How Good RDM Platforms Can Support Digital Twins
- Alain Lai
- Jun 4
- 4 min read
Updated: 3 days ago
In the age of technology, digital twins are innovative tools used by organizations to provide unparalleled information about an asset. At a high level, digital twins are virtual model replicas of physical assets, artifacts, or entities. These models are useful in helping organizations simulate real situations and forecast outcomes, helping inform decisions and educate decision-makers. For example, the U.S. home improvement retailer Lowe’s has adopted this technology to create digital duplicates so select stores, inputting key insights such as spatial data, product location, and order history to identify methods to improve store operations and maximize customer experience. This has empowered Lowe’s to accomplish impressive results, including the optimization of their store layouts to enhance sales, the monitoring of individual store operations from headquarters, and the ability to proactively identify risks.
However, to effectively capitalize on these digital models, accurate and reliable data must be integrated. This is where robust data management, or RDM, is indispensable. Digital twins often rely on large volumes of reference data. In a supply chain context, for example, such data may include things such as product barcodes, currency types, and units of measurement, to accurately simulate real-world activity. If any of this data is inaccurate or falsely interpreted, the results extracted from the digital twin may become corrupt. For example, if the size data of different products were erroneous, the digital twin may recommend suboptimal shelf space layouts, which ultimately hurt sales. To mitigate these risks and create a model that can be relied upon, companies like Lowe’s must leverage a robust “Reference Data Management (RDM) platform” to ensure that all data inputs are standardized and maintained with integrity. Due to this relationship, an effective RDM platform is the core foundation that empowers digital twins to produce valuable insights.
Let's explore an overview of how these two concepts are interconnected:
Encouraging Data Quality
Digital twins often require data from a variety of sources to create a holistic replication of their target. Otherwise, there can be devastating consequences, such as an oversight of certain possibilities, leading to an ill-informed observation and/or conclusions. . As such, the extracted information from each of its sources must be consistent, relevant, timely, and accurate. In this situation, it is the RDM platform's task to harmonize all these data sources, ensuring that every input, such as an asset or transaction, is entered in a manner appropriate for its processing, regardless of origin.
Real-Time Data Synchronization
A digital twin’s core functionality is its ability to represent the current state of its physical counterpart. To accomplish this, an RDM platform serves as the intermediary that standardizes and governs the flow of incoming data streams, allowing these valid inputs to be processed by the digital twin. This allows the model to reflect the current state of the physical entity without delay, enabling the user to monitor the performance and detect errors instantaneously.
Composition of Data Across Systems
Modern enterprise management platforms such as Workday or SAP rely heavily on a complex ecosystem of platforms, devices, and data design rules. It is akin to languages, where each platform speaks a language that external parties can not understand. In this context, an RDM platform can be seen as an efficient translator that can understand all these diverse languages and act as a translator to allow these parties to communicate and share data.
A great example of an RDM platform’s importance is during the construction of Brisbane’s first underground railway project, which was Queensland’s largest infrastructure initiative in history. During the development, this project required the consideration of complex data streams for construction, scheduling, and maintenance systems, and they each had unique identifiers and standards. During this operation, the RDM platform ensured that critical reference inputs such as the identifiers for ongoing piping operations or construction elements were consistent across all involved systems. This ultimately enabled the development team to gain a clear snapshot of the present state and a glimpse into the future, empowering all stakeholders to be more informed.
Enhancing Scalability
As organizations begin to scale, their digital twins will similarly need to align with this growth. Therefore, new assets, processes, and data inputs are continuously added to simulate real world conditions. This can only be substantially enabled by RDM platforms which can ingest data from a variety of sources and jurisdictions, and can provide the scalability required to transform new data into insights that can be integrated into the existing model. Without a robust RDM system, a digital twin, even if initially accurate and relevant, will face roadblocks in remaining aligned with changes of its physical counterpart, ultimately producing an unreliable representation.
Overall, as reliable digital twins slowly become an indispensable source of competitive advantage for organizations in a number of sectors, the importance of having a strong data management platform that can continue to scale will become more important. With a strong RDM, researchers and organizations are empowered to create higher quality digital twins, enabling better decision-making built on accurate and consistent data. To truly leverage the power of a digital twin and drive efficiency, innovation, and data-driven decision making, a thorough data management plan and a strong RDM platform are non-negotiable.
myLaminin is a blockchain-enabled generalist research data management solution that delivers holistic institutional governance and control of research data. myLaminin empowers organizations with full control over their data and can decide exactly where their data is stored, whether that be on-premises or in the cloud. This flexibility is important when the number of data sources and volumes of data increases.
With immutable audit trails and data integrity, myLaminin’s foundation can guarantee that all actions taken on operational data, such as the input of new data, are permanently logged in a consistent format. This traceability also helps digital twins, as the accuracy and completeness of historical data directly impact the quality of simulation.
Lastly, with myLaminin, organizations can ensure that they meet regulatory and ethics board standards, even in situations where digital twin projects are cross-border or multi-institutional. This transparency will promote trust across different stakeholders and lessen the documentation required for complex operations.
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Alain Lai (article author) is a myLaminin intern studying Business Administration at Wilfred Laurier University.
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