For businesses, the rise of AI means faster and more efficient access to information, with a newfound possibility to turn raw data into valuable Business Intelligence.
For users, interacting with an AI tool is straightforward. But beneath this simplicity is an intricate system working to connect the dots.
At the core of this intricate system is a Business Knowledge Graph (BKG), a sophisticated architecture that organizes, links, and contextualizes data, which not only enables AI-driven analytics but transforms data into meaningful, actionable insights.
This is a pivotal element for integrating and interpreting complex data and information. For any company looking to leverage AI, understanding the architecture and mechanism behind a BKG is crucial, not only to implement it correctly but also to use it to its full potential.
This article explores what a Business Knowledge Graph is, including its components and architecture. It also gives practical examples of technologies that can be used to create a BKG, along with recommendations to set one up.
What is a Business Knowledge Graph?
Picture what happens when you're doing an online search. Beyond the links displayed, the search engine returns a variety of related information – locations, product reviews, company descriptions etc, – displayed in a structured format called a Knowledge Graph Panel. It is powered by a Knowledge Graph, a framework that stores and organizes data by linking it with semantic data, allowing seamless integration, unification, and analysis of information. Ultimately, it enables the delivery of accurate and relevant results.
A Business Knowledge Graph is similar in concept but tailored specifically to the needs of a particular business. It’s a network of entities – objects, events, situations, or concepts – stored in a database and visualized as a graph structure.
This graph not only represents the relationship between these entities but also provides a structure for interpreting data in a business context. When applied to an AI-powered Business Intelligence tool, the BKG organizes a business’ raw data and uses machine learning algorithms to create meaningful links between multiple data sources. These links then generate insights that are relevant to a user’s specific request.
If data is the information, the Business Knowledge Graph is the brain that absorbs, processes, and makes sense of it all.
What are the components of a Business Knowledge Graph?
A Business Knowledge Graph is not just a network of data points. It’s a system that captures the relationships between various entities within a specific environment.
Each of the BKG’s core components plays a crucial role in the graph’s functionality:
1. Nodes
Nodes are the entities in the BKG. These could be real-world objects, abstract concepts, or organizational assets. Depending on what a user asks, the nodes could include names, geographical locations, or time periods.
E.g.: If a business wants to know how much the profit margin increased in Q4 2024 in the United States, the nodes are “United States”, “profit margin”, and “Q4 2024”.
2. Edges
Edges capture the connection structure between entities, establishing relationships between them.
3. Labels
Labels are metadata that describe and categorize the nodes (entity) and edges (relationship), making it possible to define relationships within the graph.
E.g. In the example described above, the labels will detect the values of the different nodes, such as “United States” – “country”, “Q4 2024” – “time period”, and label the edges (the connections) with words such as “observe”, “increase”, or “achieve”, to return meaningful results in plain conversation language, in line with the user’s request.
How Business Knowledge Graphs Changed Data Analytics
Knowledge graphs have revolutionized data and analytics by enabling businesses to harness valuable insights.
Data once resided in different systems, such as CRMs or financial databases. This made it difficult to have a unified view of all data available, which required complex ETL processes and schema transformations to integrate these data sources. With BKGs, all data lies under the umbrella of one common graph structure, able to provide a global picture of all business operations.
And while traditional analytics methods treat data as isolated, static objects, graph analytics focus on creating relationships and connections between different entities. In other words, a BKG creates a network of interconnected data, linking previously isolated information. As a result, businesses can extract greater value from their data, revealing hidden patterns, identifying influencers, and exploring sub-networks.
A BKG is essential for analyzing customized business insights and answering questions in plain conversational language, for the following reasons:
1. Contextual Insights
The graph provides a comprehensive view of past and current knowledge of a company’s data, allowing for a thorough interpretation of information. This contextual understanding enables more accurate and relevant insights.
Going back to the example of a business interested in the “profit margin of Q4 2024 in the United States”, the graph will reveal information such as the sales impact of a particular season (e.g. around a holiday), regional sales trends, or the effectiveness of marketing campaigns or promotions.
2. Data Enrichment
Facts extracted from various data sources can be added to the knowledge graph, enriching the analysis and reporting. This means that over time, as more data is incorporated, the graph becomes a more powerful tool able to uncover more trends and make better predictions.
As per the example above, data enrichment could mean incorporating market research reports, which could add insights on consumer behavior for a specific season. It could also include economic indicators, competitor analysis, or supply chain data.
Key Benefits of a Business Knowledge Graph
Through its interconnected structure, a BKG helps businesses uncover actionable insights and optimize decision-making by turning raw data into strategic value. These are some of the benefits that a BKG can bring to business organizations:
1. Streamlined Data Integration
Knowledge graphs create a unified ecosystem by breaking down data silos and linking diverse datasets. With consistent identifiers and flexible schemas, they simplify data integration and make it possible to access fresh insights.
2. Optimized Data Governance
The knowledge graph’s structured semantic layer enhances data governance with standardized metadata and terminology, which improves data quality, accessibility, and collaboration.
3. AI-Ready Framework
Knowledge graphs offer context-rich data that strengthens AI performance. This foundation supports smarter applications and automation, and ultimately helps businesses stay competitive.
4. Scalable and Agile Development
Agile development of knowledge graphs ensures the scalability of data integration. Moreover, the flexible framework of knowledge graphs supports iterative growth, which will keep data infrastructures relevant even as new sources emerge.
How to set up a Business Knowledge Graph
Let’s say your company wants to use an AI-powered Decision Intelligence tool to make better data-driven decisions, here’s how you’d have to set it up and use the BKG:
Step 1: Lay the Foundation
Before diving into the analysis, the BKG needs a foundation, which is made up of three components:
I. A business framework
This is an information database from which the BKG can extract insights. A company can connect data from multiple sources, such as CRM data, customer care data, ERP data or general market data. The interconnected data forms the backbone of the Knowledge Graph.
II. User profile definition
Once your company decides to set up a BKG, you must define and establish user profiles for the different team members. These will determine who can access and use the tool. Typically, there can be two types of profiles: admin or member. The admin is responsible for configuring the tool, and granting permissions and access levels to all other collaborators. Knowledge graphs should adhere to industry regulations, such as GDPR or CCPA, by restricting access to personal or sensitive data.
III. Business Specifics
To ensure that an AI-powered tool provides meaningful insights, it is crucial to insert industry-specific information into the BKG, including jargon, keywords, and other relevant details that the tool should understand and process. This is how it can then return insights that speak in words the company uses.
Step 2: Grow your BKG over time
Once the foundation is set up, the BKG is ready for use. But it doesn’t stop there – the graph continues to evolve and become more sophisticated over time, just like a brain! Here’s how:
IV. Interaction and Learning
The graph makes progress with each user’s interaction. The more users ask questions and explore suggested topics, the more the graph learns and picks up their habits, preferences, and specific interests. As a result, it provides more personalized and relevant insights tailored to each user.
V. Dynamic Growth
The BKG is designed to grow dynamically. As more data is added and as the tool learns from user interactions, the BKG becomes more proficient in providing nuanced and detailed analysis. As a result, the tool remains relevant and effective, and is able to keep up with the business’ changes and the market’s evolution.
The BKG is key to making an AI-powered tool work. By connecting all of a company’s KPIs in one conversation interface, AI tools can then guide users through their company data, notify them about important changes, and ultimately make data-driven decisions.
How to choose the technology to create a Business Knowledge Graph
These are some of the tools and tech that can help build and manage a BKG.
1. Memgraph
Memgraph is a real-time graph database platform designed to build and manage graphs. It offers features such as high-performance data processing, real-time analytics, and the ability to handle complex graph structures. Memgraph is particularly useful for businesses that need to analyze large volumes of interconnected data quickly.
2. Amazon Neptune
Amazon Neptune is a fully managed graph database service provided by AWS, supporting both property graphs and Resource Description Frameworks (RDF) graphs. This makes it versatile for different types of knowledge graph applications. Neptune is ideal for companies needing a scalable solution to manage their knowledge graph.
3. Type DB
Type DB is a knowledge graph platform that uses a hyper-relational model to represent complex data. It’s particularly suited for scenarios where relationships between data points are intricate and multidimensional.
4. Neo4j
Neo4j is known for its robust and flexible graph databases, making it ideal for business knowledge graphs that can scale with the business’ needs.
How the BKG works on Crystal
On our flagship Decision-Intelligence product, Crystal, the BKG serves as the foundation for addressing our users’ business questions, just like an information network of interconnected metadata, derived from private data sources.
For each project on Crystal, the BKG is unique, isolated, and responsible for extracting information from metadata for AI models to transform information into conversations.
On Crystal, the BKG gets built during the configuration process, as soon as users select their data sources, define their business entities and their semantics (alias). These choices are fundamental for the BKG to “learn” the business language, taxonomy, and specificities. After grasping the business thoroughly, it will be able to form semantic connections within the knowledge base, interpret data, and generate responses.
With time, the BKG evolves based on the requests received on Crystal. It will identify new associations between data, learning from users, and providing accurate, contextualized, and certifiable answers every single time.
From both a business and a technical standpoint, a Business Knowledge Graph is far more than just a great data analytics enhancer – it’s a strategic investment that can contribute to the long-term success of a business organization.
By understanding the architecture, implementing the right technology, and continuously refining the system, companies can unlock unprecedented value from their Decision Intelligence tools, securing a competitive edge in an ever-evolving digital landscape.
Frequently Asked Questions
Yes, they can. As a matter of fact, emerging research demonstrated that the synergy between LLMs and BKGs help create AI systems that are more contextually aware and accurate. Combining the structured knowledge of a BKG with the contextual language processing of LLMs enhances both data understanding and extraction. While BKGs organize data into relationships, LLMs interpret and generate insights from unstructured queries. With techniques like Retrieval-Augmented Generation (RAG), LLMs pull real-time, fact-based data from BKGs, reducing inaccuracies and hallucinations. As a result, BKGs and LLMs form AI solutions that enable users to ask questions in natural language and get accurate, dynamic, and up-to-date answers.
Integrating Business Knowledge Graphs (BKGs) with Large Language Models (LLMs) addresses key limitations of LLMs, such as factual inaccuracies and hallucinations, by providing structured, symbolic knowledge. BKGs organize and link entities in a way that enables more accurate, contextually relevant outputs for tasks such as question-answering, sentiment analysis, and reasoning. When LLMs query BKGs in natural language, they access real-time, fact-based information, which ensures better data retrieval and therefore reduces errors, making it befitting for regulated industries, such as finance, legal, or healthcare.
Yes, Business Knowledge Graphs can be tailored for specific industries by integrating data relevant to their unique processes, regulations, and vocabularies. They provide significant value by organizing and connecting industry-specific information, enhancing decision-making and operational efficiency. Common applications span across sectors like finance, healthcare, retail, and manufacturing, where these graphs help manage relationships, improve risk assessments, and optimize operations. Each industry's graph is customized to address its distinct data landscape, leading to more informed decisions and better business outcomes.
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