How to migrate data from one technical management system to another
Today, most maritime companies rely on digital systems for technical management. While some use basic Excel sheets, others leverage advanced integrated software solutions. As it varies widely, it is hard to set a concrete example that will be true for all, but there are some main considerations that you need to take into account when planning your migration and choosing a supplier.
Regardless of the system in place, they all share a common goal: to protect, reuse, and maximize the value of their data during migration.
Data migration is a critical process when transitioning between digital solutions, and the process involves harmonizing data across the fleet and performing data cleansing to improve accuracy and efficiency.
The illustration above shows a generalized process of how we carry out data migration for systems in regards to maintenance, procurement, HSQE, logbook, vessel reporting and performance.
In this article, we explore the most common challenges in maritime system migration and provide key recommendations to ensure a smooth and effective transition.
Who owns your maritime vessel data?
The technical and economic challenge of extracting data from an existing system.
A company recording their data within a system is usually the rightful owner of the data. They have a contractual agreement with the supplier, outlining data ownership as the creator, and the supplier’s responsibility for confidentiality and data security as the host.
Although the company owns the data, extracting it from an existing system can be both technically and financially challenging, particularly depending on the type of data hosting.
In some cloud environments, direct access to data is restricted. Instead, access is only possible through the user interface, which is not designed for data extraction or migration.
Key elements and critical data identifiers (IDs) may not be extractable. Additionally, some suppliers include contractual clauses that make data extraction difficult or even impossible.
This means that, even if a shipping company has created and stored all its own data, it may not be permitted to extract or use it when transitioning to a new system.
- Always review your contracts carefully to ensure that you have the rights to your data and the ability to extract it when needed.
The vessel data structure
A well-structured maritime data framework is key to efficient operations. Avoid spending excessive time building your data from scratch—focus instead on ensuring a logical and structured approach.
When maritime companies manage their data, they often encounter these common pitfalls:
- Too much flexibility, leading to unstructured data
- Copy-pasting standard data
- Overcomplicating data, making it difficult to manage
Too much flexibility, leading to unstructured data
If users have unlimited freedom to input data as they see fit, data quickly becomes inconsistent and unstructured. While flexibility may seem beneficial on an individual level, it often results in a lack of standardization across the fleet.
This becomes a challenge when migrating to a new system, making it essential to establish clear data structuring guidelines from the start.
Copy pasting standard data
Some systems come with built-in standard data, which can be appealing for companies starting from scratch or working with minimal data. You can relatively quickly build up some form of data structure and although it is not tailor made for you, it gives an easy start.
While this approach provides a quick and easy foundation, it raises a critical question: Who owns the original data? If your data is preloaded by a supplier, you may face restrictions when migrating to a new system. Ensure that you have the right to modify, extract, and transfer your data when needed.
Overcomplicating data, making it difficult to manage
Some maritime companies aim for extreme precision, leading to overly complex data structures that become difficult to manage. If your data is too intricate and your system lacks proper data exporting tools, extracting and mapping data for migration can become a major challenge.
Striking the right balance between detail and usability is crucial to ensuring smooth operations and future scalability.
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Maritime vessel data migration principles
Migrating data from one maritime system to another is a complex but essential process. To ensure a seamless transition, companies must follow a structured approach to maintain data integrity, consistency, and usability. Below are the key steps in a successful maritime data migration.
Extracting vessel data from the existing system
The first step in data migration is to retrieve data from the current system. If you are using a structured and professional system, this process is usually straightforward, as the data follows a clear and logical model. However, companies using closed or proprietary systems may face obstacles that require technical expertise and legal consultation before proceeding.
Key challenges to consider:
-
Data accessibility
Some systems limit data extraction, requiring workarounds or additional permissions. -
Data structure
Extracted data must follow a logical format to be usable in the new system. -
Contractual limitations
Certain vendors may restrict data exports, making migration more difficult.
Cleaning and standardizing vessel data
Data migration is an opportunity to improve and optimize the data structure. Before transferring data to a new system, it is crucial to:
-
Remove outdated or irrelevant data
Old records that no longer serve operational purposes should be archived or deleted. -
Ensure consistency across datasets
Standardizing naming conventions, formats, and units of measurement enhances data usability. -
Harmonize key records
Aligning item, component, and maintenance data across the fleet ensures better integration in the new system.
Cleaning data not only improves accuracy and efficiency but also prevents errors that could cause operational disruptions after migration.
Moving vessel data to a neutral staging area
Before data is imported into the new system, it should be transferred to a neutral staging area. This intermediate step allows companies to:
-
Step 1
Apply standardized data correction rules before migration. -
Step 2
Check for inconsistencies, missing values, or duplicate entries. -
Step 3
Ensure alignment with the new system’s structure and operational framework.
A structured staging process enhances data quality while reducing the risk of errors, misalignments, and compatibility issues before the final migration.
Validating vessel data against business rules
Once in the staging area, data must be tested for accuracy and compliance with business rules. This ensures the data supports operational needs, regulatory requirements, and internal standards.
Validation includes:
-
Integrity checks
Ensuring data relationships remain intact (e.g., linking components with corresponding maintenance records). -
Format verification
Confirming that fields follow required formats (e.g., standardized part numbers, dates, and units). -
Duplicate detection
Identifying and removing redundant entries to prevent inconsistencies. -
Regulatory compliance
Ensuring the data aligns with maritime industry standards and safety regulations.
A robust validation process ensures data accuracy, reliability, and readiness for seamless integration into the new system.
Importing vessel data into the new system
The last step is to import data into the new solution. Although this might sound simple, it requires very strict principles. In many cases data is shared between the office and the individual vessels, so the sequence in which data is imported into the new system is important.
Key considerations:
-
Data sequence matters
In many maritime systems, data is shared between office locations and vessels. The order of data import must be carefully managed to prevent inconsistencies. -
System compatibility
The format and structure of imported data must align with the new system’s data model. -
Error handling protocols
A strategy should be in place to detect and correct any migration issues before they impact operations.
Data migration is often an iterative process, meaning it may need to be repeated multiple times before all business rules, data cleansing processes, and formatting adjustments are fully refined.
A successful migration does not end with data import. The final step is to test the data within the new system to ensure it functions as expected.
To minimize risks and ensure efficiency, this process should be automated wherever possible. Manual intervention should be limited to critical validation and exception handling.
Maritime vessel data conversion
Data conversion can be necessary to ensure that data from the source system is compatible with the target system’s format, structure, and requirements. Data conversion resolves differences in data types, formats, and standards between the source and target systems, reducing integration challenges and enhancing system performance.
Automatic vessel data conversion
Automatic data conversion refers to the process where data is converted from one format to another using software tools or systems, without manual intervention. This method is typically faster and more efficient, as it allows for bulk data transformation in a structured manner. However, it requires that the source and target systems are relatively aligned in terms of data format and structure.
Manual vessel data conversion
Is your data suitable for conversion, or does it require cleaning?
Manual data conversion is often not a true data migration but a last-resort solution when automated conversion is not feasible. If the quality of digital data is too poor due to inconsistencies, missing values, or lack of structure, you may need to rebuild its dataset entirely.
Several companies offer this service, and some deliver impressive results for a reasonable price.
It is also possible to have a combination meaning that some data is electronically converted, and others are manually established or cleaned from existing data.
Data conversion types
To better understand the different methods used in data conversion, we will explain the various types that are commonly applied. The following table outlines the key types of data conversions with examples.
| Data Conversion Type | Definition | Example |
|---|---|---|
| Data Type Conversion | Changing the data type of a field or column to meet the system's requirements | Converting a cargo weight from a string (e.g., "five tons") to an integer (e.g., 5) |
| Format Conversion | Changing the format of data to match the target system’s standards | Converting date formats from DD/MM/YYYY to YYYY-MM-DD for ship logs |
| Schema Mapping / Transformation | Mapping data fields between different maritime systems, such as ports and maritime companies | Mapping a vessel_name field in the source system to ship_identifier in the destination system |
| Data Cleansing / Standardization | Ensuring that data is accurate, consistent, and standardized | Standardizing vessel names by correcting misspellings (e.g., "Sertica" to "SERTICA") in a fleet database |
| Unit Conversion | Converting units of measurement to match the target system | Converting distances from nautical miles to kilometers for reporting |
| Encoding Conversion | Changing the encoding of data to ensure it is readable and compatible with the target system | Converting vessel data from ISO-8859-1 encoding to UTF-8 to support international characters in ship names |
| Data Aggregation | Summarizing or combining data for analysis or reporting | Aggregating cargo shipments by port, month, or year for performance reports |
| Data Splitting | Breaking down data into smaller parts for processing in the target system | Splitting a ship’s cargo manifest into individual item descriptions, quantities, and destinations |
| Data Enrichment | Adding external maritime data to improve decision-making and operational insights | Adding weather data (e.g., wind speeds, storm forecasts) to vessel routes for better planning |
| Normalization / Denormalization | Adjusting the data structure to either reduce redundancy (normalization) or enhance performance (denormalization) | Normalizing a fleet database by separating ship schedules and cargo records into distinct tables. Denormalizing for fast retrieval of complete shipping schedules with cargo details |
| Data Compression | Reducing the size of data to optimize storage and transfer | Compressing ship performance logs for efficient transfer during data migration |
| Data Encryption / Decryption | Ensuring the security of data during transit and storage | Encrypting sensitive cargo information or ship routes to prevent unauthorized access |
Ensuring correct data conversion is crucial for the success of your data migration, choosing a partner for your conversion is therefore an important part in the process. Learn about our data conversion services here.
Master Data Management (MDM)
Is your data fragmented and lacking centralized control?
For many shipping companies, data is accumulated over years or even decades with minimal central guidelines. Without standardized guidelines, it becomes difficult to maintain consistency, enforce best practices, and compare data across vessels.
Migrating to a new system is an ideal opportunity to introduce a more structured approach through Master Data Management (MDM).
It is not a simple task to establish Master Data from fragmented data, but it can be worthwhile to consider, as the benefits from a centrally controlled data repository are huge.
Benefits of Master data management
-
Consistency across the fleet
Reduces discrepancies between ships. -
Improved data analysis
Enables accurate comparisons and trend analysis. -
Stronger compliance
Aligns data with regulatory and safety requirements. -
Optimized efficiency
Minimizes duplication and enhances operational workflows.
Implementing MDM is not a simple task, especially when data is highly fragmented. However, the long-term benefits of a centrally controlled data repository far outweigh the challenges.
It can also be a combination, so only widely used equipment is control centrally and more unique equipment is converted specifically for each ship.
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Data Migration recommendations for maritime vessels
If you are planning to migrate your data from one system to another, we recommend that you always select a system/supplier that:
- Enables you to take control of your own data.
- Has a proven track record and tools to migrate existing data into their solution.
- Do not only convert your data, but also capable of bringing more value to your data in form of cleansing, harmonizing and/or restructuring.
- In one way is flexible in data structure, but at the same time let you define the way you want data to be structured across your fleet.
A clear strategy for the migration process is important and with a proper implementation, the shipping companies will be more prepared to harvest the future possibilities of machine learning and AI.
With unstructured data it will be unrealistic.
