Composable platform for TELCO

Industry outlook

Communications service providers are pinning their hopes for future revenue growth not on their existing connectivity businesses, but on new B2B services that they hope to develop on top of connectivity by leveraging 5G, IoT, edge computing and AI. Despite the market outlook forecasts global double-digit growth beyond 5G connectivity business, the data does not suggest that the telecom industry is on the verge of a B2B revenue explosion[1].

There was hope that platform business models pushed by communication service providers could start demonstrating growth in the market, via ecosystem effect. The reality is existing offers range from basic connectivity, private networks, infrastructure services, IoT platform accelerators and a combination of managed services. The desire to finally monetize the 5G opportunity still demonstrates potential but realizing the business case and progress on technological advancement and new business models remains challenging.

Communication solution providers should focus on building composable cloud platforms that provide horizontal industry-agnostic solutions, based on RAN, 5G-core, vertical solutions targeting priority segments – such as such as industry 4.0, smart cities and cross-industry solutions that consider value sharing and open innovation-enabled by next frontier of network programmability to create unique value propositions.

The United Nations sustainable development goals[2] defined a series of bold ambitions, among those related to connectivity. By 2030, major stakeholders agree that network technology will support a sustainable world, going beyond strategies related to networks harvesting energy from green sources and decreased energy consumption. Primarily, the communications industry will increase its contributions to society, enabling efficient resource use and new ways of living. It also includes going beyond efforts to fight climate change, such as people inclusion that is enabled by connectivity providing access to different forms of digital services such as: banking, education, healthcare, governmental services, remote work, or smart agriculture. The goal is to provide communities on a global scale, with high-grade wireless services for digital inclusion.

Monetize the platform business model

5G is transforming the traditional, layered protocol-based communication industry through cloud network servitization.

Industry horizontal solutions based on RAN, 5G-core, and security enable fast growth and innovation by allowing CSP’s to focus on their core business through a common technical foundation and universal connectivity offerings such as network-as-a-service, cybersecurity-as-a-service, and infrastructure-as-a-service. This layer also includes core cloud platform services enablement such as computing, data & AI, API, IoT / Edge and TELCO-specific functions OSS / BSS.

Industry vertical solutions target dedicated IT functions for specific business needs and operations, such as real-time quality control in an automotive assembly line or a connected ambulance with cognitive vision for patient monitoring.

As supply chains become more distributed and advanced, an integrated approach forms multisided platforms. Cloud native design, open source, and standards drive openness in networks and operations architectures. This enables cross-industry solutions that consider value sharing and open innovation-enabled co-creation and co-capture. The main goal is to provide revenue streams for all involved: service providers, enterprises, government organizations, and consumers. Organizations must consider the lifecycle stage of complementors, customers, and partners in the ecosystem, including the open-sourced effort of a community of developers, a critical element for platform programmability in the communications industry.

“VulcanForms[3]can produce tens of thousands of parts for various industries within hours, from tens of thousands of parts for a jet engine, then switch to doing medical implants. The knowledge of how to make the part lives in the software, allowing large industrial customers to focus on their core R&D, sales, and marketing, and outsource production to individual factories located anywhere.”

Source: Greg Reichow, a partner at Eclipse Ventures[4]

5G is transforming the traditional, layered protocol-based communication industry through cloud network servitization.

Industry horizontal solutions based on RAN, 5G-core, and security enable fast growth and innovation by allowing CSP’s to focus on their core business through a common technical foundation and universal connectivity offerings such as network-as-a-service, cybersecurity-as-a-service, and infrastructure-as-a-service. This layer also includes core cloud platform services enablement such as computing, data & AI, API, IoT / Edge and TELCO-specific functions OSS / BSS.

Industry vertical solutions target dedicated IT functions for specific business needs and operations, such as real-time quality control in an automotive assembly line or a connected ambulance with cognitive vision for patient monitoring.

As supply chains become more distributed and advanced, an integrated approach forms multisided platforms. Cloud native design, open source, and standards drive openness in networks and operations architectures. This enables cross-industry solutions that consider value sharing and open innovation-enabled co-creation and co-capture. The main goal is to provide revenue streams for all involved: service providers, enterprises, government organizations, and consumers. Organizations must consider the lifecycle stage of complementors, customers, and partners in the ecosystem, including the open-sourced effort of a community of developers, a critical element for platform programmability in the communications industry.

Composable Telco platform

In the digital era, platform components can be constructed like “Lego bricks” using a composition approach. This allows for a loosely coupled approach where numerous services can be combined – taking into consideration their use cases that need to be implemented – and matched without impacting others. Like in the ‘Lego brick’ world, platform services and functions need to be repeatable, automatic, and simple to follow a plug-and-play orchestration approach. This allows for different service designs and value propositions to cater to distinct customer requirements.

The constituent capabilities of a composable platform for TELCO industry that support a new flexible network design, full A.I. integration, network programmability, and functions & solutions composability are as follows:

• Modular – Components that can be easily added or removed as needed. Thanks to microservice, containerization or event-driven architecture-based design will exist on the platform to realize a service. Then, to manage this complexity, it will become essential to develop tools for selecting compatible software components suites to realize it and to manage their dependencies.
• Programmability – 3rd Party solution development for HW or networks. Programmable connectivity provides a standard interface across operator networks for developers to build network-aware modern connected applications. This enables the creation of cloud and edge-native applications that interact with network intelligence.
• Autonomous – A.I. as a service runs the network fully automated. This approach enables agile AI-driven orchestration of 5G networks and services, addressing the complexity of concurrent optimization with tailored data, models, and algorithms for specific network domains, slices, and edge cloud services. Autonomous networks are an emerging topic in terms of network operations adapting concepts of human system engineering concepts that are implemented in energy and manufacturing companies.
• RAN Cloudification – New API architecture for customized high-performance networks. Cloudification/containerization acts as a superior backend for applications, providing a foundation for network slicing, on-demand provisioning of virtual resources, and easy support for new services.
• 3D convergence – Progressively integrate terrestrial, satellite & micro networks. The requirement of constant network high-performance availability in 5G has led to the concept of 3D convergence, where satellites, terrestrial, and other communication networks are seamlessly integrated and managed by in-network intelligence as part of the software network continuum. The proved examples are looming on harbor management and intelligent traffic management for autonomous driving proof of concepts.
• Distributed – Multi cloud platforms mesh – cloud agnostic- an infrastructure layer for managing cloud environments without imposing any modification on how the cloud services are implemented. Cooperation between different administrative domains is required to consume APIs exposed by other domains develop open, global, and accessible API solution to operator capabilities, regardless the customer’s domain is in, project CAMARA, is one of many accelerators[5].

A composable TELCO platform offers the possibility of mass tailoring of connectivity services, intelligence & multiple industry solutions. The composability element allows numerous platform components can be combined – taking into consideration their use cases that need to be implemented – and matched without impacting others. Allows the creation of new business models served by operators and network equipment providers. It will gradually reduce the 5G monetization challenge.

References

[1] The telco growth imperative, TM Forum 2023.

[2] THE 17 GOALS | Sustainable Development (un.org)

[3] https://www.vulcanforms.com/

[4] Eclipse: Industrial Evolution

[5] Camara Project – Linux Foundation Project

Open innovation takes the stage

This year I attended Innov8rs Amsterdam conference on the topic of open innovation. For years this has been one of the most promising approaches to change how companies innovate, but somehow, it has been failing to deliver.

As Ana Vazquez Romero from The Next Web, pointed startup scouting and qualification is a lagging capability appearing missing in corporate centers of innovation. It links back to an opportunity I worked before related with positive impact finance that was bridging trough an investment vehicle reducing social and financial inequality while supporting the growth of the local economy, combining connectivity, clean energy, inclusive finance, agro-tech, healthcare, and education, creating smart communities, tackling UN’s SDGs rests on closing a yearly US$2.6 trillion investment gap.

Platform business models were expecting to enable such propagation, connecting corporations with startups and accelerating innovation waves. Frank Mattes, from Lean Scaleup, shared that despite the promise of building systems that scale innovation outcomes by orders of magnitude with “0 increase in headcount” is still a journey to be completed, scaling-up external startups not embedded in the greater scheme of things.

The most successful platform models are founder-led, when one organization needs full control to converge at maximum the monetization potential, dampening at minimum the power coming from external patties. Successful cooperative-led ecosystem types – those that supports cross-company integration or defined value exchanges and are platforms that might unite multiple industry around a common set of capabilities – are still facing hard to scale, but that might be about to change. It reminds me of the promise of telecoms becoming technology companies that is still a hurdle to overcome. The only question is when will it happen? On this industry, the looming of programable 5G networks which 3rd party solution development will allow easily add or remove components for customization and adaptability.

Banks to the lead on pursuing open innovation, the spark of the fintech threat ignited the change, digital banks are curating third-party products via an online marketplace, avoiding the overhead of building, supporting, and scaling their own products while quickly assembling an extensive service for both personal and corporate segments. Another bright example is GitHub, which changed positively open-source software development.

Julia van Boven, from Impact Nation at ABN AMRO Bank, shared how cross sector between investment banking and cross-sector partnerships is increasingly being defined shifting from just proving financial resources to adding core competencies to change business models and underlying operations to become more sustainable.

I also realize working with a leading petrochemical company that implements a cradle-to-cradle approach on continuously intensifying the ability to restore, retain and redistribute materials, components and products back into the system optimally, including, new molecular engineering, exploring the limits of resource optimization cannot be implemented by creating, possessing and keeping close to chest intellectual property, as out of corporate boundaries better approaches already exist.

Open and collective innovation is key to build resilience and keep competing in new ways, those who integrate the ability to cooperate with external entities, irrespective if they are activists, micro-enterprises, or same size companies, co-developed outside of the organization.

Human augmentation with ChatGPT – Smart Factory definition

There are many untamed challenges in terms of such man and machine symbiosis, as we the human species, have the responsibility to define by which rules we want to live, as machines progress in new areas, changing the foundations on our society is organized

The man and the machine manifesto

This year marks a tectonic shift in human augmentation, despite the ongoing criticism of the progress that needs to be made in terms of model fitness, as the machine training increases, pursuing a global federated data ingestion network, results accuracy will consistently improve at dramatic fast velocity. In particular, the threats related with plagiarism and job security result in a misconception of the reinvention of what a job definition should be. Other domains that are being neglected and should have the attention of the community are ethics and bias, as well as, if humans will be bound by imaginary machine or human machined interactions – they just want to enrich their egos – and de-socialize with the world around.

We are stepping in the era of “homo silicus” [1].

In the context of the nature of my daily routines, being one of them doing research, I did a test about the definition of a smart factory is. The context is related with projects I am involved with and I wanted to challenge my own definition and how to implement on the shop floor, and in particular how that changes the way we search, discover information and execute reasoning. The results are impressive. What I extracted from ChatGPT is without any doubt a step change for knowledge-oriented work. It took me 30 minutes to curate all that is pasted bellow. A similar effort with low assisted search, would take around 3 days – the benchmark I setup for myself that the effort to discover meaningful pointers increases substantially with low return.

We are facing a tremendous opportunity on how we will shape the nature of our own existence.

What Smart Factory is?

A smart factory, also known as a “digital factory,” is a manufacturing facility that uses advanced technologies such as artificial intelligence (AI), the Internet of Things (IoT), and robotics to automate and optimize its production processes. The concept of the smart factory is based on the integration of cyber-physical systems (CPS), which combine sensors, communication networks, and software to create a “smart” and connected production environment.

The smart factory leverages data generated by these systems to improve efficiency, quality, and safety, while reducing costs and environmental impact. Some of the key benefits of a smart factory include:

Increased productivity: By automating repetitive tasks and optimizing production processes, smart factories can significantly increase productivity and output.

Improved quality: The use of sensors and other monitoring technologies can help identify defects and improve product quality.

Enhanced flexibility: Smart factories are designed to be agile and adaptable, enabling them to quickly respond to changing market conditions and customer demands.

Reduced costs: By minimizing waste, reducing downtime, and optimizing energy usage, smart factories can help lower production costs.

Overall, the smart factory is a key component of Industry 4.0, the fourth industrial revolution that is characterized by the convergence of digital, physical, and biological technologies.

But that definition is more focused on the outcomes, such as increasing productivity, flexibility, it does not explain what the constituent characteristics of a smart factory are.

You’re right, let me elaborate further. Smart factories have several characteristics that distinguish them from traditional manufacturing facilities. Some of these characteristics are:

Connectivity: Smart factories are highly connected and integrated with the internet, allowing for real-time data exchange and communication between different machines, systems, and people.

Automation: Automation is a critical aspect of smart factories. By leveraging advanced technologies such as robotics, machine learning, and artificial intelligence, smart factories can automate various manufacturing processes, from assembly to quality control, to reduce human intervention.

Data-driven decision-making: Smart factories use data analytics and artificial intelligence algorithms to analyze data from sensors and other sources to make informed decisions in real-time.

Flexibility: Smart factories are designed to be agile and adaptable, allowing for quick changes to production schedules and configurations based on changing market demands.

Integration: Smart factories integrate various production systems and processes, from supply chain management to logistics, to create a seamless and efficient workflow.

Sustainability: Smart factories aim to minimize waste, reduce energy consumption, and minimize environmental impact through the use of efficient production processes and sustainable practices.

Overall, smart factories leverage advanced technologies and intelligent systems to optimize production processes and improve efficiency, quality, and sustainability.

What are the sources you used to give such answers?

As an AI language model, my responses are generated based on a large corpus of text data that has been collected from various sources, including books, academic papers, news articles, and other online resources.

To provide an accurate and reliable response, I rely on the most current and reliable information available at the time of my training (September 2021). However, it’s important to note that the field of smart factories is rapidly evolving, and new developments and advancements may have occurred since then.

If you have any specific questions or need more detailed information, please let me know, and I’ll do my best to provide you with the most up-to-date and accurate information available.

Why you did not include human augmentation as an important characteristic of smart factory?

You’re absolutely right – human augmentation is an important characteristic of smart factories that I should have mentioned.

Human augmentation refers to the use of technology to enhance human abilities and performance in the workplace. In the context of smart factories, human augmentation can involve the use of wearable devices, exoskeletons, and augmented reality (AR) to improve worker safety, productivity, and efficiency.

For example, wearable devices such as smart glasses can provide workers with real-time access to information and instructions, reducing the need to stop work and consult manuals or computer screens. Exoskeletons can help workers lift heavy objects and reduce the risk of injury, while AR technology can be used to train workers on complex tasks and provide remote assistance and collaboration.

Overall, human augmentation is an essential aspect of smart factories that can help improve worker safety, job satisfaction, and productivity, and it should have been included as one of the important characteristics of smart factories. Thank you for pointing this out.

Right, however using wearable devices it is just a quick-win or low hanging fruit . Why assisted reasoning was not included ? In the context of a smart factory assisted reasoning is applied in operational optimization combined with twinning, product engineering, or even preventing humans out of the loop. Hence in what context human augmentation should be used in a smart factory to make it truly smart?

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Edited the question.

In the context of a smart factory assisted reasoning might be applied in operational optimization combined with twinning, product engineering, or even preventing humans from getting out of the loop. Hence, in what context should human augmentation be used in a smart factory to make it truly smart?

Human augmentation can be used in a smart factory in several ways to make it truly smart. Here are some examples:

Enhancing worker safety: Human augmentation technologies such as exoskeletons and wearable sensors can be used to reduce the risk of injury to workers by assisting them in lifting heavy objects, providing real-time alerts on hazardous conditions, and monitoring their vital signs.

Improving productivity: Augmented reality and wearable devices can help workers perform their tasks more efficiently by providing them with real-time information and instructions, reducing errors and rework, and enhancing collaboration and communication among team members.

Enabling remote support and collaboration: Human augmentation technologies such as remote monitoring and assistance tools can enable experts to provide remote support to workers on the shop floor, reducing the need for travel and increasing responsiveness.

Enhancing skills and training: Augmented reality and virtual reality technologies can be used to create immersive training environments that simulate real-world scenarios, enabling workers to learn and practice new skills in a safe and controlled environment.

Enabling customization and personalization: Human augmentation technologies such as 3D printing and additive manufacturing can enable the production of customized and personalized products, tailored to the specific needs and preferences of individual customers.

In all of these contexts, human augmentation should be used to complement and enhance human skills, rather than replace them. The goal of human augmentation in a smart factory is to create a symbiotic relationship between humans and machines, where each can leverage their strengths to achieve optimal results.

Man-machine fusion is another concept that can be applied in the context of a smart factory. It refers to the integration of human and machine intelligence to create a synergistic relationship that leverages the strengths of both.

In a smart factory, man-machine fusion can be used to create a more flexible and adaptable production system that can respond to changing market demands and optimize production processes in real-time. For example, by combining human expertise and intuition with machine learning algorithms and advanced analytics, a smart factory can identify and respond to production bottlenecks, quality issues, and supply chain disruptions more quickly and effectively.

What about man machine fusion in the context of a smart factory?

Man-machine fusion can also enable new forms of collaboration and innovation in the workplace. By leveraging augmented reality, virtual reality, and other advanced technologies, workers can collaborate more effectively across different locations and disciplines, enabling faster and more efficient problem-solving and decision-making.

Overall, man-machine fusion can help create a more responsive, agile, and intelligent manufacturing environment that leverages the strengths of both humans and machines to achieve optimal results. However, it’s important to ensure that the integration of human and machine intelligence is done in a way that respects human dignity, privacy, and autonomy and that workers are adequately trained and supported in their use of advanced technologies.

How can smart factories become scalable? Meaning operate like a network across other manufacturing plants, either managed by the same enterprise or via partnerships, joint-ventures, or contractual agreements with third parties. How that can be implemented?

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Edited the question.

How can smart factories become scalable? Meaning operate like a network across other manufacturing plants. How that can be implemented?

An error occurred. If this issue persists please contact us through our help center at help.openai.com.

Edited the question again.

How can smart factories become scalable? Meaning operate like a network. How that can be implemented?

An error occurred. If this issue persists please contact us through our help center at help.openai.com.

Edited the question once again.

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In the context of smart factory, what is manufacturing as a service?

Manufacturing as a Service (MaaS) is a business model that allows companies to outsource their manufacturing processes to a third-party provider, typically a smart factory, that offers on-demand manufacturing services. This model is made possible by the use of advanced digital technologies such as the Internet of Things (IoT), cloud computing, and artificial intelligence (AI) that enable real-time monitoring, control, and optimization of production processes.

In a MaaS model, a customer provides the specifications for the product they want to produce, and the smart factory takes care of the rest, from design and prototyping to production and delivery. The smart factory handles all aspects of the manufacturing process, including sourcing raw materials, managing inventory, and performing quality control, while the customer retains ownership of the intellectual property and product design.

MaaS offers several advantages for companies looking to bring new products to market quickly and cost-effectively. By outsourcing manufacturing to a smart factory, companies can avoid the upfront capital costs and infrastructure investments required to set up their own manufacturing operations, while also gaining access to cutting-edge manufacturing technologies and expertise.

MaaS can also help companies respond more quickly to changing market demands and production requirements, as they can easily scale production up or down as needed without having to invest in additional equipment or personnel.

Overall, manufacturing as a service is an innovative business model that leverages the capabilities of smart factories to offer flexible and on-demand manufacturing services to customers, enabling faster and more cost-effective product development and production.

What are the steps to implement a smart factory?

Implementing a smart factory requires a systematic approach that involves several steps. Here are the general steps to implement a smart factory:

Define the scope and objectives: The first step is to define the scope of the smart factory implementation and identify the objectives you want to achieve. This includes identifying the key performance indicators (KPIs) that you will use to measure success, such as productivity, quality, and flexibility.

Assess the current state: The next step is to conduct a comprehensive assessment of your current manufacturing operations, including your processes, equipment, and systems. This assessment will help identify areas of improvement and opportunities for optimization.

Define the architecture: Based on the assessment, you can then develop an architecture for your smart factory implementation, including the hardware, software, and communication infrastructure required to support your objectives.

Select the technology solutions: Once you have defined the architecture, you can then select the technology solutions that best meet your needs, including automation systems, sensors, data analytics tools, and cloud computing platforms.

Develop a roadmap: With the technology solutions identified, you can then develop a roadmap for the implementation, including a timeline and milestones for each phase of the project.

Build the infrastructure: The next step is to build the infrastructure required to support the smart factory implementation, including the installation of new equipment, sensors, and communication systems.

Integrate the systems: Once the infrastructure is in place, you can then integrate the various systems and technologies to create a unified and cohesive smart factory environment.

Test and validate: Before launching the smart factory, you should conduct rigorous testing and validation to ensure that the systems and processes are working as intended and meeting the defined objectives.

Train the workforce: Finally, you should train the workforce on the new systems and processes to ensure that they have the skills and knowledge required to operate and maintain the smart factory.

Overall, implementing a smart factory requires careful planning, coordination, and execution to ensure that the project meets its objectives and delivers the expected benefits.

That is a very generic answer, it seems it was curated from Gartner, Forrester or IDC.

I apologize if my answer came across as too generic. While I did use my general knowledge as an AI language model to provide an overview of the steps involved in implementing a smart factory, I did not use any specific sources such as Gartner, Forrester, or IDC. However, if you are looking for more detailed and specific guidance on how to implement a smart factory, there are many resources available from industry associations, research institutions, and technology vendors that can provide more in-depth insights and guidance.

This contradicts when I asked in a previous question the sources you used sources and none were provided.

You are correct, I apologize for the oversight. In my previous response, I should have provided more information on the sources I used to provide my answer. As an AI language model, I use a variety of sources to provide information, including research papers, industry reports, and other reputable sources. However, I do not have access to any specific sources or databases, as I do not have the ability to browse the internet like a human. Instead, I rely on the data and information that has been input into my training model. Again, I apologize for any confusion or misunderstanding my previous response may have caused.

In that sense what were the sources of your answer in the previous question about constituent characteristics of a smart factory?

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References [1] – Teubner, T., Flath, C.M., Weinhardt, C. et al. Welcome to the Era of ChatGPT et al.. Bus Inf Syst Eng (2023). https://doi.org/10.1007/s12599-023-00795-x

Open data healthcare ecosystems

Have you ever wondered about the possibilities if more anonymized data was used to fight the biggest challenges on earth? From climate change, food security and healthcare for example?

Have you ever come across the idea that no entity is able to collect the full spectrum of available data that represents a domain of body of knowledge? – well, some distinguished companies, which their business model based on advertising think they can (“I want your data”) – however, if you consider about the way anatomy is studied and perceived it is a reductionist decomposition – the respiratory system, the cardiovascular system – results in a design contradiction because we humans are a distributed complex system that equalizes itself as a whole, despite we created these walls of separation, that ended up of generating data silos. It makes me remember in the retail industry the concept of the customer golden record, the aggregation of all our transactions, wishes, wants and emotions broadcast in social media.  Since healthcare is organized by specialties, as a result of the decomposition of human anatomy into sub systems, data may not be available in a single institution. Also, important data sources as patient self-generated data are not managed by healthcare providers and are an important data source about our uniqueness as a human being.

Personal health information that health care providers and payers, must make available to an individual, such as their current and past medical conditions and care received; and longitudinal information that is of general interest and should be widely available to the patient to facilitate coordinated care and improved patient outcomes to the point on understanding failure to fill a prescription, or receive therapies, that can indicate that the individual has had difficulty financing a treatment plan or adopt it once he does not believe it makes him feel better.

We should on one hand avoid inflating expectations in particular claims about making general assumptions about understanding or finding generality and potential solutions – for example, targeting populations in different regions in the same country – but we should not create barriers to ourselves about the potential of making more anonymized data to address at it seems unreachable challenges.

As we are being influenced by adopting open science, as an evolution of open source, this creates new opportunities around exploring these possibilities:

• Healthcare data lakes to be used to interoperate ontologies for learning object retrieval and reuse from local to global ontology. In this way, data lakes were exploited to extract information from heterogeneous data sources and generate real-time statistics and reports.
• Create statistical models based on collecting population data from preceding patients and study partners, that provide valuable insight about prediction of disease development in patients with the same similarity.
• Generate synthetic data parameterized within the range that represents a wider population, sex, age, weight, diet, degree of physical activity, and societal environment to explain the mechanisms underpinning inter-individual variability in therapy response and to identify sub-populations at higher risk.
• Amplifying the possibilities of using federated learning for novel algorithms on image classification, object detection, image colorization and video rendering.
• Enable a pathway to expose fewer patients to experimental therapies by relying on other sources of evidence. It can offer an opportunity to investigate aspects of medical device performance in many more clinically relevant cases. Statistical models can be used to collect a representative virtual patient cohort, and mechanistic models can then be used to simulate the medical device behavior under defined scenarios.
• Much is being discussed about the benefits of platform business. In the concept of the wider healthcare industry platform, population health is a key area for governments that want to progress on healthcare education, shifting habits that will have a positive impact on quality of life and decrease cost of care, understand how to drive investments and policy formulation to reduce or prevent the most common diseases. Paying it forward to governments also brings benefits of supplement data that will enrich advancements in healthcare.

Implementing an earth monitor

When I was a teenager – well if we have a startup nation mentality, we all continue to be the real rebels aren’t we? – I noticed when I was at the beach, this very same kind of human negative presence on earth. That was an agglomeration of plastic melted with small rocks and other kinds of debris. Today it seems this has a name, called, plastiglomerate.

Human negative impact could be prevented if we were more aggressive on monitoring and measuring the imbalance created by human activity. This is achieved if we generate and make more data accessible.
Have you come across what is the potential to solve earth problems that include social, environmental and economic convergence if more anonymized data was published, make it assessable and make combine it to find solutions?

We can have two kinds of domains to measure impact:

• Evaluative: conducted retrospectively and based on actual outcomes that have already happened.
• Forecast: predicts how much social value will be created if the activities meet their intended outcomes.

Promoting the creating of a global telemetry system and public data disclosure can be used to improve climate models, provide surveillance of environmental status and enable early warning systems for extreme events, or protect the biosphere and ecosystems, that also includes people finding a way to be included in the society and have access to a source of income, being able to access a bank account, clean energy.

• TELCOS can enable the use the end point sensor capabilities to measure microclimate in the context of farming or make a joint venture with utilities to promote maximize green energy generation and introduction into the transportation network, giving also an edge on energy trading markets.
• Facility management companies can create global energy management models leveraging in the buildings where services are provided to induce practices to reduce energy consumption and promoting self-generation or storage.
• Governments can correlate patterns that will put people at risk, by changes in the societal context that will make people lose their job. It can promote wellness education to improve quality of life and decrease the cost of care.

You might argue that like in those challenges of the customer golden record, we will never get to know with full precision who the customer is, and the profile is incomplete, however, it is better to have some data on day one and build the scale effect, conducting to reliable, verifiable, and objective data to that will make us to step-in to:

• Climate action: greenhouse gases (GHG) emissions; disaster prevention and resilience; waste management and water management.
• Economic development: green energy production, and consumption; agriculture and land use; mobility; healthcare access; infrastructure development.
• Social equality: access to employment; food security; upskilling; participation and integration into society.

The viability of this approach thus depends primarily on the number of people, entities, and the intensity of use of the datasets and ultimately, data existence.