reten<on
and
understanding
demand
for
new
products
and
services.
Dynamic
price
op<miza<on,
video-­‐enabled
store
layout
and
product
placement
analysis,
staffing
analysis
and
decision
support,
suppliers
analysis
and
op<miza<on
of
supply
<ming,
pricing
and
sourcing,
knowledge
of
customers'
buying
paeerns
and
behavior
are
addi<onal
ways
how
retails
can
capitalise
on
big
data
input.
• The
pharmaceu<cal
industry
began
mining
and
aggrega<ng
sales
and
prescrip<on
data
because
this
lever
helped
companies
improve
their
boeom
line
by
more
effec<vely
targe<ng
sales,
managing
sales
force
resources,
and
selec<ng
prime
areas
for
R&D.
A
number
of
pharma
companies
are
already
using
big
data,
among
them,
Bristol
Myers
Squibb.
BMS
has
spent
nearly
$46
billion
on
research
and
development
since
1997,
indexes
hundreds-­‐of-­‐thousands
of
clinical
documents
per
year
in
pursuit
of
insights
that
will
improve
the
drug
discovery
process.
BMS
is
using
soWware
from
HP
to
analyze
research
and
market
data
to
be
used
by
clinical
researchers
and
scien<sts.
• For
medical
devices
manufacturers
big
data
pla[orms
can
become
substan<ally
more
intelligent
by
including
modules
that
use
image
analysis
and
recogni<on
in
databases
of
medical
images
(X-­‐ray,
CT,
MRI)
for
pre-­‐diagnosis
or
that
automa<cally
mine
medical
literature
to
create
a
medical
exper<se
database
capable
of
sugges<ng
treatment
op<ons
to
physicians
based
on
pa<ents’
medical
records.
In
addi<on,
clinical
decision
support
systems
can
enable
a
larger
por<on
of
work
to
flow
to
nurse
prac<<oners
and
physician
assistants
by
automa<ng
and
facilita<ng
the
physician
advisory
role
and
thereby
improving
the
efficiency
of
pa<ent
care.
• Public
health
can
benefit
enormously
from
big
data.
Wider
variety
of
health
care
informa<on,
making
them
more
informed
consumers
of
the
medical
system.
Pa<ents
could
be
able
to
compare
not
only
the
prices
of
drugs,
treatments,
and
physicians
but
also
their
rela<ve
effec<veness,
enabling
them
to
choose
more
effec<ve,
beeer-­‐targeted
medicines,
many
customized
to
their
personal
gene<c
and
molecular
makeup.
Pa<ents
could
also
have
access
to
a
wider
range
of
informa<on
on
epidemics
and
other
public
health
informa<on
crucial
to
their
well-­‐being.

14. In
the
future,
it
will
be
industries
driving
the
big
data
development,
not
IT
companies
(2/3)
Demand
analysis
14
Public
sector
U?li?es
Educa?on
Telecos
June
2014
• Intelligent
use
of
smart
meter
data
will
allow
u<li<es
companies
to:
beeer
monitor
and
forecast
energy
consump<on
paeerns;
iden<fy
inefficient
energy
use
at
both
the
macro
and
household
levels;
accurately
predict
poten<al
power
outages
and
equipment
failures
before
they
occur;
improve
customer
segmenta<on
and
tailor
service
offerings
based
on
customer
behavior.
• Smart
grids
will
be
the
next
step
of
managing
energy
informa<on
but
start
grids
are
s<ll
not
common
yet,
IT
companies
need
to
get
started
to
collaborate
with
u<li<es
now.
The
level
of
sophis<ca<on
in
managing
and
analysing
data
from
smart
grids
is
even
higher.
Apart
from
smart
meters
data
there
will
also
will
be
grids
data,
energy
distribu<on
data,
IT
databases
data
and
others.
• Addi<onally,
u<li<es
are
already
able
to
use
data
about
their
customers
to
offer
beeer
or
new
services,
reduce
customers’
churn,
brand
monitoring
and
even
support
machine
performance
monitoring
and
supervision.
• EDF
Energy,
using
SAS
big
data
pla[orm,
has
created
a
dedicated
analy<cs
func<on
to
focus
on
key
areas
including
customer
segmenta<on,
churn
assessment,
probability
modeling
and
product
placement
modeling.
• Governments
have
lots
of
data
available
and
its
wise
usage
can
be
beneficial
for
the
administra<on
as
well
as
ci<zens.
Big
data
used
by
governments
will
enable
people
to
make
beeer
choices
about
the
public
services
they
use
and
to
hold
government
to
account
on
spending
and
outcomes.
• Big
Data
is
also
providing
the
raw
material
for
innova<ve
new
business
ventures
and
for
public
service
professionals.
• According
to
the
UK
free
market
think
thank
Policy
Exchange,
the
UK
government
could
save
up
to
£33
billion
a
year
by
using
public
big
data
more
effec<vely.
McKinsey
has
inves<gated
that
the
poten<al
annual
value
to
Europe’s
public
sector
thanks
to
big
data
is
250
billion
Euro.
• Educa<on
has
always
had
the
capacity
to
produce
a
tremendous
amount
of
data,
more
than
maybe
any
other
industry.
The
benefits
range
from
more
effec<ve
self-­‐paced
learning
to
tools
that
enable
instructors
to
pinpoint
interven<ons,
create
produc<ve
peer
groups,
and
free
up
class
<me
for
crea<vity
and
problem
solving.
Big
data
could
enable
customized
modules,
assignments,
feedback
and
learning
trees
in
the
curriculum
that
will
promote
beeer
and
richer
learning,
customise
courses
and
even
big
data
can
be
used
in
admissions,
budge<ng
and
student
services
to
ensure
transparency,
beeer
distribu<on
of
resources
and
iden<fica<on
of
at-­‐risk
students.
• Telcos
already
have
the
customer
profile
data
with
demographics
informa<on
(age,
income,
gender,
profession,
etc.),
subscriber
usage
and
loca<on.
The
simple
thing
is
to
put
together
the
knowledge
of
the
customer
and
proac<ve
customer
service:
offer
with
renewing
contract
ahead
of
expira<on,
roaming
discounts
ahead
of
foreign
travel,
etc.
Basically,
the
amount
of
data
hold
by
telcos
on
their
customers
is
a
marke<ng
goldmine
and
apart
from
helping
to
increase
revenues
it
will
also
support
to
reduce
subscribers’
churn,
control
cost
of
acquisi<on
simula<on
tools,
reduce
opera<ng
costs,
help
with
fraud
detec<on,
help
products
improvements
and
tailor
upon
customers’
needs
in
real
<me,
etc.

15. In
the
future,
it
will
be
industries
driving
the
big
data
development,
not
IT
companies
(3/3)
Demand
analysis
15
Manufacturing
Avia?on
Automo?ve
Professional
services
June
2014
• Thanks
to
advanced
analy<cs
of
all
customer
transac<onal
data
and
external
data
sources
(e.g.
social
media),
automakers
will
be
able
to
make
improvements
in
customer
acquisi<on,
customer
reten<on
and
manage
beeer
return
on
marke<ng
investment.
Addi<onally,
the
automo<ve
sector
is
able
to
use
big
data
for
op<mizing
supply
chains,
predict/an<cipate
maintenance;
connec<ng
data
from
the
vehicles,
or
the
devices
they
integrate
with,
to
relay
informa<on
from
vehicle
to
vehicle
(V2V),
and
vehicle
to
infrastructure
(V2I)
too;
GPS
and
Satellite
Naviga<on
systems
performing
in
real
<me,
etc.
• Big
data
offers
significant
inroads
for
making
cars
safer
–
mostly
through
its
ability
to
automate
func<onality.
On
board
vehicle
systems
can
now
inform
each
other
of
their
whereabouts
and
of
other
hazards
in
the
road
so
that
drivers
can
avoid
collisions.
• Google's
self-­‐drive
car
is
an
example
of
using
big
data
in
automo<ve
to
use
external
and
internal
data
for
this
inven<on.
• By
analysing
data
created
by
jet
engines
and
sensors
that
collect
data
on
the
surrounding
environment
(temperature,
humidity,
air
pressure,
etc.),
service
providers
are
able
to
predict
when
various
parts
are
likely
to
fail
and
take
preventa<ve
maintenance
ac<on.
Replacing
a
soon-­‐to-­‐fail
part
before
it
malfunc<ons
is
significantly
less
costly
than
doing
so
aWer
the
part
fails
during
opera<ons.
More
efficient
jet
engines
consume
less
fuel
and
emit
fewer
environmentally
contamina<ng
gasses.
• Other
advantages
of
using
big
data
tool
by
avia<on
are:
preventa<ve
maintenance
reduces
aircraW
“down
<me”
,
improved
customer
sa<sfac<on,
<cket
pricing
predic<ons
and
others.
• New
revenue
genera<on
tools.
Bri<sh
Airways
for
its
new
personalized
service
and
offers
program,
Know
Me.
It
collects
and
tracks
an
usual
amount
of
data
on
individual
passengers,
their
preferences
and
travel
history.
Data
on
the
online
behavior
and
buying
habits
of
20
million
Bri<sh
Airways
customers,
crea<ng
hundreds
of
predic<ve
signals
that
suggest
a
person’s
“behavioral
DNA
to
offer
new
services.
• Big
data
can
help
manufacturers
reduce
product
development
<me
by
20
to
50
percent
and
eliminate
defects
prior
to
produc<on
through
simula<on
and
tes<ng.
That
a
massive
saving
for
the
R&D
process.
• Manufacturers
could
capture
a
significant
big
data
opportunity
to
create
more
value
by
ins<tu<ng
product
lifecycle
management.
Designers
and
manufacturing
engineers
can
share
data
and
quickly
and
cheaply
create
simula<ons
to
test
different
designs.
Big
data
can
help
with
further
improvements
in
product
quality,
use
real-­‐<me
data
from
sensors
to
track
parts,
monitor
machinery,
and
guide
actual
opera<ons.
• Taking
inputs
from
product
development
and
historical
produc<on
data
(e.g.,
order
data,
machine
performance),
manufacturers
can
apply
advanced
computa<onal
methods
to
create
a
digital
model
of
the
en<re
manufacturing
process.
• First
adopters
are
management
consultancy
and
market
research
companies
to
replace
manual
data
mining
to
speed
up
analyst
work
in
order
to
focus
more
on
analy<cs
and
value
to
the
clients
rather
than
data
provider.
• Legal
firms
and
accountancy
companies
are
known
to
be
tradi<onal
and
slow
with
implemen<ng
technologies.
On
the
other
hand
they
collect
and
store
massive
amount
of
data
and
their
services
are
also
based
on
finding
the
right
data
and
correctly
apply.
Introducing
big
data
tools
will
help
them
with
overall
performance,
speed
and
accuracy.

16. Spot
on…
what
you
need
to
take
away
16
June
2014
For
vendors:
§ To
mone<se
your
innova<ons
and
solu<ons,
transform
your
big
data
concepts
into
value
proposi<ons
that
are
based
on
ac<onable
insights
that
drive
revenue
and/
or
reduce
costs
for
your
customers.
§ Integrate
big
data
from
structured,
mul<-­‐structured
and
unstructured
data
from
various
(internal
and
external)
source
system
together
in
a
common
pla[orm.
§ Put
safeguards
in
place
to
address
public
concerns
about
big
data,
including,
but
not
limited
to,
privacy,
security,
intellectual
property,
and
liability.
For
companies:
§ Manage
big
data
as
a
corporate
asset
and
educate
employees
on
how
to
iden<fy
business
requirements
for
big
data
projects
and
effec<vely
communicate
insights
extracted
from
big
data
to
the
business.
§ Trust
big
data
input
and
make
analy<cs-­‐driven
decision
rather
than
follow
“gut
ins<nct”.
§ Protect
compe<<vely
sensi<ve
data
or
other
data
that
should
be
kept
private
or
corporate
secret.

17. www.bspotconsulting.com