However, this dependence on manual methods is coming under scrutiny as the industry seeks better ways to navigate an increasingly complicated reality, in which factors such as economic volatility, heightened geopolitical risk and, most recently, the pandemic add to the complexity and uncertainty.
In order to understand and respond to a rapidly changing world, maritime stakeholders are collecting and using more data than ever before. According to research conducted by industry analysts IDC in July and August 2020, more than a third of maritime and shipping organizations are seeing their data volumes increase by more than 50% per year. It is not only the volume, but also the variety of data that is increasing. Whilst this surge creates opportunities across the supply chain, many industry participants are unable to extract the full value from their data.
To manage this deluge of data, organizations are accelerating plans to digitalise and automate their operations. While many industry participants are at the beginning of the journey towards digital transformation, others have already embraced advanced initiatives using artificial intelligence (AI), predictive analytics, data lakes and business intelligence reporting. There is growing recognition across the industry that sound data management practices are critical to the success of such investments. Participants understand that even the most sophisticated digitalisation program will fail if the data being processed does not meet the appropriate standards in relation to quality, completeness and transparency.
Despite this realization, the implementation of data management best practices is a work in progress in the maritime and shipping industry. For example, while almost 40% of those surveyed by IDC have identified data management as a strategic priority, fewer than one in four (22%) are currently using master data management (MDM) techniques – centralizing and consolidating data from multiple sources to create a single version of the truth that will be used throughout the organization.
With this in mind, for those that are planning to launch a new digitalisation initiative or take an existing program to the next level, the following five steps will help build a strong data management foundation that supports success and ensures high ROI.
Step 1. Examine your current state. Take a close look at the data sets you use. What types of data do you collect throughout the organization? Who uses that data, and what purpose does it fulfill? How is the data managed throughout its lifespan - how is it collected, verified, cleaned, distributed to downstream systems, protected and audited? By conducting a thorough review of the data driving your decision-making, and the way in which it does so, you can begin to outline the data architecture that will form the starting point for your digital transformation.
Step 2. Explore your future state. What do you want to be able to do with your data in an ideal future? Where could that data bring the greatest value to your business? How could it support your organizational goals? Perhaps the data will help you demonstrate greater transparency in the face of increasing supply-chain volatility. Perhaps you are projecting aggressive business growth targets through increased throughput, customer acquisition or infrastructure projects. Perhaps the data can help you enhance service to and elicit greater loyalty from your customers in a highly competitive market. Start with your organizational goals and work backwards to set the data priorities.
Step 3. Create a framework. Before you begin a digital transformation initiative, focus on master data management (as defined above). Create a framework that masters all your data sets to ensure that the data is accurate, verified, consistent and ready to support automation and analytics activities across user groups. There is a valuable lesson to be learned from the first wave of industries to undertake digital transformation, many of which skipped this foundational step and discovered that without the right data governance model in place, their data-driven initiatives did not deliver the value expected.
Step 4. Embrace data as an asset. Organizations making the transition from manual to digitalised operations often see data management as a burden. This approach leads to missed opportunities, inefficiencies and unnecessary risk. With a structured data management framework in place and a digital repository of trustworthy data, your organization can begin to use data to reduce risk, enhance service delivery and drive growth through efficiency, innovation and continual optimization. But first, you must embrace data as an asset within your organizational culture. The understanding that data is an asset should be driven from the top down by stakeholders who have both organizational influence and an understanding of the value data brings to the organization.
Step 5. Be realistic about your capabilities. For the maritime industry, data requirements are rapidly becoming more complex than internally developed systems can address. For many, the cost of maintaining these capabilities in-house is challenging, time-consuming and takes the focus away from the core business. Outsourcing data management can provide the best of both worlds by enabling users to access data they need to perform their work while alleviating the effort required to manage the underlying tools and technologies that consume, clean, amalgamate and distribute that data for decision-making.
As the maritime and shipping industry looks to ride the wave of digital transformation across the supply chain, these five key steps will position you to serve not only your needs, but also those of your customers. With market environments continually changing and partner demands only increasing, embracing robust data management is imperative for providing best-in-class service.