Data Fabrics advantageous for the Metaverse?
What makes Data Fabrics advantageous for the Industrial Metaverse?
So what exactly is a data fabric? Data fabrics are the best means of storing, networking, and contextualizing data that we have today. Data fabrics give us the best shot of getting to this highly connected Industrial Metaverse. So what are the key features and advantages of data fabric architectures?
Let’s step back for a minute and look into how data has been conventionally stored and used. Typically and traditionally, data has been stored largely in rows and columns in tables. Data is stored parameterized in columns which may be used to describe salient characteristics for example of a building, its height, the number of floors, the year it was built, construction materials used, how many people live in that building. All this information is stored in some form of tabular format tabular database in a row-column structure.
The need for conventional data structures began with programming languages that needed to be able to cycle through rows and columns for expediency in while-loops, if-then statements, and the need to increment and decrement in batch. The right data structures enables the utilization of efficient programming languages of the early Internet and provided the main method to iterate within easily traversable tabular structures. But we have evolved way past this point. Similarly, the right data structures will enable the Metaverse and its constituent parts (avatars, digital twins).
Data Fabrics
A data fabric architecture 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. There are several key advantages to using a data fabric architecture:
Scalability: A data fabric architecture can scale horizontally, allowing it to easily handle large volumes of data and support a large number of users and applications.
Flexibility: Data fabric architectures are highly flexible, allowing organizations to easily connect and integrate new data sources and systems as needed.
Data governance: Data fabric architectures 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 fabric architectures can support real-time data integration, allowing organizations to quickly and easily access and use the most up-to-date data.
Data security: Data fabric architectures 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.
Overall, the use of a data fabric architecture can provide organizations with improved data management, integration, and governance capabilities, as well as greater scalability, flexibility, and security.
Data Architectures of the Metaverse: From Rows & Columns to Data Networks
It is clear that change is needed upstream of those algorithmic mechanisms that give rise to artificial intelligence. Data management in the Metaverse will likely predominantly be graph-based, running primarily on GPUs, distributed, massively parallel, and federated. Many have also concluded that data management (and standardization) forms one of the major obstacles in the realization of the Metaverse in the near-immediate term:
"[T]he creation and effective use of a metaverse or mirrorworld will be data (and knowledge, which is contextualized data with supporting relationship logic) management. More specifically, to be useful and accurate, interactive digital twins demand web-scale of interoperability–not just application scale."
How we manage data has been a direct function of how we utilize that data, and this has been seen throughout the history of data management. For example, higher-level programming languages such as FORTRAN, COBOL, C, C++ were all predicated on the regularized tabular data structures that they were manipulating. ETL then came around in order to collect data from different sources and convert them into a consistent form - to be stored in data warehouses. SQL then focused on relational databases and relational data models - unified languages for navigating, manipulating, and defining data. NoSQL formed a leap towards 'big data' and being able to store, manipulate, and search vast quantities of structures and unstructured datasets. This then gave rise to data lakes and the ability to store a large mass of unstructured datasets for downstream consumption into analytics.
And part of the problem again are the collective psychologies, and the way that data has been managed overtime via: poor architectural design choices, legacy, point-to-point integrations, custom code, data siloes, application-centric stacks, and increasing complexities (SaaS, cloud, tools, services, IT/OT/IoT).
But where does this leave us now? Do we have the prerequisites for the connected Metaverse in terms of foundational data architectures? What requirements will need to be encapsulated by these new architectures?
Plug & play connections
Interaction logic between datasets
Dynamic data models
Contextualized datasets
Data enrichment
"Knowledge graphs are ideal for this purpose. At the heart of knowledge graphs are contextualized, dynamic models that allow data enrichment and reuse in the form of knowledge graphs, graphs of instance data, rules and facts that can be easily interconnected and scaled. These knowledge graphs can make interoperability between larger and larger systems possible. Think of it this way: more and more logic is ending up in knowledge graphs, where it can be reused for interoperability purposes. Once you’ve done the hard work of making the graph consistent, graphs and subgraphs can be snapped together, and you can create data contexts, rather than just data monoliths."
And there are many companies today that are moving towards this shift, re-imagining their enterprise architectures to support what comes next, whether its Thomson Reuters' financial knowledge graph-as-a-service or Airbnb's knowledge graph to surface relevant context to people - the largest companies by market capitalization are all not only investing in the analysis of data, they are firmly investing in those data structures that will support richer man-and-machine interactions.
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