Transforming enterprise data into a unified whole

To revolutionize how enterprises manage data, we must rethink how we structure and understand it. The traditional methods are quickly becoming insufficient, and the key to this transformation lies in the Enterprise Knowledge Graph. This graph offers a simple yet powerful way to organize and connect data.

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A graph is a straightforward concept. It consists of nodes (or vertices), which represent individual data items, and edges, the connections between them. Think of nodes as circles and edges as the lines linking them. This intuitive structure captures the inherent messiness of data while maintaining enough order. Graphs embody a “structured scruffiness” that makes them versatile and capable.

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Unlike traditional data structures like tables (used in Excel or relational databases) or trees (such as folder systems or JSON/XML formats), graphs are uniquely flexible. They can model tree-like hierarchies, tabular data, and even complex, interconnected concepts like relationships in images or text. Importantly, graphs don’t replace these structures but instead provide a universal way to represent and connect them.

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What truly sets graphs apart is their emphasis on relationships. In graphs, relationships are first-class citizens. It means that the connections between data points (nodes) are as important and explicit as the data points themselves. Unlike traditional data models, where relationships are often hidden or secondary (e.g., stored as metadata or implied through table joins), knowledge graphs directly represent relationships as edges. This approach makes the connections both visible and queryable, enabling deeper insights and a richer contextual understanding of how data points interact.

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Data is inherently relational; facts do not exist in isolation but gain meaning through their connections. This relational perspective is vital for enterprises looking to pivot from siloed, business-aligned systems to a holistic, interconnected view of their data.

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While relational databases or other data stores can mimic these capabilities, graphs are purpose-built for this kind of work, making it effortless to model data in the form of three-part statements (e.g., "Ben is a person," "Bob is Ben’s son," "Bob lives in the UK"). By breaking enterprise data into such atomic facts, you can seamlessly construct a vast, interconnected graph.

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What is the difference from a relationship diagram?

At first glance, graphs and relationship diagrams might seem interchangeable. Both involve nodes and connections, representing data and its relationships. However, their purpose, structure, and applications reveal fundamental differences that make them suited for distinct tasks in data management and analysis.

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Knowledge graph in Dawiso: A Step Beyond Relationship Diagrams

The introduction of the Knowledge Graph in Dawiso marks a significant evolution from the traditional relationship diagram. While the previous relationship diagram was a useful tool for visualizing linear connections between objects stored in Dawiso, it ultimately fell short in providing a comprehensive view of how data and relationships are truly interconnected. The Knowledge Graph addresses these shortcomings, transforming the way users can explore and understand the relationships within their data ecosystem.

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From Linear Relationships to a Connected Network

The earlier relationship diagram excelled at mapping linear relationships. It provided a straightforward view of an object and its directly connected elements, acting as a “map” of Dawiso's stored knowledge. For example, if you had a business term linked to another term, such as Product, the diagram would visually present these relationships in a clear, tree-like structure.

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However, complexities arose when these relationships became multi-layered or circular. Consider the following example:

• The term Electronic links to Product, but Laptop—a subset of Electronic—also links back to Product.
• Furthermore, Product might link to a Supplier, which includes details like the name “Dell” and specific products like the XPS Laptop.
• The XPS Laptop, in turn, links back to Product, creating overlapping and interconnected relationships.

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In a linear relationship diagram, this web of connections can become cluttered and harder to navigate, as it tries to fit everything into a hierarchical tree structure. The result is a loss of clarity and an inability to fully represent the rich interconnections between terms and objects.

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The same situation in knowledge graph:

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Why the Knowledge Graph is Superior

The Knowledge Graph addresses this limitation by shifting from a linear representation to a networked one. Instead of forcing data relationships into a tree structure, the Knowledge Graph creates a web of connections, linking objects wherever relationships exist. This approach has several advantages:

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1. Comprehensive Connectivity

Relationships are no longer constrained to a single hierarchy. For instance, the XPS Laptop can simultaneously link to its Supplier, its category under Electronic, and directly to Product, without the restrictions of a linear path.

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2. Cross-Space Connections

The Knowledge Graph enables linking not only between objects but also across different contexts, such as shared Spaces. For example, if a dataset in one space is being filled by data from another, the graph visually represents this cross-space connection, providing a deeper understanding of dependencies and collaboration.

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3. Visual Clarity

The networked visualization of the Knowledge Graph reveals how objects are organized within Dawiso. By connecting related objects directly, it eliminates the need to navigate through intermediary steps, making complex relationships more intuitive and easier to explore.

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A More Intuitive Map of Data Relationships

The Knowledge Graph transforms Dawiso into a true map of all data and relationships within the platform. It provides users with a dynamic, visually rich representation of how their data is interconnected. Beyond simply replacing the relationship diagram, it redefines how users interact with their data, offering a level of clarity and flexibility that was previously unattainable.

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This move from a linear diagram to a graph-based structure not only reflects the growing complexity of modern data systems but also empowers users to uncover deeper insights and foster collaboration across their data ecosystems. By leveraging the Knowledge Graph, Dawiso continues to innovate, making data management more accessible, intuitive, and powerful.

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