Many organizations, including the World Economic Forum, describe the IIoT as being the fourth generation of the Industrial Revolution. The four generations have shared a common business goal such as running businesses more efficiently and producing goods and services more cheaply for stakeholders and consumers at large. They owe their existence to new capabilities created by inventions and advances in technology. In each generation, old manual jobs disappear, but new jobs and job types are created that operate at higher efficiency levels.

Today, pessimists point to the fact that many jobs will disappear during the age of the Industrial Internet. Optimists believe that because many new job types will be created, new jobs (albeit with different skills) will also be created. Time will tell if individuals whose jobs are displaced will be able to move into these new jobs, but many now feel that another revolutionary change is occurring. The future of work in the age of Industrial Internet is becoming a critical topic and connects it to the societal aspects of these innovative solutions. The term Internet of People (IoP) is sometimes used to remind us that people consume the benefits from the information that is extracted from data generated by people and/or the machines.

Most agree that the first generation of the Industrial Revolution began in the middle of the 18^th century. Let's go back in history to see how it led to the evolution of the IIoT today. The 18^th and 19^th centuries, which experienced the Industrial Revolution saw a transition from the manually intensive manufacturing processes to the mechanization of the manufacturing. This laid the foundation of the modern heavy industries. At the time, most people lived on farms and worked in agriculture. Factories were commonly located close to rivers and streams where they could be powered by water wheels, and there was usually much handwork involved. With the invention of the steam engine, factories could be located elsewhere. The power that was supplied to machinery by steam engines became more predictable, and more processes could be aided by machinery.

Great Britain experienced many technological innovations ranging from the first engine in 1712 by Thomas Newcomen to the steam engine in 1765 by James Watt to the first public railway line in 1825. The Industrial Revolution transformed manufacturing from the home and cottage industry level to a vastly more scalable level. With the introduction of railroads as a transportation alternative to river traffic and horse-driven carriages, faster travel between distant locations was enabled and provided a new means to deliver supplies to factories and products from them. This theme of decoupling the production facilities from the consumers can be seen in today's computing world where remote data centers can be decoupled from the information technology users. Over time, this Industrial Revolution led to a transition from human labor to the use of machines, spread over whole of Europe and to North America, leading to the industrialization of the world. Gradually, this led to consumerism as goods became available, accessible, and affordable.

The increased widespread availability of electricity through power grids and the invention of the assembly line in the first decades of the 1900s introduced the second generation of the Industrial Revolution. Once again, power became more predictable and the amount of space required for power generation in factories was reduced. Production became more optimized through assembly lines, and workers assumed new specialized roles. Motorized vehicles also appeared for the delivery of supplies and transporting finished products, thus enabling more variation in factory locations. We began an age of mass production as well as mass merchandising, which resulted in the creation of many additional, new kinds of job.

In the third generation, business computing was introduced and efficiencies were greatly improved. Mainframe computers became widely available with subsequent pricing adjustments, making them more affordable and hence more widely adopted in the 1960s. Still cheaper minicomputers and then personal computers followed. The Internet was in common usage for networking within companies and across the world by the 1990s.

The Internet evolved from a way for the military to connect and communicate and appeared in universities and then mainstream companies. The mid 1990s saw the transition from the military's Advanced Research Projects Agency Network (ARPANet) to the consumer Internet. In this wave, computers and servers connected across the world and then provided an information super highway for the people. This revolutionized how people interacted with each other and with the businesses leading to the growth of e-commerce and social media. New leaders emerged in this era, starting with Information Technology (IT) system providers, and companies such as Amazon, which started with online sale of books and went onto become a general-purpose e-commerce platform. Likewise, on the human interaction front, emails became mainstream and more interactive and rich multi-media evolved on the web. This led to the rise of Myspace, Facebook, Twitter, and similar social media platforms to essentially connect the people across the world. We refer to this as the consumer Internet.

Computing also became more accessible through improved software development tools and through business applications and tools that provided increasingly more intuitive user interfaces. Some refer to this as the beginning of the information age as line of business users could access and manipulate their data to measure and optimize their business activities.

While the consumer Internet focused on connectivity between businesses, consumers, the IT systems, and the computing devices such as servers, PC's, laptops, and emerging mobile devices, it largely ignored the machines from the Industrial Revolution. This led to a great divide between the machines for industrial operations and the traditional IT systems and set the stage for the fourth wave that we call the Industrial Internet Revolution.

The Industrial Internet can be defined as the connecting of industrial-grade machines and devices to networked computing devices with the goal of collecting the diverse data originating both inside the machine and the surrounding environment and processing or analyzing this data for meaningful outcomes. Such data originates in various forms and is often referred to as big data. The systematic organization and analysis of this data is referred to as big data analytics for industrial outcomes.

The Industrial Internet and IIoT are the industrial flavor of the IoT. While IoT refers to any physical object or thing connected to a network and the Internet, IIoT focuses on scenarios where the connected objects are primarily industrial in nature (such as manufacturing assembly lines, power generation equipment, or mass transportation vehicles). Thus, Industrial Internet is often used interchangeably with IIoT. There are three Ps important to an industry:

• Products (machines and assets)
• Processes (assembly lines and supply chain)
• People (human stakeholders)

The following illustration captures the interaction of these three Ps:

Industrial machines and assets have a long life, especially when compared to many consumer devices. The following table serves as a reference to highlight the difference of scale in usable life comparing various industrial assets to a smartphone:

Due to the long life and cost of ownership of industrial machines, it is important to provide ways to protect the investment in these machines over time. Thus, the optimization of field maintenance services is an integral part of the Industrial Internet. Service execution and service delivery platforms and applications are within the realm of the Industrial Internet architects, and this book will provide coverage to it.

The long life of industrial assets leads to two terms often used in the context of Industrial Internet solutions: greenfield and brownfield applications. A greenfield project refers to a scenario where a company decides to build a new infrastructure since it offers the maximum design flexibility and efficiency to meet a project's needs (an existing infrastructure limits the ability to change by its present design). From the Industrial Internet architecture perspective, the new infrastructure can add sensors to collect relevant data.

Brownfield projects leverage infrastructure that is already in use. The costs of starting up are usually greatly reduced with this approach, but it can be more difficult to modernize the infrastructure and incorporate the addition of sensors. Construction and commissioning times can be minimized using this approach. For Industrial Internet projects, brownfield systems can be retrofitted by adding external sensors to collect data. For example, external acoustic sensors might be added to the body of air compressors in a factory to do the harmonic analysis and determine air leaks in a brownfield project. Air leaks can cause wasted electricity in manufacturing plants where compressors are used to drive several pneumatic tools.

Some of the concepts we associate with the Industrial Internet today began to mature in the last few years. For example, in a world before widely available smart sensors, oil and gas exploration companies brought computers to the exploration sites, processed the data locally in relational databases, and transmitted the processed data and their conclusions back to their headquarters. Some referred to this as early edge computing on the remote computers. The following diagram reflects this type of deployment:

Data warehouses and data marts became common in most businesses. Batch-fed by Online Transaction Processing (OLTP) systems, they became the place to store historical data used to report on current trends and compare current data with past data through business intelligence tools. Of course, this footprint remains common today.

Predictive algorithms were also developed, tested, and deployed with increasing rapidity in certain industries and gained wider adoption over time. Some early use cases included understanding financial market investment strategies and insurance risk, and the prediction of the likely quality of expensive manufacturing processes to better optimize the production.

Each generation became shorter. Moving from the first generation of the Industrial Revolution to the next was a matter of centuries, but the subsequent generations took half the time of the previous change. This implies that future generations may come at a faster pace, and while we are embracing the Industrial Internet, we need to be prepared for the possible next generations as well.

In 2010, the IoT and Industrial Internet became familiar terminology. The World Economic Forum and others declared this to be the next generation of the Industrial Revolution. As in previous generations, several technological advancements came together to enable a new class of solutions and applications, changing business models and capabilities.

Sensors began to be mass produced at ever decreasing costs. As price points, size, weight, and power requirements for sensors decreased, engineers began to create device designs that included them in anticipation of being able to gather useful data on device status as soon as it became feasible. Since smart sensors can also be programmed and updated, they can evolve and become more "intelligent" over time. For example, inclusion of such smart sensors in automobiles led to rapid advancements in the development of autonomous vehicles.

The sensors themselves most often transmit semi-structured data in a streaming fashion. Coincidentally, analyzing mass quantities of semi-structured data became possible a decade earlier through development of NoSQL data engines (and Hadoop specifically) to solve the problems of Internet search optimizations and recommendations. Next generation platforms holding exabytes of data are deployed today by companies in the search engine business.

The development of new and innovative software solutions became more viable for start-ups and smaller organizations as cloud-based platforms became available (mostly eliminating an expensive upfront investment in infrastructure). The cloud also enabled faster time to deployment and elastic scalability that was difficult in classic data centers.

The cost of networking and bandwidth reduced over this time to provide ubiquitous connectivity for the IIoT. Some of the connectivity options and technologies used include Radio-Frequency Identification (RFID), Wi-Fi, Bluetooth Low Energy (BLE), and 2G/3G/4G with 5G on the horizon.

The growing popularity of open source software data management offerings and development tools also helped minimize early costs. Today, as the Industrial Internet has matured, we see many integrated solution footprints and applications that rely on underlying open source components.

The following diagram represents a common architectural pattern often seen in Industrial Internet implementations and is called a Lambda architecture:

The illustration shows streaming data feeds from smart devices. The streaming analytics engine analyzes this feed in real time and will sometimes have machine learning algorithms deployed to process the data. The data lake pictured is most often a Hadoop cluster and is designed to load and store massive amounts of data of all types. As in the previous generation, traditional data warehouses and data marts are batch fed. Business intelligence tools are shown pointed at the data mart, data lake, and streaming analytics engine in our illustration.

We'll describe these components in much more detail in subsequent chapters as we lay out the data and analytics architecture. Obviously, there is also a lot more detail in the information technology platform architecture, which we'll cover as well.

New manufacturing technologies are also now employed in Industrial Internet solutions. Robotics in manufacturing became common in industries where the cost of labor was high, such as in the automotive industry, around the turn of this century. The robotics that were deployed improved the consistency and quality of the products produced and helped to contain costs. The addition of intelligent or smart sensors to newer generations of these devices enabled more functional and flexible capabilities. The wider applicability and growing usage of robotics also led to decreases in their pricing, helping drive further adoption.

Many manufacturers and companies that design products are now experimenting with 3D printing. 3D printers enable the manufacturing of products and components anywhere; such a printer is deployed and accessible via a network. Such technologies are often referred to as additive manufacturing. The ability to print spare parts on demand for industrial machines can have a profound positive impact on the supply chain ecosystem, as the cost of such additive manufacturing continues to decrease.

Artificial intelligence (AI) and machine learning are also enabling more intelligent devices. As devices become self-learning, they can react to changing situations in real time. We'll discuss these topics and other emerging technologies when we explore what is likely to occur in the near and more distant future in the last chapter of this book.

These new capabilities are causing companies to rethink the value of their data and the kinds of businesses they are competing in. Many are facing new and non-traditional competition from other industries and are evaluating digital transformation strategies that sometimes include new strategies for monetizing their data assets. Some are becoming data aggregators, selling data to other companies and subscribers that find it useful.

The following diagram summarizes the four generations of the Industrial Revolution we described:

As always seems to happen when a new generation begins, there are some holdover problems from the old generation as well as problems introduced by the new architecture. One carryover from the previous generation is the need for projects to be driven by line of business requirements, not by IT. As it was earlier, projects will usually stall when IT-initiated proof of concepts do not really solve problems that the business needs and wants to address.

In Industrial Internet projects, architects and IT must also sometimes work with engineering designers who are specifying the types and locations of sensors in devices to assure that data needed for the proposed solution can be gathered. Similarly, these teams need to work together regarding networking requirements given the amount of data that might be transmitted. Continuous data gathering from equipment operated in industrial settings is key to enabling maintenance and field services-related solutions.

The mixture of semi-structured and unstructured data and the variety of data management solutions needed introduce complexity and the need for new skill sets that an organization might not possess and face difficulty in finding. Further adding to the complexity is the rate at which data is transferred over networks arriving in the data management engines and the data volumes that must be managed in them.

Of course, device and data security must be maintained throughout the ecosystem. Software and firmware updates that are pushed to intelligent sensors and devices must be secure and successful, or denied. Data transmitted to cloud-based solutions must meet or exceed industry-relevant certifications and country data sovereignty and privacy laws.

External threats can exploit vulnerabilities in under-protected Industrial Internet systems and thereby cause harm to the organization owning the assets and the associated business processes. Such concerns led to an increased focus on solving these security risks and adopting the emerging standards.

If your company or organization is like many, it defined many roles and job titles for its architects. Most often, the roles we will describe in this section reside in the IT organization. That said, linking these projects to business needs and requirements is critical as we previously noted. We'll describe the process to do that later in this book.

Many look to The Open Group Architecture Framework (TOGAF) as a place to begin to define the skills an architect must possess. TOGAF describes characteristics needed to define a business architecture, application architecture, data architecture, and technical architecture. As cloud-based computing has gained popularity, some of the architecture considerations and emphasis have changed a bit. Today, the following roles are the typically defined ones for each architecture type:

• Business architecture: This architecture includes the business strategy and goals, business processes, organization, and governance that are primarily driven by the lines of business and provides documentation for the business justification for projects
• Application architecture: This architecture maps the relationships between identified-needed business processes and the application footprints, the interactions among applications, and how the applications are to be deployed (such as defining cloud-based SaaS strategies)
• Data architecture: This architecture defines the appropriate logical and physical data structures aligned to business needs and the most appropriate data management platforms (choosing among relational databases, NoSQL databases, Hadoop, graph databases, and other options)
• Technology architecture: This architecture defines software, server, storage, and networking solutions (including cloud-based PaaS and IaaS strategies) in response to technical requirements

The TOGAF definitions became the basis for defining the role of the Enterprise Architect (EA) in many organizations and a certification process. An EA could become certified by demonstrating skills in each of these architecture areas. In truth, many of today's EAs have strong IT technology backgrounds because of their heritage but are weaker in other areas.

Because of the unbalanced skills often present in architects, many organizations designate specialists for each architecture area. So, they will have business architects, application architects, data architects, and technology, infrastructure, or cloud architects. An organization will sometimes also have a chief architect who serves as the lead strategist and participates in strategic planning across the different specialties.

The growing realization of the importance of secure data and data centers in always delivering a trusted and timely picture of true business state has caused many organizations to create the role of Chief Security Officer (CSO). Security architects or cloud architects with strong security backgrounds are sometimes part of the team. They bring skills in defining authorization, authentication, and encryption architectures, and a knowledge of secure networking designs and options. They also have knowledge of industry and country mandates, as well as security certification standards that must be adhered to.

In the crowded C-suite alphabet soup, a relatively new entrant is the CDO or Chief Digital Officer. CDO has also been used for Chief Data Officer. However, in the context of the Industrial Internet, the Chief Digital Officer often plays a pivotal role. A CDO is the leader who helps a private company or a public organization drive digital transformation initiatives to achieve well-defined outcomes.

Digital transformation can be defined as the change associated with the conversion from traditional and often analog business technologies to digital ones using one or more of the modern computing paradigms involving data, analytics, mobility, social media, or cloud computing. A simple example of the digital transformation of business in the public sector setting is the use of automated toll machines communicating with automobile transponders to process tolls on highways, thus eliminating coin-operated or human-operated tool booths. The transponder is a good example of a thing.

CDOs are appearing in more and more companies. Examples include leaders of IIoT projects and initiatives at General Electric (William Ruh) and ABB (Guido Jouret). CDOs will sometimes have the title of Vice President - Digital. Regardless of the exact title, the person in the CDO role is often closer to the business operations than the traditional Chief Information Officer (CIO). Such an individual can have a natural promotion progression to President of an operational division or CEO.

CDOs usually have a strong architecture background. In fact, a career path we have seen is evolution from one of the architect roles defined by TOGAF to chief architect and then CTO and finally CDO or Vice President of Digital. Thus, IIoT is introducing new career paths for architects.

The architects and similarly skilled individuals responsible for the Industrial Internet are increasingly becoming part of the CDO organization as opposed to the CIO organization. Such digital organizations are often tasked to help break the barriers between Operations Technology (OT) and IT. This convergence of IT and OT is key to the full realization of the value of the Industrial Internet. This idea of the convergence of IT and OT systems into IIoT systems in visually represented in the following illustration:

This implies that the organization needs to hire and develop skills based on these new demands. In some cases, companies are developing Digital Leadership Programs to groom professionals from the lines of businesses who are skilled in OT and pairing them with more traditional enterprise IT skills to accelerate the delivery of the Industrial Internet, inside and outside their organizations.

The traditional Systems Integrator (SI) and professional services companies are creating digital and IoT practices. They are creating reference architectures and building proof of concepts to showcase applications for Industrial Internet. As these organizations increase the number of Industrial Internet architects to implement these IIoT solutions, we will likely see the emergence of new kinds of training and certifications.

In this section, we will look at the need for an Industrial Internet-centric architectural approach to be successful in delivering the business outcomes. Just as civil engineers and building architects use blueprints to incorporate best practices in their work in a reusable way, reference architectures for IT systems have been extensively defined and used to prevent the reinvention of the wheel again and again.

Here, we will focus on reference architectures for IoT and more specifically on emerging reference architectures for the Industrial Internet and IIoT projects. To fully understand such reference architecture, a familiarity with system design principles, enterprise architecture, security frameworks, and networking architecture will be highly useful.

Reference architectures for the Industrial Internet can be very useful in facilitating the communication between the architects and the stakeholders in industrial manufacturing domains, including plant managers, field engineering managers, service professionals, business managers, and others. The solutions tend to address very specific business problems such as determining fuel efficiency and when engine maintenance is required. IT-centric architecture frameworks are less useful for understanding how the convergence of OT and IT will provide a means to achieve the business outcomes expected from the Industrial Internet solutions. However, there is a need for the reusability of this underlying IT architecture to scale the lessons that are being learned broadly.

Architects refer to the reference architecture and use it as a template as they capture the requirements. They design the specific implementation of the architecture and can convey a consistent understanding to internal and external stakeholders. Thus, interoperability, security, and other requirements are addressed upfront and do not become an afterthought.

Reference architectures lay the foundation for best practices and the reuse of the architectural patterns. As per the US Treasury Architecture Development Guidance (TADG) publication (http://pubs.opengroup.org/architecture/togaf8-doc/arch/chap28.html), the definition of a pattern is an idea that has been useful in one practical context and will probably be useful in others. The structure of the pattern can include some of the following elements:

• Name: Easy-to-remember nomenclature
• Problem statement: Description of the challenge to solve
• Context: The current state where the pattern could be applied
• Forces: The internal and external drivers and constraints; this could include the regulatory landscape as well as the societal implications
• Solution: The details of how to solve the problem at hand
• Resulting context: The outcomes and the trade-offs
• Examples: Sample applications
• Rationale: The why and the detailed explanations
• Related patterns: How this pattern is similar or related to others
• Known uses: Where this pattern is in use

Throughout this book, we will see the evolution of and use of architecture patterns in the context of the Industrial Internet. For example, there are different patterns for gateways and edge architecture. New cloud-based architecture patterns continue to be introduced.

The Industrial Internet Consortium (IIC) has recognized the need for the reference architecture and has published the Industrial Internet Reference Architecture (IIRA). This three-tier architecture provides different view points targeted at the different stakeholders. IIC defines the reference architecture as the output of the application of architecture principles to a class of systems. This is used to provide guidance as the architects analyze and solve the common architectural concerns. The resulting IIRA then provides a template for use in the concrete architecture of Industrial Internet systems.

IIoT projects and architecture solutions can be extremely complex. A proven approach to solving complex problem design is to decompose it into its subsystems. So, to further accelerate the adoption of the Industrial Internet and enable delivery of the desired business outcomes, similar analytics, security, and connectivity frameworks are provided by IIC.

Next, we will take a look at the tiers of the architecture and how they interact to produce the desired system behaviors. In subsequent chapters of this book, we will provide guidance on how to simplify the design and analysis of the subsystems and foster their reusability.

The most commonly used reference architectures for the Industrial Internet and IIoT have three-tiers: Edge tier, Platform tier, and Enterprise tier. The commonly used definitions of the three tiers are as follows:

• Edge tier: The Edge tier collects data from the deployed machines (the sources of data) using various connection types. The architectural concerns for the Edge tier can include the nature of sensors and the machines or devices where data is being collected from, their location, governance scope, and the type of network connection, as well as the storage, transmission, and the computing needs for the collected data.
• Platform tier: The Platform tier receives, processes, and forwards data and control commands from the Edge tier to the Enterprise tier and vice versa. It can provide structures for data ingestion, data stores, and asset metadata, and can store configuration data and provide non-domain-specific services such as data aggregation and analytics.
• Enterprise tier: The Enterprise tier can implement domain-specific applications, decision support, and business intelligence systems, and provide user interfaces to human consumers of the information.

Let's take a quick glance at the following diagram depicting the tiers mentioned earlier:

The providers for Industrial Internet platforms and solutions decide what functionality to provide and which components to configure in each of the tiers. General Electric often uses the terminology get connected, get insight, and get optimized, which requires all the three tiers to fully realize outcomes from Industrial Internet. A more detailed review of typical IIoT platform strategies and solutions will be covered in subsequent chapters.

Industrial accidents can cause devastating damage (as witnessed at Fukushima, Chernobyl, and Bhopal) with large-scale damage to the environment, injury, or the loss of human life. As we enable software-based systems to increasingly interact with operations of critical infrastructure, there is an increasing need for robust security frameworks for the Industrial Internet.

The IIC has defined an Industrial Internet Security Framework (IISF). The IISF is a collective work product of security experts from companies such as ABB, GE, Intel, RTI, as well as academicians from JHU and UPenn. It was reviewed by professionals from Oracle, Microsoft, and IBM to name a few. Such cross-company initiatives prove that security frameworks and best practices cannot be over-emphasized for Industrial Internet.

The purpose of IISF is to provide a point of view on the security-related architectures, designs, and technologies, and identify procedures relevant to trustworthy Industrial Internet systems. IISF describes the security characteristics, technologies, and techniques needed to address security concerns and gain the assurance that system trust worthiness is achieved.

Apart from the traditional concerns of hacking and theft of information, resiliency is a key concern for Industrial Internet systems. IIC defines resilience as the condition of the system that allows it to be able to avoid, absorb, and/or manage dynamic adversarial conditions while completing assigned mission(s), and to reconstitute operational capabilities after casualties.

For example, a smart thermostat controlling the HVAC system in a building could receive a command to raise the building's temperature by 50 degrees Fahrenheit in the next hour. It is known that the building was operating in a normal temperature range for the human occupants in the building. The resilience built into the system would prevent a sudden rise in temperature and would either create an alarm for this command or include a reliable human in the loop of the decision making before acting.

This kind of system is called a Cyber-Physical System (CPS). According to the National Science Foundation (NSF), CPSs are engineered systems that are built from, and depend upon, the seamless integration of computational algorithms and physical components (https://nsf.gov/funding/pgm_summ.jsp?pims_id=503286).

Future research and advances in CPS will enable capability, adaptability, scalability, resiliency, safety, security, and usability that can be used for the benefit of Industrial Internet systems. CPS technology will drive innovation and competition in the industrial sectors such as agriculture, energy, transportation, building design and automation, healthcare, and manufacturing.

Connectivity is at the heart of making IIoT projects functional. Interoperability among the tiers and to devices must be planned for.

IIC released the Industrial Internet Connectivity Framework (IICF) to deal with such architectural considerations; this is a result of contributions from professionals in several large companies, including Cisco, Ericsson, Nokia, RTI, GE, Samsung, AT&T, and SAP. The IICF links the different elements of the rich and diverse landscape of the Industrial Internet and defines an open connectivity reference architecture. IICF enables architects to evaluate and determine the suitability of a connectivity technology for their systems and solutions.

The IICF answers commonly asked by architects about connectivity in the different layers and their functions for Industrial Internet systems. It describes the architectural characteristics and design trade-offs at each layer, commonly available industry standards for these layers, and the categorization and evaluation of the relevant connectivity technology. As we dig deeper into the connectivity issues for Industrial Internet systems, in upcoming chapters, you will learn about connectivity frameworks often used in manufacturing such as Open Platform Communications Unified Architecture (OPC-UA), developed by the OPC Foundation. OPC-UA is a machine-to-machine (m2m) communication protocol commonly used for industrial automation.

The industrial data analytics framework describes dig data analytics management systems on Industrial Internet systems data, which often take the form of the following data:

• Relational data: This format is best suited for metadata of assets and things, and as it captures the system configurations and relations to enterprise data systems. Commonly used relational database systems are Oracle, Microsoft SQL Server, IBM DB2, MySQL, and PostgreSQL.
• Time series data: This is a series of discrete data points in time order, often equally spaced in time. For industrial assets and sensors, this may be the bulk of the data. Such data is often stored in historian software that records the historical information and trends about industrial processes. NoSQL databases are also used to manage this type of data.
• Object related data: This form of bulk object storage is best suited for images, blobs, and other unstructured data. Examples of this type of storage are Amazon S3, Microsoft Azure blob storage, and Scality that can be deployed on-premise.

To run industrial analytics on such a variety of data formats, real-time and batch capabilities are required. The ability to orchestrate multiple analytics workflows is also required.

The stakeholders for analytics can be data scientists, analytics developers, architects, as well as subject matter experts (SMEs). The following diagram illustrates the typical life cycle of the development of industrial analytics:

This is an iterative process and suitable for agile development. An important characteristic of the industrial analytics is the ability to not only pull the aggregated and summary data to the analytics but also to be able to push down the analytics to near real-time data feeds. This is due to the extremely large data volumes that devices transmit and the frequent nature of these transmissions.

In subsequent chapters, we will talk about such near real-time analytics technologies and discuss the emergence of the NoSQL database and Hadoop-based data management solutions fundamental to solving these problems. Architects of Industrial Internet solutions must embrace skills in industrial analytics and new data paradigms to be able to design effective solutions.

The frameworks we've described provide some of the background material you will need. However, other areas are less well covered because they are relatively new (cloud computing) or not typically in the domain of architects (user experience).

Architects are typically very comfortable defining on-premises systems and translating that knowledge into public cloud concepts. IaaS is a form of cloud computing that provides virtualized computing resources for enterprise and Industrial Internet systems in the form of operating systems, servers, storage, and, networking. They can also choose PaaS which also delivers data management systems, tools, and the management of those components, or SaaS that provide an even more complete solution. We will further discuss the business and technical trade-offs of each in the next couple of chapters.

That said, architects must make key design decisions that span the on-premises and Public Cloud paradigms. They must consider where the data is stored and who it belongs to. The compliance and regulatory landscape often becomes a key consideration for the architects. As with any solution, they must consider who has access to the data and under what context. We'll provide much more guidance here later in the book.

As data is collected and analyzed to turn insights into action, the user experience, or UX, assumes importance. It is important to remember that UX Design refers to the User Experience Design, while the more understood UI Design stands for User Interface Design. An industrial worker on the factory floor or a field service technician working on overhead power lines has a very different expectations when interacting with the environment and the device they use to deliver actionable tasks.

While architects often confine themselves to technology challenges, in the realm of the Industrial Internet, business considerations go hand in hand. IIC provides a business strategy framework that illustrates the major areas that should be of interest:

Industry analysts agree that the IIoT is experiencing explosive growth, and emerging leaders in companies, such as the CDOs, are being tasked with driving strategies. Architects must sharpen their business acumen and have an opportunity to groom up for future digital leadership roles.

Areas where architects can broaden their contributions to their companies will include, but not be limited to, the following things:

• Understand the competitive landscape and help define their company's role
• Understand new market dynamics and pressures introduced by IIoT
• Understand new business models, the value chain, and partnerships/alliances and continuous reevaluation of the same within their company
• Evaluate the societal impact of the Industrial Internet

This chapter served as an introduction to some of the fundamental concepts we will discuss in the book and provides you with some background if you are new to the Industrial Internet. After reading this chapter, you should now understand how the Industrial Internet evolved from the Industrial Revolution and why organizations are investing in IIoT solutions. You should have also gained a better understanding of some of the challenges to prepare for in the deployment of IIoT solutions. This chapter also explained the roles and responsibilities of the various architects and tied this to their professional development paths.

We'll continue our journey in the next chapter by further exploring how various roles will interact and how we can begin to define our Industrial Internet architecture in a way that will ultimately lead to a viable project. You will also learn about different architecture viewpoints and their impact on the design of your overall IIoT solution.