Self-Sovereign Identification raises the value of Data shared in an Ecosystem

Self-Sovereign Identity: A Distant Dream or an Immediate Possibility?
As organizations focus on data-driven business models to remain competitive, they will increasingly seek to collaborate with partners and exchange data. Data shared in an ecosystem is more valuable than data locked in a silo because it leads to new innovations and customer experiences. This trend is playing out in many industries including transportation, logistics, energy, manufacturing, healthcare, telecommunications and financial services. The effort of maintaining countless data repositories has made data acquisition very expensive, causing a drag on the overall competitiveness of an industry, not to mention the additional burden of adherence to the General Data Protection Regulation and personally identifiable information standards with respect to handling personal data. In the case of autonomous cars, for example, there is so much data to be had — and so much needed — that pooling it makes sense to get a sufficient amount for R&D in as timely and cost-effective a manner as possible. This moves the entire industry forward. Through a combination of internet of things, artificial intelligence, and distributed ledger technology (DLT) we will see auto manufacturers, fleet operators, OEMs and end users willing to share and exchange data as digitized assets through data marketplaces, enabling the players to benefit from new offerings based on the transparency and monetization of shared data.

Shifting from cars to mobility services

Shared Mobility–Changing the Landscape of Automotive Industry - FutureBridge

In the automotive industry, the market is shifting from selling cars to providing mobility services. A DLT-based system, combined with self-sovereign identity, makes it straightforward to build a mobility ecosystem where it’d be easy to enable new ways to engage with customers and partners, leveraging trusted and safe data exchange. For example, make it simple for customers to get the best deal on car financing (no need to re-enter their personal details to get approval at each dealership). Make customer interactions seamless by having customer history (i.e., loyalty data) immediately available with the customer’s explicit consent, without the need to store this data at each dealership or at a car manufacturer.

A trusted and verifiable data-enabled mobility ecosystem helps companies offer new services, such as car-sharing or car-exchanging that can involve dealers, for example, as drop-off and pick-up locations. This includes making the process of verifying auto insurance and driver qualifications seamless.

If I’m on vacation and don’t need my car, I can share it, or if I drive a compact car and need an SUV for a few days, I can connect with the ecosystem and benefit from this shared economy. In addition, the revenue I collect from the shared vehicle could be applied to a down payment on a new car.

Dealers benefit too. They can offer inspecting and cleaning services for the shared cars, and they get potential new customers at their location. Car makers benefit because they can grow their brand’s value and get insights into car usage.

Controlling identity, unblocking consent

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Empowering an individual or an asset to control one’s own identity is a precursor to companies’ reaping the benefits of pooled data. Through self-sovereign identity, an individual will be able to consent, authenticate or verify themselves without having to present their documents. Users can access third-party-owned products and services while keeping their anonymity.

Many organizations and consortiums are contributing towards the establishment of open source decentralized identity to achieve interoperability among all participants, set protocols, and develop technologies and code in areas including decentralized identifiers (DID) and verification, storage and compute, authentication, claims and verifiable credentials. The focus of these efforts has been to decouple the trust between the identity provider and the relying party to create a more flexible and dynamic trust model such that the ecosystem benefits from increased market competition and customer choice.

In 2020, we expect to witness deployment of self-sovereign identity to unblock consent, which will help organizations leverage inaccessible data sets without breaching privacy regulations. This will facilitate decentralized data marketplaces that provide a level playing field for all market players by enabling any of them to monetize data. It allows for improved and customized offerings, thus setting higher standards and increasing the overall value of the ecosystem.

Designing systems for machines rather than people: Latest Tech Trend.

A brief history of robotics and AI

For businesses to be agile and respond quickly to changing market conditions, they need to provide business users with real-time and near-time operational data. That means harnessing data from devices and tackling the latency challenge. In 2020, we will see more organisations shift their design thinking from services and systems for people to services and systems for machines. The move to machine-to-machine (M2M) systems also means processing is moving to the network edge, where the data is.

Action at the edge

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Organisations are experimenting with extending data clusters to the edge to reduce latency, gain operational efficiencies and improve products and services. Fast food company Chick-fil-A is running Kubernetes on 6,000 kitchen devices in all 2,000 of its restaurants. This is part of the chain’s internet of things (IoT) strategy to collect and analyze more data to improve throughput, operational efficiency and, most of all, customer service.

The move to M2M dovetails with the notion of catering to local markets using local data. This will foster new design architectures that take into account privacy, security and regulatory concerns regarding the “frame of reference” of data — i.e., the notion of localised data being more valuable than global data in terms of its usability and mining. When organisations consider modernizing their IT operations and changing the way microservices are deployed — especially large multinationals with a wide global reach — it becomes critical for them to consider the advantages of targeting a geography locally, and realizing that there are new tools and ways of doing this.

Another facet driving the change in design thinking is the need to make maximum use of computing resources. Whereas the decision frequency of a person ranges between 1-15 hertz, which means that people can decipher information and make a decision in about ½-1 second, today’s microprocessors can operate at gigahertz and process information in nanoseconds. If these processors are not operating as fast as they can, they are just space eaters. Organisations want to keep their processors as busy as possible, which means designing for billions of decisions or operations per second. Otherwise, they may end up paying for unused capacity.

Signs of M2M

M2M technology: business value explained - Itransition

Two examples of M2M architecture are SAP Leonardo and KubeEdge. Through SAP Leonardo intelligent technologies and capabilities, SAP is integrating its ERP applications with IoT platforms, combining traditional IT services with M2M capabilities. Importantly, SAP can address broader markets than niche IoT platforms.

KubeEdge, built on Kubernetes, is an open source platform for building edge computing solutions that extend to the cloud. The platform supports network, application deployment and metadata synchronization between the cloud and edge. It extends the Kubernetes ecosystem from cloud to edge and provides benefits such as lower latency, low resource consumption and applications at the edge that can run in offline mode.

The road ahead

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We are starting to observe a shift in IT design from IT for humans to IT for machines. These design patterns deliver richer experiences because they enable substantially more processing in the same experience time. Design shifts will lead to changes in batch processing and stream processing architectures, which are constantly being updated and reimagined with better M2M capabilities. Data and analytics will continue moving to the edge where the machines are, to analyze the massive influx of IoT data and provide maximum throughput with minimum latency. Rapid deployments of these transformational architectures may not be immediate, but over time these new architectures will be a forcing function for IT modernization.

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