It is important to recognize that intelligence within IoT technology market is not inherent but rather must be carefully planned. AIoT market elements will be found embedded within software programs, chipsets, and platforms as well as human-facing devices such as appliances, which may rely upon a combination of local and cloud-based intelligence.
Just like the human nervous system, IoT networks will have both autonomic and cognitive functional components that provide intelligent control as well as nerve end-points that act like nerve endings for neural transport (detection and triggering of communications) and nerve channels that connect the overall system. The big difference is that the IoT technology market will benefit from engineering design in terms of Artificial Intelligence (AI) and cognitive computing placement in both centralized and edge computing locations.
AI is rapidly making its way into many advanced solutions including autonomous vehicles, smart bots, advanced predictive analytics, and more. Many industry verticals will be transformed through AI integration with enterprise, industrial, and consumer product and service ecosystems. It is destined to become an integral component of business operations including supply chains, sales and marketing processes, product and service delivery and support models. The term for AI support of IoT (or AIoT) is just beginning to become part of the ICT lexicon as the possibilities for the former adding value to the latter are only limited by the imagination.
AI enhances the ability for big data analytics and IoT platforms to provide value to each of these market segments. The use of AI for decision making in IoT and data analytics will be crucial for efficient and effective decision making, especially in the area of streaming data and real-time analytics associated with edge computing networks. Real-time data will be a key value proposition for all use cases, segments, and solutions. The ability to capture streaming data, determine valuable attributes, and make decisions in real-time will add an entirely new dimension to service logic. In many cases, the data itself, and actionable information will be the service.
The convergence of AI and IoT technologies and solutions (AIoT) is leading to “thinking” networks and systems that are becoming increasingly more capable of solving a wide range of problems across a diverse number of industry verticals. AI adds value to IoT through machine learning and improved decision making. IoT adds value to AI through connectivity, signaling, and data exchange.
- AI adds value to IoT through machine learning and improved decision making.
- IoT adds value to AI through connectivity, signaling, and data exchange
While early solutions are rather monolithic, it is anticipated that AIoT integration within businesses and industries will ultimately lead to more sophisticated and valuable inter-business and cross-industry solutions. These solutions will focus primarily upon optimizing system and network operations as well as extracting value from industry data through dramatically improved analytics and decision making processes.
In many cases, the data itself, and actionable information will be the service for enterprise, industrial, and government sectors.
AIoT infrastructure and services will therefore be leveraged to achieve more efficient IoT operations, improve human-machine interactions and enhance data management and analytics, creating a foundation for IoT Data as a Service (IoTDaaS) and AI based Decisions as a Service.
IoTDaaS constitutes retrieving, storing and analyzing information and provide customer either of the three or integrated service package depending on the budget and the requirement.
New models are emerging to reduce friction across the value chain including enhanced Big Data as a Service (BDaaS) offerings. BDaaS is anticipated to make cross-industry, cross-company, and even cross-competitor data exchange a reality that adds value across the ecosystem with minimized security and privacy concerns.
IoTDaaS offers convenient and cost effective solutions to enterprises of various sizes and domain. IoTDaaS constitutes retrieving, storing and analyzing information and provide customer either of the three or integrated service package depending on the budget and the requirement.
AI algorithms enhance the ability for big data analytics and IoT platforms to provide value to each of these market segments.
Big data in IoT is different than conventional IoT and thus will requires more robust, agile and scalable platforms, analytical tools and data storage systems than conventional big data infrastructure. Looking beyond data management processes, IoT data itself will become extremely valuable as an agent of change for product development as well as identification of supply gaps and realization of unmet demands. Big data and analytics will increase in importance as IoT evolves to become more commonplace with the deployment of 5G IoT.
The Massive Machine-type Communications (mMTC) portion of fifth generation cellular networks will facilitate a highly scalable M2M network for many IoT applications, particularly those that do not require high bandwidth. Data generated through sensors embedded in various things/objects will generate massive amounts of unstructured (big) data on real-time basis that holds the promise for intelligence and insights for dramatically improved decision processes.
Big data in IoT is also dissimilar than non-machine related analytics and thus will require more robust, agile and scalable platforms, analytics tools, and data storage systems than conventional infrastructure. Due to this new architecture approach, the need to handle data differently, and the sheer volume of unstructured data, there will be great opportunities for big Data in IoT. Analytics used in IoT will become an enabler for the entire IoT ecosystem as enterprise begins to take advantage of new business opportunities such as syndicating their own data.
AI coupled with advanced big data analytics provides the ability to make raw data meaningful and useful as information for decision-making purposes. The use of AI for decision making in IoT and data analytics will be crucial for efficient and effective decision making, especially in the area of streaming data and real-time analytics associated with edge computing networks. Real-time data will be a key value proposition for all use cases, segments, and solutions. The ability to capture streaming data, determine valuable attributes, and make decisions in real-time will add an entirely new dimension to service logic. In many cases, the data itself, and actionable information will be the service.
These advanced analytics provide the ability to make raw data meaningful and useful as information for decision-making purposes. AI enhances the ability for big data analytics and IoT platforms to provide value to each of these market segments. The use of AI for decision making in IoT and data analytics will be crucial for efficient and effective decision making, especially in the area of streaming data and real-time analytics associated with edge computing networks.
The ability to capture streaming data, determine valuable attributes, and make decisions in real-time will add an entirely new dimension to service logic. In many cases, the data itself, and actionable information will be the service. However, real-time data is anticipated to become a highly valuable aspect of all solutions as a determinant of user behavior, application effectiveness, and identifier of new and enhanced mobile/wireless and/or IoT related apps and services.
In terms of overall AIoT data management, Mind Commerce sees three different types of IoT Data:
- Raw Data: Untouched and Unstructured Data
- Meta Data: Data about Data
- Transformed Data: Valued-added Data
AI will be useful in support of managing each of these data types in terms of identifying, categorizing, and decision making.
We see the AIoT market transforming from today's largely consumer appliance and electronics related approach to one in which AIoT data is highly valued asset wherein companies like SAS provide a utility function in terms of helping enterprise, industrial, and government clients monetize their data. This will likely occur in a Data as a Service market model, which may be segmented in various ways including by Sector including Public Data, Business Data, and Government Data:
- Public Data consists of Communications and Internet Data (broadcast media, social media, texting, voice, video/picture sharing, etc.), Government Tracked Data (public records such as vehicle and home title, licensing, public resource usage including roadway usage), User Generated Data (consumer and business data made public [may be anonymized or not] such as vehicle usage, appliance data, etc.), and Other Data category.
- Business Data consists of Enterprise Data and Industrial Data across various industry verticals. This data comes from many different business related activities. Some of this data may be static and/or stored in data lakes. Some of this data may be generated and used in real-time.
- Government Data is data that the government collects about itself such as Government Services Administration (GSA), essential services (such as public safety), military, homeland security, etc. This is not to be confused the government collecting certain public data (such as highway usage).
It may also be segmented by Source Type. As it is prohibitively difficult to identify all of the sources and source types, Mind Commerce has broadly segmented Source by Machine Data (consumer appliances, vehicles [ cars, trucks, planes, trains, ships, etc. ], robots and industrial equipment, etc.) and Non-machine Data (everything else including people texting/talking/etc., enterprise data collected by humans, etc.).
It is important to note that the DaaS also includes data sourced from a machine (such as from a jet engine) that is not “Internet-connected” and thus limited in utility without the Internet of Things (IoT) to collect, relay, and provide opportunities for feedback loops. Accordingly, Mind Commerce has also segmented the Data as a Service market by Data Collection Type, which includes IoT DaaS data and Non-IoT DaaS data. Machine Data that does not use IoT, by definition, will not be streaming data or allow for real-time analytics.
