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Market	
  validation study	
  	
  
Industrial	
  Internet	
  	
  
	
  
‘Making	
  most	
  out	
  of	
  gathered	
  data’	
  
San	
  Francisco,	
  Feb	
  13	
  2015	
  
1	
  
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	
  
INTRODUCTION	
  
	
  Background	
  &	
  Defini8on	
  
3	
  
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	
  
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	
  
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	
  
Defini@on	
  -­‐	
  Internet	
  of	
  Everything	
  
Source:	
  Cisco,	
  Feb	
  2015	
  
7	
  
Industrial	
  Internet	
  of	
  Things	
  	
  
Source:	
  Cisco,	
  Feb	
  2015	
  
8	
  
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	
  
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	
  
MARKET	
  STATUS	
  &	
  MARKET	
  SIZE	
  
	
  Business	
  opportunity	
  	
  
11	
  
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	
  
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	
  
 
“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	
  
Market	
  Status	
  Industrial	
  Internet	
  
Source:	
  Cisco,	
  Feb	
  2015	
  
15	
  
Market	
  Status	
  Industrial	
  Internet	
  
Source:	
  Accenture,	
  Feb	
  2015	
  
16	
  
Industrial	
  Internet:	
  The	
  Power	
  of	
  1	
  %	
  	
  
Source:	
  GE	
  Industrial	
  Internet,	
  Nov	
  2012	
  
17	
  
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	
  
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	
  
“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	
  
MARKET	
  OPPORTUNITY	
  &	
  
POTENTIAL	
  CUSTOMERS	
  
	
  Business	
  opportunity	
  	
  
21	
  
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	
  
Key	
  Sectors	
  in	
  Industrial	
  Internet	
  
Source:	
  Cisco,	
  Feb	
  2015	
  
23	
  
Key	
  Market	
  Sector	
  Opportunity	
  
Source:	
  McKinsey	
  
Global	
  Ins@tute,	
  
June	
  2011	
  
24	
  
Big	
  Data	
  levers	
  in	
  Manufacturing	
  
Source:	
  McKinsey	
  Global	
  Ins@tute,	
  June	
  2011	
  
25	
  
Market	
  Sector	
  Opportunity	
  
Source:	
  McKinsey	
  Global	
  Ins@tute,	
  June	
  2011	
  
26	
  
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	
  
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	
  
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	
  
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	
  
CUSTOMER	
  NEEDS	
  &	
  BUSINESS	
  
MODEL	
  
	
  Business	
  opportunity	
  	
  
31	
  
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	
  
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	
  
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	
  
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	
  
COMPETENCE	
  LEVEL	
  &	
  TALENT	
  
	
  Market	
  maturity	
  
36	
  
Talent	
  Gap	
  in	
  Industrial	
  Internet	
  
Source:	
  McKinsey	
  Global	
  Ins@tute,	
  June	
  2011	
  
37	
  
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	
  
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	
  
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	
  
COMPETITION	
  
Market	
  density	
  
41	
  
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	
  
43	
  
FINNISH	
  COMPANIES	
  	
  	
  	
  
	
  Market	
  accessibility	
  for	
  	
  
44	
  
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	
  
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	
  
ANALYSIS	
  &	
  RECOMMENDATION	
  
Conclusion	
  
47	
  
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	
  
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	
  
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	
  
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	
  
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	
  

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Industrial internet big data usa market study

  • 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  
  • 3. INTRODUCTION    Background  &  Defini8on   3  
  • 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  
  • 7. Defini@on  -­‐  Internet  of  Everything   Source:  Cisco,  Feb  2015   7  
  • 8. Industrial  Internet  of  Things     Source:  Cisco,  Feb  2015   8  
  • 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  
  • 11. MARKET  STATUS  &  MARKET  SIZE    Business  opportunity     11  
  • 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  
  • 15. Market  Status  Industrial  Internet   Source:  Cisco,  Feb  2015   15  
  • 16. Market  Status  Industrial  Internet   Source:  Accenture,  Feb  2015   16  
  • 17. Industrial  Internet:  The  Power  of  1  %     Source:  GE  Industrial  Internet,  Nov  2012   17  
  • 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  
  • 21. MARKET  OPPORTUNITY  &   POTENTIAL  CUSTOMERS    Business  opportunity     21  
  • 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  
  • 26. Market  Sector  Opportunity   Source:  McKinsey  Global  Ins@tute,  June  2011   26  
  • 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  
  • 31. CUSTOMER  NEEDS  &  BUSINESS   MODEL    Business  opportunity     31  
  • 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  
  • 36. COMPETENCE  LEVEL  &  TALENT    Market  maturity   36  
  • 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  
  • 43. 43  
  • 44. FINNISH  COMPANIES          Market  accessibility  for     44  
  • 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  
  • 47. ANALYSIS  &  RECOMMENDATION   Conclusion   47  
  • 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