By: Hemanta Banerjee, Vice President of Public Cloud Data Services, Rackspace Technology

Minimising disruption is par for the course for the logistics sector. Whether caused by natural disasters or humans, delays, redundancies, and bottlenecks can have a domino effect on the entire supply chain that is often costly.  

Yet, there are unprecedented threats today to the global supply chainwhich will continue to persist as we go into 2025. Ensuring operations remain agile is critical to preventing downtime or dips in productivity due to supply chain disruptions. That, however, hinges on modernising data. 

Making the most out of data 

The good news is that the logistics, transport and shipping industries have access to vast amounts of data they collect from their operations. The problem is that not all organisations are aware of how they can use that data as most of it is unstructured and siloed. That’s why data modernisation is imperative to ensure smoother operations and continuous productivity.  

Currently, logistics, transport and shipping companies typically use data to streamline and automate processes like inventory management. However, the industry needs to scale its adoption of technology to support more use cases. 

Having a unified platform that is capable of managing data generated is imperative, but organisations must also modernise to make use of existing unstructured data to support their future workloads and processes. This rests on:

• Understanding the company’s domain;
• Comprehending how to ingest unstructured data onto a common platform; and
• Exploring new data concepts, such as synthetic data, which can be deployed to train artificial intelligence (AI) and machine learning models

Data modernisation and AI  

According to another report by IDC, 40% of logistics teams in Asia Pacific will deploy advanced supply chain visibility tools that rely on AI and machine learning analytics by 2027 to get better insights. This is estimated to drive logistics savings by up to three percent. 

AI helps the industry by solving mundane and repetitive tasks. But it can also do a lot more. This includes providing predictive insights based on data analytics. For example, a shipping company can use AI to plan faster and more efficient routes and anticipate products that are in demand across different locations. 

The caveat, however, is that realising the full potential of AI hinges on access to the right type of data. These capabilities are only possible through robust analysis of the vast amounts of data that companies have. 

Furthermore, because data modernisation helps businesses make the most out of their unstructured data, it is immensely important to clarify available data, what they can be used for, and how that benefits the business. That ultimately gives decision-makers a fuller picture and gets AI projects off the ground. 

Driving resilience 

While data modernisation is critical, it is also crucial to prioritise data security. Gartner says that no one should be surprised that 44% of organisations intend to substantially increase supply chain cybersecurity budgets in the years ahead. However, spending must be done in a considered manner, and businesses do not need to go it alone. They can tap the expertise of vendors to assist them in their data modernisation journeys in ways that prime them for resiliency. Some helpful services to look out for include:

• Advisory functions: Helps organisations understand their current position and identify the appropriate solution to leverage their data. 
• Day-one services: Helps organisations with data migration and transformation or modernisation supported by Amazon Web Services.
• Day-two services: Ensures efficiency for an organisation’s systems and workloads running on the cloud. 

Data modernisation stands as a pivotal strategy for enhancing operational resilience and efficiency within the logistics, transport, and shipping sectors. By embracing data modernisation, organisations can unlock the potential of data, enabling informed decision-making, predictive analytics, and the adoption of advanced technologies like AI and machine learning.