industrial internet big data usa market study

Market validation study Industrial Internet

‘Making most out of gathered data’

San Francisco, Feb 13 2015

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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.

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INTRODUCTION Background & Defini8on

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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

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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

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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

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Defini@on -‐ Internet of Everything

Source: Cisco, Feb 2015 7

Industrial Internet of Things


Key Elements of the Industrial Internet

Source: GE Industrial Internet, Nov 2012

IntelligentMachines

Connect the world’s machines, facilities, fleets and networks with advanced sensors, controls and software applications

AdvancedAnalytics

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

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2

3

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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

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MARKET STATUS & MARKET SIZE Business opportunity

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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

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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

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“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


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.

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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 Da

ta Com

poun

d An

nual Growth Rate

(CAG

R) Predic8on

s

“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…”

The BNET External Voice program is suggested to initially focus on spokespersons and include Presenters and Evangelists at a later stage

The BNET External Voice program is suggested to initially focus on spokespersons and include Presenters and Evangelists at a later stage

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

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MARKET OPPORTUNITY & POTENTIAL CUSTOMERS

Business opportunity

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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


Key Market Sector Opportunity

Source: McKinsey Global Ins@tute, June 2011 24

Big Data levers in Manufacturing


Market Sector Opportunity


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


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.

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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


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

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CUSTOMER NEEDS & BUSINESS MODEL

Business opportunity

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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

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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

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Talent Gap in Industrial Internet


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.

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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.

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COMPETITION Market density

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Roles of BCB and BCT Database 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 Process Analy@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)

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FINNISH COMPANIES Market accessibility for

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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.

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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

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ANALYSIS & RECOMMENDATION Conclusion

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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?

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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.

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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.

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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

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