Today’s Linear Technology Stacks
One of the first foundational technical prerequisites for the Industrial Metaverse is the need for digital twins that precisely simulate reality in a reactive real-time bi-directional, synchronous, high-fidelity, closed-feedback-loop process. Therefore, while the word “digital twin” represents imprecise terminology, its true definition requires far more adjectives. Such futuristic “digital twins” would thereby act as virtual reality representations of assets, systems, and networks.
Digital Twin Characteristics
Real-time
Reactive
Bi-directional
Synchronous
High-fidelity
Closed-feedback-loop
Precise simulation
And while the digital twin is the foundational and constituent building block for the Industrial Metaverse, a digital twin will not run without the right data and data architecture. Data management and data architectures to enable such digital twins is such an important concept, that without the right framework, the true Industrial Metaverse will never be achieved.
We need to rethink our approach to both data management and data architectures. Today, there is an inherent linearity and pipeline nature of technology architecture stacks. Again, data traverses something like a pipeline. And at each stage of the technology stack, value is presumably added to that data. Inevitably, at the end of the stack funnel, value adding steps should ultimately add to some business value, whether that business value is ROI, cost, profit, FTE or any other KPI that can mathematically be represented.
Raw data inputs enter a technology stack and are continuously manipulated across a value chain, until there is some realization of economic benefit that is instantiated by that data. In this view, data is treated like a pipeline, not as a network. This conundrum reinforces the linearity of data stacks and the nature of the composition of technology that we have today. There is typically a layer for data acquisition, a layer for data quality, a layer for data aggregation, a layer for data integration, a layer for data management, and then maybe data is then organized in some form of structural database.
These days, there's a lot of conversation around data lake and data lake strategies, dumping data, and connecting various data sets within the context of a data lake or an enterprise data lake.
And then once this data of various forms and formats is organized in some way, then off it goes into some analytics pipeline with various steps and stages where the end-result is finally orchestrated in a dashboard, tool or application.
This represents our conventional thought processes and our linear thought processes around data management and data architectures. But this really needs to change in order to move one step closer to the Industrial Metaverse that will be predicated on the seamless free-flow of data in data fabric architectures and through strategies involving data centricity.
Data Centricity
Data centricity refers to the idea that data is the most important asset of an organization, and that all business processes, operations, and decisions should be centered around and driven by data.
In a data-centric organization, data is treated as a strategic asset and is carefully managed and governed to ensure its quality, integrity, and security. The organization's systems, processes, and culture are designed to prioritize the collection, storage, analysis, and use of data in order to drive business value and decision-making.
Data centricity involves several key components, including:
Data governance: This involves establishing policies, processes, and systems to ensure the quality, integrity, and security of the organization's data.
Data management: This involves the collection, storage, and organization of data in a way that allows it to be easily accessed, analyzed, and used by the organization.
Data analysis and insights: This involves using data analytics and machine learning techniques to extract insights and inform business decisions.
Data-driven decision-making: This involves using data to inform business decisions and strategy, rather than relying on gut feelings or traditional methods.
Overall, data centricity is a key approach to managing and leveraging data in a way that drives business value and enables organizations to make better, more informed decisions.
Data Fabric
A data fabric is a type of data management and integration approach that aims to provide a single, unified view of an organization's data by connecting and integrating data from various sources and systems.
The benefits of using a data fabric include:
Scalability: A data fabric can scale horizontally, allowing it to easily handle large volumes of data and support a large number of users and applications.
Flexibility: Data fabrics are highly flexible, allowing organizations to easily connect and integrate new data sources and systems as needed.
Data governance: Data fabrics can help organizations better manage and govern their data by providing a centralized, unified view of all data and establishing rules for data access and usage.
Real-time integration: Data fabrics can support real-time data integration, allowing organizations to quickly and easily access and use the most up-to-date data.
Data security: Data fabrics can provide enhanced security by encrypting data in transit and at rest, and by providing access controls to ensure that only authorized users can access sensitive data.
Data fabrics are expected to accelerate digital transformation by providing organizations with improved data management, integration, and governance capabilities, as well as greater scalability, flexibility, and security. By making it easier for organizations to access, analyze, and use their data, data fabrics can help organizations drive business value and make better, more informed decisions.
Both concepts of data centricity and data fabrics will be needed in unison in order to connect the landscape of communicating digital twins and algorithms.
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