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How Can We Fix The Missing Link Holding Back Blockchain?

The main obstacle to exploiting blockchain technology in the next wave of digital transformation is the huge chasm between the amount of data being generated and the ability of businesses to effectively harness and use it.

The global digital supply chain market is projected to grow from $3.92bn in 2020 to $13.68bn by 2030, with digitalisation seen as key to ensuring the necessary agility and robustness across operations to cope with today’s rapidly changing landscape. The headlines suggest that blockchain technology is set to play a crucial role in this growth, enabling data to be shared in a fast, secure way among different organisations.

Yet blockchain is just a data store – in itself, it is not the ‘silver bullet’ that will unlock the next wave of transformation. The real key to success lies in the gathering of the data in the first place and the building of the records that will underpin digitalisation. Unless organisations gather the right data in the first place – and harness it effectively – even the best new technologies will fall victim to the age-old problem of ‘garbage in, garbage out’.

Given the complexity of modern supply chains, the number of influencing factors and the number of separate organisations interconnecting, the latest next-generation technologies are extremely attractive. In particular, blockchain’s ability to provide many stakeholders with access to trusted data across a supply chain network could deliver profound advances.

Technologies such as blockchain need to be fed data. There is certainly no shortage of data. However, the missing link in blockchain technology is the huge chasm between the amount of data being generated and businesses being able to harness and use that data. Blockchain is not capable of bridging this gap, as it is not what it is designed to do.

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Accessing data in the supply chain is very challenging. Relevant data could reside in a separate business unit or an external organisation. Without the whole picture, it is difficult to maximise value. Data lakes are often seen as a potential solution, as they enable huge amounts of data to be centralised – meaning stakeholders can connect to the data lake and access the data they require. However, this approach can cause data privacy issues between organisations.

Some blockchain vendors are offering an enhancement whereby participants can upload data and use permission-based rules to control which participants can see their data. However, as there is so much data, companies struggle to know and manage which data to share with which organisation.

Access to structured data products, as opposed to many streams of different data types, enables users to access and analyse all relevant data simply and quickly – allowing more complex queries to be run and much greater insights to be generated.

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The real key to bridging the data chasm is digital twin technology. By enabling real-time models of real-world objects to be created in the digital world, it makes them accessible by all participants in a supply chain via a central platform. The digital twins comprise all relevant data for a consignment, dynamically updating in real time with new data as the consignment progresses through the supply chain.

Digital twin technology offers a way to combine data from many different sources to deliver a single data product which can be accessed, on a permission basis, by stakeholders across the supply chain network – enabling them to access all data simply and effectively. Unlike with traditional object-oriented databases, the digital twin is temporary – only existing for the duration of a consignment’s lifecycle, after which the digital twin disappears from the central platform.

Global network

The technology to make this scenario possible is intelligent data orchestration. Using the central digital twin concept, only relevant data is sourced from connected systems – with little or no effort required from the respective domains. The automated technology brings the disparate data together, structuring it to form a complete data product and dissolving it at the end of its operational lifecycle – providing a firm data foundation to unlock the next generation of digital transformation.

Source: E&T

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