Market	
  validation study	
  	
  
Industrial	
  Internet	
  	
  
	
  
‘Making	
  most	
  out	
  of	
  gathered	
  data’	
...
Execu@ve	
  Summary	
  
The	
  defini@on	
  of	
  Industrial	
  Internet,	
  as	
  well	
  as	
  the	
  market	
  size	
  v...
INTRODUCTION	
  
	
  Background	
  &	
  Defini8on	
  
3	
  
Defini@on	
  -­‐	
  Industrial	
  Internet	
  	
  
The	
  industrial	
  internet	
  refers	
  to	
  the	
  integra@on	
  of...
Defini@on	
  -­‐	
  Internet	
  of	
  Things	
  
The	
  Internet	
  of	
  Things	
  is	
  a	
  term	
  used	
  to	
  descri...
Defini@on	
  -­‐	
  Big	
  Data	
  
	
  
Big	
  data	
  typically	
  refers	
  to	
  datasets	
  whose	
  size	
  is	
  
be...
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	
  ...
The	
  focus	
  of	
  the	
  market	
  study	
  
●  Applica@on	
  of	
  new	
  found	
  knowledge	
  
●  Product	
  of	
  ...
MARKET	
  STATUS	
  &	
  MARKET	
  SIZE	
  
	
  Business	
  opportunity	
  	
  
11	
  
Current	
  Market	
  Size	
  in	
  the	
  U.S.	
  	
  
€57.3BN	
  €23.1BN	
  €15.6BN	
  
Industrial	
  Internet	
  Market	...
Market	
  Status	
  Industrial	
  Internet	
  
Source:	
  hlp://postscapes.com/internet-­‐of-­‐things-­‐market-­‐size,	
  ...
 
“Between	
  2013	
  and	
  2022,	
  $14.4	
  trillion	
  of	
  value	
  (net	
  profit)	
  will	
  be	
  “up	
  for	
  
g...
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	
 ...
Big	
  Data	
  Market	
  Size	
  and	
  Status	
  Big	
  Data	
  Compound	
  Annual	
  Growth	
  Rate	
  
(CAGR)	
  Predic...
Big	
  Data	
  Market	
  Size	
  and	
  Status	
  
•  “Not	
  all	
  Big	
  Data	
  is	
  created	
  equal.	
  Data	
  ass...
“Buying and selling data
will become the new
business bread and butter.”
-Forbes, 2014
“ 2015 will mark an inflection poin...
MARKET	
  OPPORTUNITY	
  &	
  
POTENTIAL	
  CUSTOMERS	
  
	
  Business	
  opportunity	
  	
  
21	
  
Key	
  Poten@al	
  Target	
  Customers	
  
	
  
Industry	
  companies	
  with	
  mission	
  cri8cal	
  infrastructure	
  w...
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	
  fle...
Market	
  Sector	
  Opportunity	
  
•  Case:	
  Healthcare	
  
–  Data	
  generated	
  by	
  high-­‐value	
  assets	
  suc...
Market	
  Sector	
  Opportunity	
  
•  Case:	
  Energy	
  &	
  Natural	
  Resources	
  
–  By	
  analyzing	
  data	
  crea...
Market	
  Player	
  Overview	
  
The	
  need	
  of	
  Big	
  Data	
  input	
  and	
  output	
  provides	
  massive	
  capi...
CUSTOMER	
  NEEDS	
  &	
  BUSINESS	
  
MODEL	
  
	
  Business	
  opportunity	
  	
  
31	
  
Trends	
  in	
  Data	
  Analy@cs	
  &	
  Visualiza@on	
  
From data collection to data visualization – Numbers and basic d...
4	
  Business	
  Models	
  Examples	
  	
  
1.  Tableau:	
  Recurring	
  high	
  end	
  per	
  user	
  license	
  model.	
...
Service	
  Offerings	
  for	
  Big	
  Data	
  Clients	
  
	
  
	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  ...
Redefining	
  Industry	
  Boundaries	
  
The	
  increasing	
  capabili@es	
  of	
  smart,	
  connected	
  products	
  not	
...
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	...
Skills	
  and	
  Knowledge	
  
	
  
•  Automated	
  decision-­‐making	
  will	
  come	
  of	
  age	
  in	
  2015	
  and	
 ...
Data	
  Driven	
  Decision	
  Making	
  
•  Even	
  if	
  firms	
  that	
  adopt	
  data	
  driven	
  decision	
  making	
 ...
COMPETITION	
  
Market	
  density	
  
41	
  
Roles of BCB and BCTDatabase	
  Management	
  Systems	
  
●  Access	
  (Jet,	
  MSDE)	
  (Microsog)	
  
●  DB2	
  Everypla...
43	
  
FINNISH	
  COMPANIES	
  	
  	
  	
  
	
  Market	
  accessibility	
  for	
  	
  
44	
  
Market	
  Accessibility	
  for	
  Finnish	
  Companies	
  	
  
•  According	
  to	
  several	
  respondents	
  in	
  condu...
Key	
  Opportuni@es	
  for	
  Finnish	
  Companies	
  
1.  Data	
  analy@cs	
  &	
  visualiza@on,	
  both	
  tools	
  and	...
ANALYSIS	
  &	
  RECOMMENDATION	
  
Conclusion	
  
47	
  
Risks	
  with	
  Industrial	
  Internet	
  
Adding	
  func8onality	
  that	
  customers	
  don’t	
  want	
  to	
  pay	
  f...
Cross	
  Industry	
  Coopera@on	
  Challenges	
  	
  
Need	
  to	
  manage	
  challenges	
  regarding	
  cross	
  industry...
Internal	
  Structures	
  and	
  IT	
  Investments	
  
Underes8ma8ng	
  the	
  challenges	
  with	
  Internal	
  coopera8o...
Opportuni@es	
  in	
  Industrial	
  Internet	
  
Products	
  as	
  a	
  service	
  poten8al	
  (PaaS)	
  
•  There	
  is	
...
Sources	
  &	
  Interview	
  Respondents	
  
Reports	
  and	
  presenta8ons:	
  	
  
•  Harvard	
  Business	
  Review,	
  ...
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Industrial internet big data usa market study

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Industrial internet big data usa market study

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Industrial internet big data usa market study

  1. 1. Market  validation study     Industrial  Internet       ‘Making  most  out  of  gathered  data’   San  Francisco,  Feb  13  2015   1  
  2. 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. 3. INTRODUCTION    Background  &  Defini8on   3  
  4. 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. 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. 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. 7. Defini@on  -­‐  Internet  of  Everything   Source:  Cisco,  Feb  2015   7  
  8. 8. Industrial  Internet  of  Things     Source:  Cisco,  Feb  2015   8  
  9. 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. 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. 11. MARKET  STATUS  &  MARKET  SIZE    Business  opportunity     11  
  12. 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. 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. 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. 15. Market  Status  Industrial  Internet   Source:  Cisco,  Feb  2015   15  
  16. 16. Market  Status  Industrial  Internet   Source:  Accenture,  Feb  2015   16  
  17. 17. Industrial  Internet:  The  Power  of  1  %     Source:  GE  Industrial  Internet,  Nov  2012   17  
  18. 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. 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. 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. 21. MARKET  OPPORTUNITY  &   POTENTIAL  CUSTOMERS    Business  opportunity     21  
  22. 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. 23. Key  Sectors  in  Industrial  Internet   Source:  Cisco,  Feb  2015   23  
  24. 24. Key  Market  Sector  Opportunity   Source:  McKinsey   Global  Ins@tute,   June  2011   24  
  25. 25. Big  Data  levers  in  Manufacturing   Source:  McKinsey  Global  Ins@tute,  June  2011   25  
  26. 26. Market  Sector  Opportunity   Source:  McKinsey  Global  Ins@tute,  June  2011   26  
  27. 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. 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. 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. 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. 31. CUSTOMER  NEEDS  &  BUSINESS   MODEL    Business  opportunity     31  
  32. 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. 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. 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. 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. 36. COMPETENCE  LEVEL  &  TALENT    Market  maturity   36  
  37. 37. Talent  Gap  in  Industrial  Internet   Source:  McKinsey  Global  Ins@tute,  June  2011   37  
  38. 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. 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. 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  
  41. 41. COMPETITION   Market  density   41  
  42. 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. 43  
  44. 44. FINNISH  COMPANIES          Market  accessibility  for     44  
  45. 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. 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. 47. ANALYSIS  &  RECOMMENDATION   Conclusion   47  
  48. 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. 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. 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. 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. 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  

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