Digital transformation accelerated during the pandemic, as companies of all sizes and in a range of industries invested in advanced, collaborative technologies in a bid to adapt to the new normal. As a result, many more organizations have now established a solid foundation on which to build a digital future.
At the heart of this digital transformation is data – every digital interaction generates vast quantities of it. And it can help businesses identify new opportunities, make better decisions, and improve operational efficiency.
However, data can only deliver business benefits if it can be discovered, analyzed, and used to generate actionable insights. That’s part of the reason why organizations are turning to technologies such as artificial intelligence (AI) and blockchain – viewing them as a ‘silver bullet’ to unlock the next wave of transformation.
But, without a firm data foundation, their efforts are doomed to failure. Unless organizations gather the right data in the first place – and harness it effectively – even the best new technologies will fall victim to the classic trap of ‘garbage in, garbage out’.
There is often a huge chasm between the vast amount of data being generated and businesses being able to harness that data – and it is tempting to turn to blockchain and AI to bridge the gap. But they are not capable of doing so – it is not what they are designed to do.
Blockchain offers the ability to provide many different stakeholders with access to trusted data across a network – and AI provides a path to predictive, autonomous decision-making. But accessing data is often no mean feat, as relevant data could reside in a separate business unit or an external organization. And, without the full picture, it is difficult to maximize value.
It is easy to think the answer lies in collecting vast quantities of data, storing it on a blockchain so it is ‘trusted’, and then relying on AI to deliver valuable insights to transform business operations. There are plenty of data lakes and data warehouses to help with this approach. But it’s not as simple as that.
The secret of success when it comes to bridging the data chasm is to look at data through the lens of the entity it relates to, not the data itself. Looking at real-world objects and using data to describe them digitally allows more comprehensive and intricate pictures to emerge. This is why digital twin technology holds the key to unlocking the next wave of digital transformation.
Modern supply chains are a good example, with their complexity, large numbers of influencing factors, and a variety of separate organizations interconnecting. Creating real-time digital models of physical consignments makes it possible for all participants to access them via a central platform. These digital entities comprise all relevant data for a consignment, dynamically updating in real time with new data as the consignment progresses through the supply chain.
Consignment and inventory data can be combined with transport schedules and allocated transport, for example. Data captured from a vehicle telematics system can then be added to the relevant consignment digital twin, offering real-time information contexualized to an individual consignment.
The use of intelligent data orchestration creates relationships between the digital models – unlocking complex, real-time, multi-dimensional insights with the data that is generated across the supply chain. The questions being asked of the data can be easily changed, and new entities can be introduced.
This approach ensures only relevant data is sourced from connected systems – something that is important for a supply chain, where the requirements of each stakeholder are different. The telematics system used by a haulier, for example, will provide visibility of all its vehicles, all the time, to enable the company to monitor its vehicle fleet. The manufacturer whose goods are being transported, on the other hand, simply wants to know where their consignment is in real time. They only need the GPS data of a specific vehicle for a specific period of time while the goods are being moved.
Intelligent data orchestration can also feed data into a blockchain, providing long-term trust in the data and ensuring integrity. And it can fuel machine learning and AI models to provide predictive analysis across operations. There is no single answer to unlocking digital transformation. It will be a combination of technologies, each playing an important role. What is critical is that these technologies are used in the correct way, playing to their respective strengths, and helping to drive the next wave of change across our connected world.
