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Why is data so important in maritime?

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In this featured Q&A with James Kwan (Business Lead, EDM for Maritime, IHS Markit), Seatrade Maritime explores questions like 'Why is data so important?' and 'What are the most innovative companies doing?' amongst data management and digital transformation best practice.

Why is data so important in the maritime and shipping industry today?

In all industries there is a need to make faster, more accurate decisions in order to gain a competitive edge, and data and the underlying decision-support technologies are key to this.

In the context of maritime and shipping, we see increasing consumer demands resulting in greater pressure on supply chain stakeholders. In order to optimize supply chains and adapt to these new demands, decision-makers need to be able to access reliable data in real time and apply that information in a practical way (these topics are discussed in our white paper about data management and digital transformation).

Furthermore, data volumes and frequencies are only growing. This creates opportunities, but also challenges in terms of how to extract high value. When data is managed and applied in the right way, it can also drive other benefits for the industry, such as helping to improve sustainability, safety, logistics efficiencies and supply chain transparency.

In general, are industry participants making optimal use of their data? If not, what are the most common barriers?

There are a number of different aspects to optimizing data usage – not all of which are widely adopted today. Firstly, it is important to develop a culture in which data is understood to be an asset. This will help ensure support and buy-in throughout the enterprise. Organizations should be aligned from top-to-bottom to implement data management and governance best practices – with an emphasis on data quality, transparency and completeness.

Technology can and should be an enabler of this. Too often today we see that a reliance on manual processes and inflexible legacy technologies is preventing maritime and shipping participants from making optimal use of their data – when the opposite should be true. By investing in the right platform and architecture, you can eradicate data siloes and ensure that all users have access to the same high-quality, validated data for use in their decision-making and operational processes.

What are the most innovative organizations doing?

Culture is a big factor. When senior leaders view data as an asset, the rest of the organisation will too. Those that want to take data management to the next level and extract the maximum value from their data often start by assessing their existing state; thinking about their future landscape; and then identifying the areas that need attention in order to achieve strategic goals. They use formal frameworks to track their progress and assign responsibilities to various in-house stakeholders, resulting in accountability.

Many participants are investing in analytics, AI, data lakes and business intelligence solutions to gain insights into vessel performance, port call optimization and vessel fuel efficiencies, for example. The most successful are those that first focus on getting data management right – including data quality, completeness, governance and stewardship. They understand that no matter how sophisticated their analytics applications are, the outputs will only be reliable if the input data is accurate. Implementing these core capabilities will also prove valuable for future projects as participants navigate constantly changing market environments.

Digital transformation is a strategic priority for many industry participants. Why is data management so important for digitalization initiatives?

Data, technology, business processes and organizational change are critical components of digital transformation, and data management is relevant to each of these components. For example, data management best practices provide a foundation for improved business processes, such as automation and decision-making accuracy. Organizational change is easier to implement when your decision-making landscape (including, but not limited to, data and the way it is managed) is well understood.

As discussed in our recent white paper, without sound data management, digital transformation initiatives are likely to either fail or fall short of the envisioned benefit. Consequently, users are likely to build (often bespoke) workaround solutions, creating a fragmented data and technology landscape.

Digital transformation is an ongoing project due to the continually changing nature of the industry. As competition and supply chain demands increase, industry participants need to be able to use their data and the underlying technology capabilities to adapt, grow and scale.

Many industry participants are investing in analytics. What is the role of data management?

Downstream systems such as analytics platforms are only as good as the data going into them. It is the old adage: “garbage in, garbage out”.

Most of the time data will be coming from multiple sources (for example, vessels, ports, terminals, railroads, containers, shipping lines, data providers and customers) in various formats and frequencies. The role of data management is to control the integration, validation and completeness of data. Data management is critical for ensuring analytics initiatives and solutions are fed with validated and complete data. Data management is also a mechanism for then distributing those analytics to other parts of the business. This will lead to organizational-wide innovation.

Having a core data management platform gives users confidence that the data that is being analysed is the exact same data that is also being processed in other downstream systems – there is consistency across the whole organisation. This builds trust across user groups, leading to cross-business collaboration.

Regulatory reporting is becoming more prominent. What is the role of data management?

When it comes to regulatory reporting, there are several fundamental steps that need to be taken: sourcing and validating the relevant data; creating an accurate and complete report; and distributing this information in a timely way. Data management plays a crucial role, ensuring each of these steps is executed accurately and efficiently.

Regulatory requirements often evolve, and data management is a mechanism to enable scale when required, as well as providing transparency and confidence to stakeholders. Participants must ask themselves – Do I trust the data I am reporting? Do I understand what I am disclosing? What are the consequences of misreporting? We have seen this for the past decade in the financial services industry, and those that implement robust reporting capabilities are able to comply with more transparency and ease as new requirements emerge.

Isn’t data management just an IT issue? Why should senior managers, leaders and non-tech teams engage with data management within their functions?

To a certain degree, everyone is a data manager and should care about the information that drives their decisions every day – its completeness, accuracy and timeliness. If data is looked at purely from an IT perspective, the needs of other groups (especially the business) are not met. When this happens, business users end up building their own workarounds which are often bespoke and tactical. To become a truly digitalized and data-driven organization, data management and its core principles need to be embraced throughout, with a strong emphasis on collaboration to ensure solutions and practices can serve both short- and long-term needs.

Decision-making in all departments and at all levels revolves around the organisation’s data. Therefore, the governance and management of the data must be performed across all of these areas, as each area will have different requirements and priorities for the data.

Cloud technology is becoming a bigger focus for the industry. How does this relate to data management?

Business environments are continually and quickly changing. The COVID-19 pandemic is a good example of this. Organizations had to move swiftly from in-office to working-from-home arrangements and needed access to information and data to navigate the initial impact. Those that had cloud-enablement strategies and cloud-based data and technology infrastructures were able to weather the storm more smoothly than others.

Cloud-based data management also allows participants to scale up or down their operations more effectively as data and technology demands change – for example onboarding new content or integrating a new decision-support platform. Having your data estate in order (validated, complete and trustworthy) and in a manageable, centralized manner in the cloud is a key pillar for futureproofing your organization.

Cloud enablement also allows organizations to tap into managed services offerings from solutions providers. This means, for example, outsourcing application management, change management and upgrades to a third-party provider. Becoming a data-driven organization does not mean you need to take on the cost of hosting and maintaining technology. Leveraging a cloud-based managed service offering frees your internal teams up to focus on higher-value tasks and innovation, while also reducing the total cost of ownership.

As a hosted and managed services provider, we are continually adding more functionality (such as monitoring, performance optimisation, analytics etc.) to our cloud offerings and these benefits are passed directly to our clients. Finally, with regards to security, cloud and managed services offerings typically offer a more secure and innovative environment than individual organisations can achieve on their own. This is clear from the widespread adoption of cloud and managed services offerings in many industries.

Learn how IHS Markit is helping industry participants extract more value from their data.

Read our white paper about data management’s role in supporting digital transformation.

Join the online seminar 'Digital transformation: using data management to unlock efficiencies in maritime supply chains' to learn: 

  • Organization priorities and strategies in relation to digitalisation
  • Strategies for automating data-intensive workflows
  • Best practices for ensuring data quality, consistency and availability across the enterprise
  • The use of master data management (MDM) techniques for deriving value from data
  • How to use strategic data management as a means of driving transformational business goals and KPIs
  • The full potential of cloud and managed services models
Find out more about the seminar here

 

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