1. Market
validation study
Industrial
Internet
‘Making
most
out
of
gathered
data’
San
Francisco,
Feb
13
2015
1
2. Execu@ve
Summary
The
defini@on
of
Industrial
Internet,
as
well
as
the
market
size
vary
depending
on
the
source.
However,
there
is
general
consensus
regarding
the
immense
poten@al
of
the
market.
According
to
GE,
the
industrial
internet
revolu@on
will
affect
nearly
46%
of
the
global
economy
or
€29.8
trillion
in
global
output.
There
are
several
challenges
that
need
to
be
addressed
in
order
for
the
Industrial
Internet
to
take
off.
These
difficul@es
include
a
shortage
of
talent,
the
need
for
major
IT
investments,
industry
and
cross
company
coopera@on
challenges
,
and
various
security
concerns.
One
major
threshold
is
the
s@ll
limited
capacity
to
analyze,
visualize
and
make
informed
decisions
on
the
immense
amount
of
data
made
available
through
the
industrial
internet
in
real-‐@me.
Besides
the
technological
requirements
of
an
Industrial
Internet,
such
as
sensors,
infrastructure,
and
others,
there
are
many
qualita@ve
aspects
that
will
influence
the
success
of
the
system.
New
ways
of
working,
extensive
coopera@on
between
companies
and
departments,
policy
and
standardiza@on
work,
and
the
lack
of
skilled
analy@cs
talent
are
some
challenges
that
need
to
be
resolved.
The
outlook
for
Finnish
companies
to
address
the
US
Industrial
Internet
market,
especially
when
it
comes
to
data
analy@cs
and
visualiza@on
products
and
services
is
posi@ve.
They
can
u@lize
their
credibility
and
knowledge
when
it
comes
to
design,
quan@ta@ve
analysis,
technology,
and
engineering
to
establish
thought
leadership
in
the
space.
There
is
large
demand
for
products
and
services
related
to
1;
Data
analy@cs
&
visualiza@on,
2;
Building
and
hos@ng
data
centers,
3;
Products
and
services
aimed
at
retrofibng/upgrading
exis@ng
industrial
equipment,
4;
Security
solu@ons
focused
on
the
Internet
of
Things
(IoT),
and
5;
Consul@ng,
training
and
execu@ve
educa@on
services
focused
on
addressing
the
shortage
of
approximately
1.5M
qualified
analy@cs
workers
and
managers
in
the
US
alone.
2
4. Defini@on
-‐
Industrial
Internet
The
industrial
internet
refers
to
the
integra@on
of
complex
physical
machinery
with
networked
sensors
and
sogware.
The
industrial
Internet
draws
together
fields
such
as
machine
learning,
big
data,
the
Internet
of
things
and
machine-‐to-‐machine
communica@on
to
ingest
data
from
machines,
analyze
it
(ogen
in
real-‐
@me),
and
use
it
to
adjust
opera@ons.
-‐
Coined
by
General
Electric,
2012
4
5. Defini@on
-‐
Internet
of
Things
The
Internet
of
Things
is
a
term
used
to
describe
the
ability
of
devices
to
communicate
with
each
other
using
embedded
sensors
that
are
linked
through
wired
and
wireless
networks.
These
devices
could
include
your
thermostat,
your
car,
or
a
pill
you
swallow
so
the
doctor
can
monitor
the
health
of
your
diges@ve
tract.
These
connected
devices
use
the
Internet
to
transmit,
compile,
and
analyze
data.
-‐
Execu@ve
office
of
the
President,
2014
5
6. Defini@on
-‐
Big
Data
Big
data
typically
refers
to
datasets
whose
size
is
beyond
the
ability
of
typical
database
sogware
tools
to
capture,
store,
manage,
and
analyze.
The
defini@on
can
vary
by
sector,
depending
on
what
kinds
of
sogware
tools
are
commonly
available
and
what
sizes
of
datasets
are
common
in
a
par@cular
industry
-‐
McKinsey,
2011
6
9. Key
Elements
of
the
Industrial
Internet
Source:
GE
Industrial
Internet,
Nov
2012
Intelligent
Machines
Connect the
world’s machines,
facilities, fleets
and networks with
advanced sensors,
controls and
software
applications
Advanced
Analytics
Combines the
power of physics-
based analytics,
predictive
algorithms,
automation and
deep domain
expertise
People at
Work
Connecting people at
work or on the move,
any time to support
more intelligent
design, operations,
maintenance and
higher service quality
and safety
1
2
3
9
10. The
focus
of
the
market
study
● Applica@on
of
new
found
knowledge
● Product
of
data
consump@on
● Ac@onable
informa@on
● Associa@on
of
applicable
categories
● Finding
similari@es/trends
in
data
● Search
for
predictability
● Categorize
data
● Separate
relevant
from
irrelevant
● Locate
source
and
context
● Intake
of
facts
and
sta@s@cs
● Large
quan@@es
of
informa@on
● Ogen
feedback
from
circumstance
Source:
David
McCandless,
kmbeing.com
The
Informa8on
Pyramid
10
12. Current
Market
Size
in
the
U.S.
€57.3BN
€23.1BN
€15.6BN
Industrial
Internet
Market
Big
Data
products
&
Services
Analy8cs
and
Visualiza8on
“70% of large organizations already purchase external data and 100% will do so by 2019.”
-Forbes, 2014
Source:
hlp://postscapes.com/internet-‐of-‐things-‐market-‐size,
Exchange
rate
USD-‐Euro,
0.924,
March
9,
2015
12
13. Market
Status
Industrial
Internet
Source:
hlp://postscapes.com/internet-‐of-‐things-‐market-‐size,
Exchange
rate
USD-‐Euro,
0.924,
March
9,
2015
Projec8on
of
Value
Delivered
by
industrial
internet
2012-‐2020
Projected
value
by
2020:
€1.57
Trillion
Current
US
value:
€57.3
Billion
13
14.
“Between
2013
and
2022,
$14.4
trillion
of
value
(net
profit)
will
be
“up
for
grabs”
for
enterprises
globally
—
driven
by
IoE
(Internt
of
Everything).
IoE
will
both
create
new
value
and
redistribute
(migrate)
value
among
winners
and
laggards,
based
on
how
well
companies
take
advantage
of
the
opportuni@es
presented
by
IoE.”
-‐Cisco,
2013
“The
IoT/M2M
market
is
growing
quickly,
but
the
development
of
this
market
will
not
be
consistent
across
all
ver8cal
markets.
Industries
that
already
"understand"
IoT
will
see
the
most
immediate
growth…”
-‐IDC,
2014
Market
Status
Industrial
Internet
There
is
a
lot
of
poten@al
in
the
US
Industrial
Internet
sector
both
for
companies
that
owns
data
and
for
market
players
that
aims
to
enhance
and
visualize
that
data.
The
maturity
level
of
both
the
supply
and
demand
side
varies
across
industries
and,
the
dynamics
of
the
market
will
change
over
the
next
few
years
because
of
more
sophis@cated
AI
and
machine
learning
developments
etc.
14
18. Big
Data
Market
Size
and
Status
Big
Data
Compound
Annual
Growth
Rate
(CAGR)
Predic8ons
“A
recent
IDC
forecast
shows
that
the
Big
Data
technology
and
services
market
will
grow
at
a
27%
compound
annual
growth
rate
(CAGR)
to
$32.4
billion
through
2017…”
“IoT
analy0cs
will
be
hot,
with
a
five-‐year
CAGR
of
30%”
“Looking
ahead,
the
Big
Data
market
is
currently
on
pace
to
top
$50
billion
in
2017,
which
translates
to
a
38%
compound
annual
growth
rate…”
Source:
IDC,
2014,
Forbes,
2014,
Wikibon,
2013
18
19. Big
Data
Market
Size
and
Status
• “Not
all
Big
Data
is
created
equal.
Data
associated
with
the
Industrial
Internet
–
that
is,
data
created
by
industrial
equipment
such
as
wind
turbines,
jet
engines,
and
MRI
machines
–
holds
more
poten@al
business
value
on
a
size-‐adjusted
basis
than
other
types
of
Big
Data
associated
with
the
social
Web,
consumer
Internet
and
other
sources.”
-‐Jeff
Kelly,
wikibon
• “The
IoT/M2M
market
is
growing
quickly,
but
the
development
of
this
market
will
not
be
consistent
across
all
ver8cal
markets.
Industries
that
already
"understand"
IoT
will
see
the
most
immediate
growth…”
-‐IDC,
2014
• Machine
data
is
a
cri@cal
subset
of
big
data—it’s
the
fastest
growing,
most
complex
and
most
valuable
subset
of
big
data,
largely
because
of
its
sheer
ubiquity.
Every
GPS
device,
RFID
tag,
interac@ve
voice
response
(IVR)
system,
database
and
sensor—almost
anything
that
uses
electricity—generates
machine
data
that
can
tell
companies
something
important
about
the
way
their
businesses
actually
run
each
day.
Source:
HBR,
Nov
2014
and
McKinsey
Global
Ins@tute,
June
2011
19
20. “Buying and selling data
will become the new
business bread and butter.”
-Forbes, 2014
“ 2015 will mark an inflection point of intentional
investment by mainstream firms in generating
and monetizing new and unique data sources.”
-IAA, 2014
“The use of Big Data is
becoming a crucial way for
leading companies to
outperform their peers.”
- iveybusinessjournal.com
20
22. Key
Poten@al
Target
Customers
Industry
companies
with
mission
cri8cal
infrastructure
will
grow
and
need
support
Companies
whose
products
(and
associated
technological
capabili@es)
are
central
to
overall
product
system
opera@on
and
performance,
such
as
major
mining
machines,
will
be
in
the
best
posi@on
to
integrate
the
Industrial
Internet
ecosystem.
Manufacturers
that
produce
less
system-‐cri@cal
machines,
such
as
the
trucks
that
move
the
material
extracted
from
the
mines,
will
have
less
capability
and
credibility
in
customers’
eyes
to
take
on
a
broader
system
provider
role
according
to
Harvard
Business
Review.
Large
and
midsize
corpora8ons
most
eligible
poten8al
customers
According
to
interviews
with
industry
experts,
the
most
preferable
customers
for
Finnish
companies
to
target
ini@ally
is
large
or
midsize
corpora@ons.
This
is
due
to
the
fact
that
there
needs
to
be
a
substan@al
amount
of
data
generated
in
order
for
a
company
to
value
3rd
party
products
and
services
that
generates,
analyses
and
visualize
big
industrial
data.
Source:
HBR,
Nov
2014
and
subject
maler
expert
interviews,
March
2015
22
23. Key
Sectors
in
Industrial
Internet
Source:
Cisco,
Feb
2015
23
24. Key
Market
Sector
Opportunity
Source:
McKinsey
Global
Ins@tute,
June
2011
24
25. Big
Data
levers
in
Manufacturing
Source:
McKinsey
Global
Ins@tute,
June
2011
25
27. Market
Sector
Opportunity
• Case:
Transporta@on
– Shipping
companies
that
ouyit
truck
fleets
with
sensor
technology
can
leverage
the
data
generated
to
iden@fy
more
efficient
routes
and
improve
fuel
efficiency.
– Airlines
sector
is
very
well
posi@oned
to
take
advantage
of
the
Industrial
Internet
era.
1
%
in
fuel
savings
=
$30BN
over
15
years
Source:
GE
Industrial
Internet,
Nov
2012
27
28. Market
Sector
Opportunity
• Case:
Healthcare
– Data
generated
by
high-‐value
assets
such
as
MRI
machines
can
be
monitored
and
analyzed
to
predict
the
likelihood
of
part
failure
in
advance
to
facilitate
preventa@ve
maintenance.
– Beler
understanding
likely
pa@ent
traffic
palerns
can
allow
hospitals
to
beler
allocate
resources
and
staff.
The
Industrial
Internet
is
es@mated
to
be
able
to
reduce
equipment
cost
by
15-‐30%.
It
could
also
free
up
1h
extra
care
@me
in
process
efficiency
per
day.
Source:
GE
Industrial
Internet,
Nov
2012
Given
that
the
US
Healthcare
industry
is
heavily
regulated
and
in
several
instances
lacks
up
to
date
IT-‐
Systems
to
fully
embrace
the
Industrial
Internet
revolu@on
ini@ally,
there
are
several
other
sectors
that
could
be
easier
to
address
in
the
US
before
healthcare.
28
29. Market
Sector
Opportunity
• Case:
Energy
&
Natural
Resources
– By
analyzing
data
created
by
wind
turbine
engines
and
sensors
monitoring
the
surrounding
environment
(temperature,
humidity,
air
pressure,
etc.),
service
providers
can
predict
when
various
parts
are
likely
to
fail
and
take
preventa@ve
maintenance
ac@ons
– 1
%
in
oil
efficiency
improvements
would
result
in
savings
of
$66BN
Source:
GE
Industrial
Internet,
Nov
2012
29
30. Market
Player
Overview
The
need
of
Big
Data
input
and
output
provides
massive
capitaliza@on
poten@al.
Data
analy@cs
themselves
are
used
to
organize
valuable
business
informa@on
and
insight.
Therefore
these
analy@cs
are
crucial
to
the
success
of
any
organiza@on
in
any
industry.
Below
are
some
of
the
largest
data
consumers
in
the
industry
and
a
broad
categorized
market
overview.
Data
Centers
&
Hardware
Infrastructure
&
Network
Storage
Database
Services
Integra@on
30
32. Trends
in
Data
Analy@cs
&
Visualiza@on
From data collection to data visualization – Numbers and basic data is being supported or
replaced by pedagogic visualization of information in order to enable swift and informed decisions higher up
in the information pyramid.
From batch processing of historic data to swift analysis of real time data – The increased
numbers of sensors and technologies being deployed based on the Internet of Things and Industrial Internet
Movement makes the demand for quick processing and analysis of real time data, more and more important.
From broad to deep analysis and an increase in niche experts – Larger and more established
companies such as Tableau that are providing more generic visualization of data are being challenged by an
increased rise in niche players in the data analytics and visualization field such as:
• ZoomData: Focuses on speed by rendering just a bit of data to show the real time trend quickly.
• Graphistry: Provides detailed graphs to their clients
• Recorded Future: Real time analysis and visualization of cyber threats
Source:
Subject
maler
expert
interviews,
Feb
&
March
2015
1
2
3
32
33. 4
Business
Models
Examples
1. Tableau:
Recurring
high
end
per
user
license
model.
$50.000-‐100.000/customer/year
to
have
their
sogware
in
place
+
Addi8onal
consul8ng
star@ng-‐up
costs
to
build
ini@al
customized
dashboards
etc.
2. Char8o:
SaaS
company,
cloud
based:
Purely
Sogware,
more
hands
off
and
standardized
offering
to
a
lower
prize
point
than
Tableau.
Used
for
more
specific
tasks,
like
for
sales
teams
etc.
3. Splunk:
Visualise,
analyse
and
store
your
data.
Charge
for
storing
and
analysing
data.
One
of
the
first
big
data
companies.
Hunk
is
their
offering
for
Hadoop
analy@cs,
charged
through
a
yearly
fixed
fee,
minimum
$25
000/year.
4. Palan8er:
Super
high
end
consul8ng
based
on
their
data
analysis
sofware.
Roughly
$5M/year
per
client.
Started
in
the
government
sector.
Now
Fraud
analysis
for
banks
etc.
Source:
Company
websites
and
subject
maler
expert
interviews,
March
2015
33
34. Service
Offerings
for
Big
Data
Clients
People
Analy@cs
Tailor
searches
Price
discrimina@on
Discerning
intelligible
palerns
in
data
Predic@ve
Models
Industry-‐personalized
solu@ons
Real-‐@me
Updates/Trends
Customizable
Repor@ng
Social-‐marke@ng
Op@miza@on
Char@ng
Big
Data
for
Customers
Monitor
transac@ons
end
to
end
Customer
experience
insight
Hotel
op@miza@on
Personalize
data
to
individual
searches
Source:
Inc.com,
2015
and
subject
maler
expert
interviews,
Feb
2015
34
35. Redefining
Industry
Boundaries
The
increasing
capabili@es
of
smart,
connected
products
not
only
reshape
compe@@on
within
industries
but
expand
industry
boundaries.
This
occurs
as
the
basis
of
compe@@on
shigs
from
discrete
products,
to
product
systems
consis@ng
of
closely
related
products,
to
systems
of
systems
that
link
an
array
of
product
systems
together.
Source:
Harvard
Business
Review,
Nov
2014
35
37. Talent
Gap
in
Industrial
Internet
Source:
McKinsey
Global
Ins@tute,
June
2011
37
38. Great
Need
for
Analy@cal
Talent
• McKinsey
es@mate
that
a
demand
for
deep
analy@cal
posi@ons
in
a
big
data
world
could
exceed
the
supply
being
produced
on
current
trends
by
140,000
to
190,000
posi@ons
(Exhibit
above).
Furthermore,
this
type
of
talent
is
difficult
to
produce,
taking
years
of
training
in
the
case
of
someone
with
intrinsic
mathema@cal
abili@es.
They
believe
that
the
constraint
on
this
type
of
talent
will
be
global,
with
the
caveat
that
some
regions
may
be
able
to
produce
the
supply
that
can
fill
talent
gaps
in
other
regions.
Source:
McKinsey
Global
Ins@tute,
June
2011
1.5
million
=
The
projected
need
and
gap
for
addi@onal
managers
and
analysts
in
the
United
States
who
can
ask
the
right
ques@ons
and
consume
the
results
of
the
analysis
of
big
data
effec@vely.
38
39. Skills
and
Knowledge
• Automated
decision-‐making
will
come
of
age
in
2015
and
the
organiza@onal
implica@ons
will
be
profound.
The
very
way
that
firms
operate
and
organize
themselves
will
be
ques@oned
this
year
as
common
workflows
become
ra@onalized
through
analy@cs.
Key
to
success
is
the
transparency
of
the
automated
systems
and
preparing
managers
“to
occasionally
look
under
the
cover”
of
established
models
and
algorithms.
• One
of
the
most
important
alribute
sought
in
candidates
for
big
data
analy@cs
jobs
is
communica@ons
skills.
Storytelling
will
be
on
of
the
hot
new
job
in
US
data
analy@cs
and
visualiza@on
market.
• Shortage
of
skilled
staff
will
persist.
In
the
U.S.
alone
there
will
be
181,000
deep
analy@cs
roles
in
2018
and
5x
that
many
posi@ons
requiring
related
skills
in
data
management
and
interpreta@on.
-‐
IDG
Source:
GE
Industrial
Internet,
Nov
2014,
McKinsey
Global
Ins@tute,
June
2011
39
40. Data
Driven
Decision
Making
• Even
if
firms
that
adopt
data
driven
decision
making
can
reap
gains
of
5-‐6
percent
higher
produc@vity
compared
with
firms
that
dosen’t
according
to
General
Electrics,
organiza@onal
leaders
ogen
lack
the
understanding
of
the
value
in
big
data
as
well
as
how
to
unlock
it.
In
compe@@ve
sectors
this
may
prove
to
be
an
Achilles
heel
for
some
companies
since
their
established
compe@tors
as
well
as
new
entrants
are
likely
to
leverage
big
data
to
compete
against
them.
Source:
GE
Industrial
Internet,
Nov
2012,
McKinsey
Global
Ins@tute,
June
2011
• Many
organiza@ons
do
not
have
the
talent
in
place
to
derive
insights
from
big
data.
In
addi@on,
many
organiza@ons
today
do
not
structure
workflows
and
incen@ves
in
ways
that
op@mize
the
use
of
big
data
to
make
beler
decisions
and
take
more
informed
ac@on.
40
42. Roles of BCB and BCTDatabase
Management
Systems
● Access
(Jet,
MSDE)
(Microsog)
● DB2
Everyplace
(IBM)
● NonStop
SQL
(Tandem)
● Oracle
8I
(Oracle)
● PointBase
Network
Server
(PointBase)
● PostgreSQL
(Freeware)
● Db.linux
(Centura
Sogware)
Source:
Company
websites
and
industry
expert
interviews,
Feb
2015
Increased
Compe@@on
in
the
Market
Escalation ProcessAnaly@cs
Vendors
● Cloudera
‘Data
Hub’
(Open
source
Hadoop)
● Databricks
(Up
and
coming
player)
● Ac@an
Matrix
(aesthe@cally
pleasing
data
poryolios)
● Amazon
Webservice
(Hosts
a
list
of
DBMS
from
third
party
players)
● Algoritmica
(Big
Data
Algorithms
for
Companies)
42
45. Market
Accessibility
for
Finnish
Companies
• According
to
several
respondents
in
conducted
interviews,
Finnish
companies
have
a
good
reputa8on
on
the
American
Market.
The
companies
are
especially
seen
as
skilled
when
it
comes
to
design,
engineering,
math
and
games
related
areas.
Given
McKinsey’s
es@mated
future
shortage
of
skilled
analysts
and
managers
that
can
make
data
driven
decisions,
there
might
be
poten@al
for
Finnish
companies
to
establish
themselves
as
global
thought
leaders
in
this
field
going
forward.
• Two
areas
that
needs
special
alen@on
by
Finnish
companies
entering
the
US
Industrial
Internet
and
data
analy@cs/visualiza@on
market
has
been
brought
up
during
our
study:
1. Marke8ng
approach
–
The
US
and
the
Finnish
communica@on
and
marke@ng
style
differs
a
lot,
which
is
something
to
be
aware
of
when
entering
the
market.
2. Legal
issues
–
The
US
has
a
much
more
“law
suit
prone”
culture
than
Finland.
It’s
important
to
remember
to
prepare
legal
documenta@on
related
to
whom
is
responsible
if
decisions
made
on
data
generated
by
the
Finnish
companies
have
nega@ve
outcome
etc.
Neglect
to
do
so
may
end
up
in
costly
legal
balles.
45
46. Key
Opportuni@es
for
Finnish
Companies
1. Data
analy@cs
&
visualiza@on,
both
tools
and
services
2. Build
and
host
data
centers,
u@lizing
the
technology
credibility
and
the
cold
weather
condi@ons
3. Support
exis@ng
machine
parks
with
retrofibng
and
upgrade
to
new
standards
4. Provide
data
talent
and
consultant
support,
as
well
as
execu@ve
educa@on
regarding
big
data
analy@cs
and
visualiza@on
5. Supply
the
market
with
various
security
solu@ons
focused
on
Internet
of
Things
and
Industrial
Internet
46
48. Risks
with
Industrial
Internet
Adding
func8onality
that
customers
don’t
want
to
pay
for
• Just
because
a
feature
is
now
possible
does
not
mean
there
is
a
clear
value
proposi@on
for
the
customer.
Adding
enhanced
capabili@es
and
op@ons
can
reach
the
point
of
diminishing
returns,
due
to
the
cost
and
complexity
of
use.
Underes8ma8ng
security
and
privacy
risks
• Smart,
connected
products
open
major
new
gateways
to
corporate
systems
and
data,
requiring
stepped-‐up
network
security,
device
and
sensor
security,
and
informa@on
encryp@on.
Failing
to
an@cipate
new
compe@@ve
threats.
Wai8ng
too
long
to
get
started
• Moving
slowly
enables
compe@tors
and
new
entrants
to
gain
a
foothold,
begin
capturing
and
analyzing
data,
and
start
moving
up
the
learning
curve.
Overes8ma8ng
internal
capabili8es
• The
shig
to
smart,
connected
products
will
demand
new
technologies,
skills,
and
processes
throughout
the
value
chain
(for
example,
big
data
analy@cs,
systems
engineering,
and
sogware
applica@on
development).
A
realis@c
assessment
about
which
capabili@es
should
be
developed
in-‐house
and
which
should
be
developed
by
new
partners
is
crucial.
Source:
HBR,
The
Internet
of
Everything,
Nov
2014,
Subject
maler
expert
interview,
Feb
2015
48
49. Cross
Industry
Coopera@on
Challenges
Need
to
manage
challenges
regarding
cross
industry
coopera8on
• Even
if
there
is
a
lot
of
poten@al
from
a
technical
and
financial
perspec@ve
in
connec@ng
machines
and
u@lizing
the
power
of
the
industrial
internet,
there
is
a
lot
of
business
and
organiza@onal
issues
that
needs
to
be
addressed
in
order
to
unlock
its
full
poten@al.
• If
you
take
the
airplane
industry
as
an
example,
there
are
several
different
companies
that
needs
to
cooperate
in
order
to
generate
a
complete
data
picture
of
a
situa@on.
American
Airlines
would
be
in
charge
of
the
over
all
opera@ons,
Boing
would
have
sensors
mounted
through
out
the
aircrag,
and
Rolls
Royce
would
measures
the
performance
of
the
aircrag
engines
that
they
provide
on
a
product
as
a
service
basis.
• Ques@ons
that
arise
in
this
and
similar
cases
are:
Who
is
in
charge
of
the
sensors
and
the
data
that
is
collected?
Who
owns
the
data?
What
are
the
incen@ves
for
various
companies
to
share
the
date?
What
does
the
business
models
look
like?
How
do
you
address
security
issues
across
various
companies?
What
legal
and
contractual
issues
will
arise?
What
industry
standards
needs
to
be
in
place
for
various
companies
equipment
to
be
able
to
transfer
or
provide
relevant
data?
49
50. Internal
Structures
and
IT
Investments
Underes8ma8ng
the
challenges
with
Internal
coopera8on
• Even
within
a
single
large
corpora@on
the
increased
use
of
sensors
and
big
data
for
decision
making
could
be
challenging.
How
should
R&D,
Product
management
and
Sales
act
and
cooperate
in
regards
to
new
data
about
customer
preferences?
Will
there
be
strong
support
of
internal
knowledge
sharing
and
coopera@on
between
organiza@onal
silos?
Who’s
budgets
will
be
affected
by
the
new
data
driven
ways
of
working?
Is
there
enough
skilled
personnel
to
analyze
and
make
relevant
decisions
based
on
the
collected
data?
Will
the
new
data
based
findings
effect
internal
power
posi@ons
with
historical
power?
Timing
of
capital
investments
• In
order
to
get
the
industrial
Internet
to
work,
the
industry
faces
massive
IT
investments
in
new
data
systems
and
upgrades
of
exis@ng
machine
parks.
The
market
agrees
that
there
is
a
lot
of
poten@al
to
be
won
by
connec@ng
the
infrastructure
and
start
working
in
a
more
data
driven
world.
The
ques@on
is
how
fast
this
transi@on
will
go
since
there
are
major
investment
decisions
on
the
table
that
needs
to
be
executed
through
out
the
industry
before
the
industrial
internet
can
reach
its
full
poten@al
on
a
global
level.
50
51. Opportuni@es
in
Industrial
Internet
Products
as
a
service
poten8al
(PaaS)
• There
is
a
lot
of
value
for
industrial
product
companies
to
capture
if
the
can
fully
u@lize
the
poten@al
of
the
industrial
internet
movement.
If
they
offer
their
solu@ons
as
a
Product
as
a
Service
(such
as
airplane
engines
and
industrial
drills
etc.)
they
are
in
a
good
posi@on
to
keep
the
increased
margins
rendered
by
decreased
energy
costs
or
improved
logis@cs
etc.
Retrofikng
and
upgrading
old
machine
parks
• In
order
to
be
able
to
generate
data
from
sensors
and
u@lize
the
industrial
internet
revolu@on
a
lot
of
capital
intense
machine
parks
will
need
to
be
upgraded
in
the
coming
years.
Companies
that
can
provide
sogware
and
solu@ons
that
updates
exis@ng
and
func@oning
equipment
without
replacing
it
has
a
lot
of
poten@al.
One
example
of
this
is
the
Medical
Health
Startup
Trice
imaging
that
provides
solu@ons
that
enables
old
ultrasound
machines
to
be
connected
to
the
internet
without
modifying
the
exis@ng
hardware.
Double
mone8za8on
of
big
data
• Besides
using
the
generated
data
to
op@mize
their
own
performance,
companies
with
mission
cri@cal
infrastructure
as
described
earlier
might
be
able
to
sell
sensor
generated
data
to
external
par@es
that
can
benefit
from
knowledge
about
the
performance
of
their
equipment.
As
an
example
the
performance
of
various
industry
components
can
be
relevant
for
the
component
manufacturer,
and
data
regarding
driving
habits
for
various
car
models
could
be
relevant
for
insurance
companies.
51
52. Sources
&
Interview
Respondents
Reports
and
presenta8ons:
• Harvard
Business
Review,
The
Internet
of
Everything,
Nov
2014
• McKinsey
Global
Ins@tute:
Big
data:
The
next
fron@er
for
innova@on,
compe@@on,
and
produc@vity,
June
2011
• Industrial
Internet:
Pushing
the
Boundaries
of
Minds
and
Machines,
GE,
Nov
2012
• BIG
DATA:
SEIZING
OPPORTUNITIES,
PRESERVING
VALUES,
Execu@ve
office
of
the
President,
May
2014
• The
Internet
of
Things
(IOT)
&
The
Internet
of
Everything
(IOE),
Christopher
Cressy,
Cisco,
Feb
2015
Ar8cle
links:
• hlp://www.forbes.com/sites/gilpress/2014/12/11/6-‐predic@ons-‐for-‐the-‐125-‐billion-‐big-‐data-‐analy@cs-‐market-‐in-‐2015/2/
• hlp://wikibon.org/wiki/v/The_Industrial_Internet_and_Big_Data_Analy@cs:_Opportuni@es_and_Challenges,
Sept
2013
• hlp://postscapes.com/internet-‐of-‐things-‐market-‐size,
Feb
2015
• hlps://hbr.org/2014/11/how-‐smart-‐connected-‐products-‐are-‐transforming-‐compe@@on
• hlp://www.idc.com/prodserv/FourPillars/bigData/index.jsp
• hlp://wikibon.org/wiki/v/Big_Data_Vendor_Revenue_and_Market_Forecast_2013-‐2017
• hlp://www.inc.com/drew-‐hendricks/6-‐companies-‐using-‐big-‐data-‐to-‐change-‐business.html
• Corporate
websites
of
all
men@oned
companies
in
the
report,
via
Google
Interviews:
• Daniel
Langkilde,
Machine
Learning
Engineer,
Recorded
Future
&
Big
Data
researcher,
Berkley
University,
Feb
2015
• Visa
Friström,
Dir.
Business
Development,
Ericsson
USA,
San
Francisco,
Feb
2015
• Geffory
Noakes,
VP
Business
Development,
Symantec,
San
Francisco,
Feb
2015
• Ann
Dretzka,
Data
research
project
manager,
GAP,
San
Francisco,
Feb
2015
• Scol
Norman,
Partner,
Velorum
Capital,
San
Francisco,
Feb
2015
• Alexander
Miller,
Founder,
Desiler
Gravity,
San
Francisco,
Feb
2015
• Will
Cardwell,
Partner,
Courage
Ventures,
Barcelona,
March
2015
• John
Ellis,
CEO,
Ellis
&
Associates,
Barcelona,
March
2015
• Leo
Meyerovic,
Founder,
Graphistry
Inc.,
San
Francisco,
March
2015
52