which technologies combine to make data a critical organizational asset ?

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Which technologies combine to make data a critical organizational asset
Machine Learning and Artificial Intelligence (AI) are the technologies that combine to make data a critical organizational asset.The terms Artificial Intelligence and Machine Learning are much discussed and also confusing these days. A subset of Artificial Intelligence is Machine Learning (ML). In machine learning, one designs and applies algorithms that are able to learn from past experiences. It is possible to predict if or when a behavior will recur based on its past behavior …

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Which technologies combine to make data a critical organizational asset?
Correct Answer is: A. Machine Learning and Artificial Intelligence (AI). Penetration Testing and intelligence practice, machine learning and Artificial Intelligence, Speech and Natural language processing technologies combine to make data a critical organizational asset. Explanation: An asset is a data which is regarded as one of the most important …

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Which technologies combine to make data a critical organizational asset?
Different technologies are commonly used to combine data to make a critical organizational asset. These technologies include: Data warehousing and extensive data management: This is the process of organizing and storing large amounts of data so that it can be analyzed quickly and effectively. Data warehousing helps organizations to identify patterns, Trends, and efficiencies in their data so that they can be acted on and improved. Big data management techniques allow organizations to store …

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Which technologies combine to make data a critical organizational asset?
Penetration Testing and intelligence practice, machine learning and Artificial Intelligence, Speech and Natural language processing technologies combine to make data a critical organizational asset. Explanation: An asset is a data which is regarded as one of the most important assets of an association. It is unique in its detail and context.

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Which technologies combine to make data a critical…get hint 4
Which technologies combine to make data a critical organizational asset? A. Internet of Things… Which technologies combine to make data a critical organizational asset? A. Internet of Things (IoT) and electronic devices B. Machine Learning and Artificial Intelligence (AI) C. Speech and Natural Language Processing (NLP)’

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Which technologies combine to make data a critical organizational asset(a) The practice of penetration testing and intelligence(b) Machine Learning and Artif…

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Which technologies combine to make data a critical organizational asset …
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Which technologies combine to make data a critical organiz…
Which technologies combine to make data a critical organiz… Berkay . 20.06.2022. Which technologies combine to make data a critical organizational asset? 1 Answers. Show reply. Forbes 10.09.2022. Answer: Information Security; The fundamental principles (tenets) of information security are confidentiality, integrity, and availability. Every element of an information security program (and …

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Which technologies combine to make data a critical organizational asset …
answered. Which technologies combine to make data a critical organizational asset? Speech and Natural Language Processing (NLP) Machine Learning and Artificial Intelligence (AI) Internet of Things (IoT) and electronic devices Penetration Testing and Intelligence Practice I don’t know this yet. yrajnish3016 is waiting for your help.

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Which technologies combine to make data a critical organizational asset …
answered. Which technologies combine to make data a critical organizational asset? Machine Learning and Artificial Intelligence (AI) Internet of Things (IoT) and electronic devices Speech and Natural Language Processing (NLP) Penetration Testing and Intelligence Practice I don’t know this yet. Mahboob7256 is waiting for your help.

A digital twin is a virtual representation of a real-world physical system or product (a physical twin) that serves as the indistinguishable digital counterpart of it for practical purposes, such as system simulation, integration, testing, monitoring, and maintenance. A digital twin can, but must not necessarily, be used in real time and regularly synchronized with the corresponding physical system. A pragmatic litmus test of the efficacy of digital twin is for a system tester to run a robust system verification & validation test suite on both the digital twin and the physical twin.[1] When the system tester cannot reliably distinguish between the digital twin and the physical twin with a high probability, the former is a bona fide digital twin.
As an example of a real time digital twin, an object being studied — for example, a wind turbine — may be outfitted with various sensors related to vital areas of functionality. These sensors produce data about different aspects of the physical twin’s performance, such as external weather conditions, RPM, and energy output. This data is then relayed to a processing system and applied to the digital twin. [2] [3] [4] Though the concept originated earlier, the first practical definition of a digital twin originated from NASA in an attempt to improve physical-model simulation of spacecraft in 2010.[5] Digital twins are the result of continual improvement in the creation of product design and engineering activities. Product drawings and engineering specifications have progressed from handmade drafting to computer-aided drafting/computer-aided design to model-based systems engineering and strict link to signal from the physical counterpart.
Contents
1 History
2 Types
3 Characteristics
3.1 Connectivity
3.2 Homogenization
3.3 Reprogrammable and smart
3.4 Digital traces
3.5 Modularity
4 Examples
5 Industrial use cases
5.1 Manufacturing industry
5.2 Urban planning and construction industry
5.3 Healthcare industry
5.4 Automotive industry
6 Related technologies
7 External links
8 References

History[edit]
Digital twins were anticipated by David Gelernter’s 1991 book Mirror Worlds.[6][7] The concept and model of the digital twin was first publicly introduced in 2002 by Grieves, at a Society of Manufacturing Engineers conference in Troy, Michigan.[8] Grieves proposed the digital twin as the conceptual model underlying product lifecycle management (PLM). [9]
An Early Digital Twin Concept by Grieves and Vickers
The digital twin concept, which has been known by different names (e.g., virtual twin), was subsequently called the digital twin by John Vickers of NASA in a 2010 Roadmap Report.[10] The digital twin concept consists of two distinct system or product parts (the physical system or product, the digital system or product) and the connections between the two systems. The connections between the physical system and the digital system include information flows and physical sensor flows between the two and their physical environment.
Types[edit]
Digital twins are commonly divided into subtypes that sometimes include: digital twin prototype (DTP), digital twin instance (DTI), and digital twin aggregate (DTA).[11] The DTP consists of the designs, analyses, and processes that realize a physical product. The DTP exists before there is a physical product. The DTI is the digital twin of each individual instance of the product once it is manufactured. The DTA is the aggregation of DTIs whose data and information can be used for interrogation about the physical product, prognostics, and learning. The specific information contained in the digital twins is driven by use cases. The digital twin is a logical construct, meaning that the actual data and information may be contained in other applications.
Characteristics[edit]
Digital twin technologies have certain characteristics that distinguish them from other technologies:
Connectivity[edit]
One of the main characteristics of digital twin technology is its connectivity. The recent development of the Internet of Things (IoT) brings forward numerous new technologies. The development of IoT also brings forward the development of digital twin technology. This technology shows many characteristics that have similarities with the character of the IoT, namely its connective nature. First and foremost, the technology enables connectivity between the physical component and its digital counterpart. The basis of digital twins is based on this connection, without it, digital twin technology would not exist. As described in the previous section, this connectivity is created by sensors on the physical product which obtain data and integrate and communicate this data through various integration technologies. Digital twin technology enables increased connectivity between organizations, products, and customers.[12] For example, connectivity between partners and customers in a supply chain can be increased by enabling members of this supply chain to check the digital twin of a product or asset. These partners can then check the status of this product by simply checking the digital twin.
Servitization is the process of organizations that are adding value to their core corporate offerings through services.[13] In the case of the example of engines, the manufacturing of the engine is the core offering of this organization, they then add value by providing a service of checking the engine and offering maintenance.
Homogenization[edit]
Digital twins can be further characterized as a digital technology that is both the consequence and an enabler of the homogenization of data. Due to the fact that any type of information or content can now be stored and transmitted in the same digital form, it can be used to create a virtual representation of the product (in the form of a digital twin), thus decoupling the information from its physical form.[14] Therefore, the homogenization of data and the decoupling of the information from its physical artifact, have allowed digital twins to come into existence. However, digital twins also enable increasingly more information on physical products to be stored digitally and become decoupled from the product itself.[15]
As data is increasingly digitized, it can be transmitted, stored and computed in fast and low-cost ways.[15] According to Moore’s law, computing power will continue to increase exponentially over the coming years, while the cost of computing decreases significantly. This would, therefore, lead to lower marginal costs of developing digital twins and make it comparatively much cheaper to test, predict, and solve problems on virtual representations rather than testing on physical models and waiting for physical products to break before intervening.
Another consequence of the homogenization and decoupling of information is that the user experience converges. As information from physical objects is digitized, a single artifact can have multiple new affordances.[15] Digital twin technology allows detailed information about a physical object to be shared with a larger number of agents, unconstrained by physical location or time.[16] In his white paper on digital twin technology in the manufacturing industry, Michael Grieves noted the following about the consequences of homogenization enabled by digital twins:[17]
In the past, factory managers had their office overlooking the factory so that they could get a feel for what was happening on the factory floor. With the digital twin, not only the factory manager, but everyone associated with factory production could have that same virtual window to not only a single factory, but to all the factories across the globe. (Grieves, 2014, p. 5)
Reprogrammable and smart[edit]
As stated above, a digital twin enables a physical product to be reprogrammable in a certain way. Furthermore, the digital twin is also reprogrammable in an automatic manner. Through the sensors on the physical product, artificial intelligence technologies, and predictive analytics,[18] A consequence of this reprogrammable nature is the emergence of functionalities. If we take the example of an engine again, digital twins can be used to collect data about the performance of the engine and if needed adjust the engine, creating a newer version of the product. Also, servitization can be seen as a consequence of the reprogrammable nature as well. Manufactures can be responsible for observing the digital twin, making adjustments, or reprogramming the digital twin when needed and they can offer this as an extra service.
Digital traces[edit]
Another characteristic that can be observed, is the fact that digital twin technologies leave digital traces. These traces can be used by engineers for example, when a machine malfunctions to go back and check the traces of the digital twin, to diagnose where the problem occurred.[19] These diagnoses can in the future also be used by the manufacturer of these machines, to improve their designs so that these same malfunctions will occur less often in the future.
Modularity[edit]
In the sense of the manufacturing industry, modularity can be described as the design and customization of products and production modules.[20] By adding modularity to the manufacturing models, manufacturers gain the ability to tweak models and machines. Digital twin technology enables manufacturers to track the machines that are used and notice possible areas of improvement in the machines. When these machines are made modular, by using digital twin technology, manufacturers can see which components make the machine perform poorly and replace these with better fitting components to improve the manufacturing process.
Examples[edit]
An example of digital twins is the use of 3D modeling to create digital companions for the physical objects.[21][22][23][24][25] It can be used to view the status of the actual physical object, which provides a way to project physical objects into the digital world.[26] For example, when sensors collect data from a connected device, the sensor data can be used to update a digital twin copy of the device’s state in real time.[27][28][29] The term device shadow is also used for the concept of a digital twin.[30] The digital twin is meant to be an up-to-date and accurate copy of the physical object’s properties and states, including shape, position, gesture, status and motion.[31]
A digital twin also can be used for monitoring, diagnostics and prognostics to optimize asset performance and utilization. In this field, sensory data can be combined with historical data, human expertise and fleet and simulation learning to improve the outcome of prognostics.[32] Therefore, complex prognostics and intelligent maintenance system platforms can use digital twins in finding the root cause of issues and improve productivity.[33]
Digital twins of autonomous vehicles and their sensor suite embedded in a traffic and environment simulation have also been proposed as a means to overcome the significant development, testing and validation challenges for the automotive application,[34] in particular when the related algorithms are based on artificial intelligence approaches that require extensive training data and validation data sets.
Industrial use cases[edit]
Manufacturing industry[edit]
The physical manufacturing objects are virtualized and represented as digital twin models (avatars) seamlessly and closely integrated in both the physical and cyber spaces.[35] Physical objects and twin models interact in a mutually beneficial manner.
The digital twin is disrupting the entire product lifecycle management (PLM), from design, to manufacturing to service and operations.[36] Nowadays, PLM is very time-consuming in terms of efficiency, manufacturing, intelligence, service phases and sustainability in product design. A digital twin can merge the product physical and virtual space.[37] The digital twin enables companies to have a digital footprint of all of their products, from design to development and throughout the entire product life cycle.[38][12] Broadly speaking, industries with manufacturing business are highly disrupted by digital twins. In the manufacturing process, the digital twin is like a virtual replica of the near-time occurrences in the factory. Thousands of sensors are being placed throughout the physical manufacturing process, all collecting data from different dimensions, such as environmental conditions, behavioural characteristics of the machine and work that is being performed. All this data is continuously communicating and collected by the digital twin.[38]
Due to the Internet of Things, digital twins have become more affordable and could drive the future of the manufacturing industry. A benefit for engineers lies in real-world usage of products that are virtually being designed by the digital twin. Advanced ways of product and asset maintenance and management come within reach as there is a digital twin of the real ‘thing’ with real-time capabilities.[39]
Digital twins offer a great amount of business potential by predicting the future instead of analyzing the past of the manufacturing process.[40] The representation of reality created by digital twins allows manufacturers to evolve towards ex-ante business practices.[36] The future of manufacturing drives on the following four aspects: modularity, autonomy, connectivity and digital twin.[20] As there is an increasing digitalization in the stages of a manufacturing process, opportunities are opening up to achieve a higher productivity. This starts with modularity and leading to higher effectiveness in the production system. Furthermore, autonomy enables the production system to respond to unexpected events in an efficient and intelligent way. Lastly, connectivity like the Internet of Things, makes the closing of the digitalization loop possible, by then allowing the following cycle of product design and promotion to be optimized for higher performance.[20] This may lead to increase in customer satisfaction and loyalty when products can determine a problem before actually breaking down.[36] Furthermore, as storage and computing costs are becoming less expensive, the ways in which digital twins are used are expanding.[38] Implementation challenges such as data integration, organizational or compliance challenges can hinder the implementation of Digital Twins and its benefits.[41]
Urban planning and construction industry[edit]
Geographic digital twins have been popularised in urban planning practice, given the increasing appetite for digital technology in the Smart Cities movement. These digital twins are often proposed in the form of interactive platforms to capture and display real-time 3D and 4D spatial data in order to model urban environments (cities) and the data feeds within them.[42]
Visualization technologies such as augmented reality (AR) systems are being used as both collaborative tools for design and planning in the built environment integrating data feeds from embedded sensors in cities and API services to form digital twins. For example, AR can be used to create augmented reality maps, buildings, and data feeds projected onto tabletops for collaborative viewing by built environment professionals.[43]
In the built environment, partly through the adoption of building information modeling (BIM) processes, planning, design, construction, and operation and maintenance activities are increasingly being digitised, and digital twins of built assets are seen as a logical extension – at an individual asset level and at a national level. In the United Kingdom in November 2018, for example, the Centre for Digital Built Britain published The Gemini Principles,[44] outlining principles to guide development of a national digital twin.[45]
One of the earliest examples of a working ‘digital twin’ was achieved in 1996 during construction of the Heathrow Express facilities at Heathrow Airport’s Terminal 1. Consultant Mott MacDonald and BIM pioneer Jonathan Ingram connected movement sensors in the cofferdam and boreholes to the digital object-model to display movements in the model. A digital grouting object was made to monitor the effects of pumping grout into the earth to stabilise ground movements.[46]
Digital twins have also been proposed as a method to reduce the need for visual inspections of buildings and infrastructure after earthquakes by using unmanned vehicles to gather data to be added to a virtual model of the affected area.[47]
Healthcare industry[edit]
Healthcare is recognized as an industry being disrupted by the digital twin technology.[48][37] The concept of digital twin in the healthcare industry was originally proposed and first used in product or equipment prognostics.[37] With a digital twin, lives can be improved in terms of medical health, sports and education by taking a more data-driven approach to healthcare.[36] The availability of technologies makes it possible to build personalized models for patients, continuously adjustable based on tracked health and lifestyle parameters. This can ultimately lead to a virtual patient, with detailed description of the healthy state of an individual patient and not only on previous records. Furthermore, the digital twin enables individual’s records to be compared to the population in order to easier find patterns with great detail.[48] The biggest benefit of the digital twin on the healthcare industry is the fact that healthcare can be tailored to anticipate on the responses of individual patients. Digital twins will not only lead to better resolutions when defining the health of an individual patient but also change the expected image of a healthy patient. Previously, ‘healthy’ was seen as the absence of disease indications. Now, ‘healthy’ patients can be compared to the rest of the population in order to really define healthy.[48] However, the emergence of the digital twin in healthcare also brings some downsides. The digital twin may lead to inequality, as the technology might not be accessible for everyone by widening the gap between the rich and poor. Furthermore, the digital twin will identify patterns in a population which may lead to discrimination.[48][49]
Automotive industry[edit]
The automobile industry has been improved by digital twin technology. Digital twins in the automobile industry are implemented by using existing data in order to facilitate processes and reduce marginal costs. Currently, automobile designers expand the existing physical materiality by incorporating software-based digital abilities.[15] A specific example of digital twin technology in the automotive industry is where automotive engineers use digital twin technology in combination with the firm’s analytical tool in order to analyze how a specific car is driven. In doing so, they can suggest incorporating new features in the car that can reduce car accidents on the road, which was previously not possible in such a short time frame.[50]
Related technologies[edit]
Digital workplace
Discrete event simulation
Finite element method
Health and usage monitoring systems
Holon (philosophy) § In multiagent systems
Industry 4.0
Integrated vehicle health management
Internet of things
Predictive engineering analytics
External links[edit]
Digital Control Twin and Supply Chain[1]
Digital Engineering FAQ — Digital Engineering Group web information portal
IEEE[2] – Digital Twin: Enabling Technologies, Challenges and Open Research [3]
ISO/DIS 23247-1 Automation systems and integration — Digital Twin framework for manufacturing — Part 1: Overview and general principles [4]
References[edit]
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