Organizations gather data from various sources, but combining that information can be complicated. An enterprise knowledge graph arranges data as interconnected entities to reduce fragmentation, highlight relationships, and promote clearer insights. This post explains what an enterprise knowledge graph is, how it differs from a standard knowledge graph, its essential components, and the steps to build one. It also examines benefits, practical use cases, and common hurdles, offering a clear picture of how this approach can support strategic objectives across an organization. It concludes with guidance on determining its suitability for different business environments.

What is an Enterprise Knowledge Graph?

An Enterprise Knowledge Graph arranges a company’s data as a network of connected entities, such as products, customers, suppliers, or services. Each entity carries details, and relationships describe how these entities link to one another. By placing information in this structure, organizations can move beyond fragmented tables or departmental databases, reducing the effort involved in searching for related facts.

Building this type of graph typically starts with identifying the central entities that matter—such as orders, projects, or user profiles—then mapping data from various sources into a shared format. Because the graph emphasizes relationships, it can highlight connections that might remain hidden in a spreadsheet or traditional database. For instance, a query on a given product can reveal its supplier, relevant support tickets, and related inventory records.

Most Enterprise Knowledge Graphs focus on data within a single organization. However, there are cases where a knowledge graph may connect information from multiple companies under agreed-upon security and governance rules. These multi-enterprise graphs help stakeholders collaborate on areas such as supply chain management or industry-wide research.

Security and data governance play a significant role in any Enterprise Knowledge Graph. The system may house sensitive information, so it usually includes role-based access controls, data validation rules, and audit trails. These measures help ensure that each department or user only sees what they are allowed to see.

Another advantage lies in how Enterprise Knowledge Graphs scale. As new data sources emerge, the graph can adjust to accommodate additional entities and relationships without extensive rework. This adaptability suits environments that accumulate large volumes of data—such as financial services, healthcare, or e-commerce. Over time, the graph becomes a reference point that supports tasks like analytics, reporting, and strategic planning.

By capturing how internal information links together, an Enterprise Knowledge Graph provides a clear view of operations. It reveals patterns, reduces time spent on manual data gathering, and offers a single framework where teams can share a consistent understanding of the data that drives daily decisions.

Enterprise Knowledge Graph vs. Knowledge Graph

A knowledge graph is a model of information that links entities—such as people, places, or concepts—to show how they connect. Public knowledge graphs often aim for broad coverage, spanning diverse topics. For example, a graph about world history might capture events, dates, and significant figures, linking them in a way that answers wide-ranging questions.

An Enterprise Knowledge Graph, however, usually focuses on a specific organization’s data, reflecting real-world relationships in areas like finance, supply chains, or customer support. Instead of capturing general facts about famous buildings or historical events, it holds detailed records of business transactions, project milestones, and operational processes. These details help employees quickly trace the chain of events behind a sales order, locate relevant documents, or identify which teams need to address a service issue.

Another key difference lies in governance and security. While public knowledge graphs might allow contributions from many sources, an Enterprise Knowledge Graph often restricts data input and updates to authorized individuals or systems. Role-based permissions determine which parts of the graph each user can view, and compliance rules shape how data is stored and shared. This level of control is necessary because enterprise data often includes sensitive or confidential information.

In terms of use cases, a general knowledge graph could power applications like a question-answering chatbot about historical facts. An Enterprise Knowledge Graph, on the other hand, might drive internal analytics or reporting tools that reveal sales trends, pinpoint supply chain bottlenecks, or integrate disparate datasets from various departments.

In summary, both forms of knowledge graphs rely on the idea of representing entities and their links. Yet the Enterprise Knowledge Graph is tailored to the needs of an organization, offering a secure, detailed, and structured view of its operations.

Enterprise Knowledge Graph Use Cases 

Enterprise Knowledge Graphs connect related data, making it easier to see patterns and uncover insights. Here are a few examples of how organizations apply this approach to address real-world challenges.

Customer 360 and Personalized Experiences

Many companies struggle to unify customer data spread across support tickets, marketing tools, and e-commerce platforms. An Enterprise Knowledge Graph links these records under one structure, enabling a comprehensive view of each customer. Teams can trace a client’s history, preferences, and interactions at various touchpoints. This helps personalize product recommendations, marketing campaigns, or support responses.

Supply Chain and Inventory Management

Complex supply networks often involve multiple suppliers, distributors, and logistics partners. By integrating data about orders, shipments, and inventory, an Enterprise Knowledge Graph makes it easier to pinpoint bottlenecks or anticipate disruptions. A simple query might reveal exactly which products are affected by a certain raw material shortage. This level of transparency supports faster decisions on rerouting supplies or adjusting production schedules.

See an example of how PuppyGraph helps create an e-Commerce order exploration & analysis graph.

Fraud Detection and Compliance

In banking and insurance, companies face large volumes of transactions and strict regulations. A knowledge graph can connect account holders, transactions, and relevant regulations to detect unusual patterns. For example, relationships between multiple accounts or entities can highlight suspicious activity that might not stand out in isolated tables. Compliance teams can also track which regulations apply to specific data, reducing the risk of violations.

See an example of how PuppyGraph helps create a P2P payment platform fraud detection graph.

Knowledge Management and Collaboration

Organizations generate valuable content—reports, presentations, and technical documents—but employees often have trouble finding the right resources. An Enterprise Knowledge Graph can link documents to departments, authors, or projects, providing a navigable structure that quickly guides users to the material they need. This approach also reveals expertise relationships, so teams can identify who has relevant insights or past experience.

Healthcare and Research

Medical institutions handle patient records, clinical trial results, and research data. A knowledge graph can link patients to diagnoses, treatments, and outcomes, helping clinicians uncover potential correlations or risk factors. In research collaborations, shared knowledge graphs can highlight common findings, reduce duplication, and foster more effective partnerships.

See an example of how PuppyGraph helps create a patient journey graph.

Product Lifecycle Management

From design concepts to sales and support, products go through multiple stages, with different teams and tools involved. An Enterprise Knowledge Graph links engineering specifications to manufacturing steps, QA results, and customer feedback. This unified view streamlines processes, allowing stakeholders to spot design flaws early or prioritize features based on real user data.

Through these use cases, it’s clear that Enterprise Knowledge Graphs solve a common challenge: connecting scattered information to reveal meaningful relationships. They can enhance day-to-day operations, improve risk management, and open the door to new insights that keep organizations competitive in an ever-changing market.

Components of an Enterprise Knowledge Graph

An Enterprise Knowledge Graph brings together various data sources under a unified structure. It typically includes several core components that ensure reliable ingestion, meaningful organization, and secure access to the information.

1. Data Ingestion and Integration
Different departments often store data in spreadsheets, databases, and external services. An Enterprise Knowledge Graph must collect this data and convert it into a consistent format. This process involves data mapping, cleaning, and enrichment so that each data element can be represented as an entity or a relationship. Tools or pipelines may validate incoming records and handle errors to maintain accuracy.

2. Semantic Model (Ontology or Schema)
A semantic model defines the types of entities (e.g., “Customer,” “Product,” “Project”), the properties they carry (e.g., “Name,” “Status,” “Location”), and how they relate to each other. In many cases, standard vocabularies or industry-specific ontologies guide this process. A well-designed model captures the essential business logic, ensuring that each data point in the graph has a clear purpose and place.

3. Graph Data Store
Behind the scenes, a graph database (or a compatible data store) holds nodes (entities) and edges (relationships). The storage system should handle queries efficiently, even at scale. Whether deployed on-premises or in the cloud, the graph data store needs to manage large volumes of data while maintaining performance.

4. Security and Governance
Enterprise data can include confidential records. Role-based permissions determine who can view or edit certain parts of the graph. Governance policies establish processes for adding, updating, and deleting data. Logging and auditing tools track changes, while compliance requirements (like GDPR) may influence how personal or sensitive data is treated.

5. Query and Analytics Layer
Users explore the Enterprise Knowledge Graph by running queries that traverse relationships, returning insights like “all orders linked to a specific supplier” or “every project involving a certain employee.” Advanced graph analytics can uncover patterns, detect anomalies, or measure centrality in a network. These capabilities support decision-making across departments.

6. Visualization and User Interface
Finally, many organizations build dashboards or visual interfaces to help non-technical users understand and navigate the graph. Graph visualizations display entities and their links, allowing stakeholders to click on a node and reveal relevant details. This layer often boosts adoption by making the data accessible to a wider audience.

Together, these components form the backbone of an Enterprise Knowledge Graph, creating a structured environment that captures how data points connect and supports a range of business objectives.

Challenges in Building Enterprise Knowledge Graphs

Building an enterprise knowledge graph presents several technical hurdles, particularly for data engineers. Some of the key challenges include:

• Error-prone ETL Process: Extracting, transforming, and loading data from multiple sources into the knowledge graph can introduce inconsistencies, errors, or missing data. Ensuring data is properly cleaned and aligned requires significant validation and testing to avoid propagating mistakes through the graph.
• Need for a Specialized Graph Database: Traditional relational databases aren’t suited to handle the complex relationships in a knowledge graph. Graph databases are essential for performance and flexibility, but they require specialized knowledge to set up and maintain, as well as ongoing resources to scale efficiently as data grows.
• Performance and Scalability: As the volume of data increases, maintaining high performance becomes critical. Optimizing graph traversal, indexing, and partitioning strategies are necessary to ensure the graph can handle large datasets while delivering fast queries.
• Complexity of Data Modeling: Designing a schema that effectively represents entities and relationships is challenging. Poorly structured graphs can result in inefficient queries and maintenance difficulties, making it essential to carefully plan the schema upfront and adapt it as the business needs evolve.
• Schema Changes and Impact on ETL: Schema changes are inevitable as business requirements shift. Modifying the graph schema requires significant updates to the ETL process, which can be both time-consuming and resource-intensive.
• Maintaining Data Quality: Data inconsistencies or outdated information can undermine the value of the knowledge graph. Ensuring high data quality through continuous validation and monitoring is essential to maintaining the graph’s reliability and usefulness.
• Integration with Existing Systems: Integrating a knowledge graph with existing software systems, databases, and APIs requires careful planning and often custom development. This integration can be complex and resource-heavy, especially in large, dynamic organizations.

Steps To Build An Enterprise Knowledge Graph

We've explored how enterprise knowledge graphs provide powerful capabilities for integrating and analyzing business data. While traditional approaches require complex ETL processes and dedicated graph databases, you can build similar capabilities directly on your existing relational data using PuppyGraph. 

PuppyGraph is the first and only graph query engine that operates directly on your relational data, eliminating the need for data migration or a separate graph database, while offering massive scalability and sub-second query performance.

For this demonstration, we'll build an e-commerce knowledge graph using the Brazilian E-Commerce Public Dataset from Olist, one of Brazil's largest department stores. This dataset captures the real-world complexity of e-commerce operations, containing over 100,000 orders made at multiple marketplaces from 2016 to 2018. It includes customer information, order details, product data, seller records, and customer reviews—providing rich material for demonstrating how enterprise knowledge graphs can uncover valuable business insights.

Prerequisites and Environment Setup

Before we begin building our knowledge graph, ensure you have the following prerequisites installed:

• Docker and Docker Compose
• Python 3
• curl (for downloading the dataset)

The demo materials are available in our GitHub repository.  You can also find a demo video on our use case website.

Step 1: Data Preparation

First, let's obtain and prepare the Brazilian E-Commerce dataset:

# Download the dataset
curl -L -o archive.zip https://www.kaggle.com/api/v1/datasets/download/olistbr/brazilian-ecommerce

# Unzip the downloaded file
unzip archive.zip -d ./csv_data/

# Convert CSV files to Parquet format
python3 CsvToParquet.py ./csv_data ./parquet_data

Step 2: Environment Setup and Deployment

PuppyGraph can be deployed quickly using Docker Compose. Our configuration includes all necessary services for a production-ready environment:

docker compose up -d

The Docker Compose file docker-compose.yaml sets up a multi-service environment for working with Apache Iceberg, MinIO, and PuppyGraph:

• spark-iceberg: a Spark instance configured to work with Apache Iceberg.
• rest: an Iceberg REST server, which provides a RESTful API for managing Iceberg tables.
• minio: a Minio server, which is an S3-compatible object storage server.
• mc: a MinIO client that sets up storage buckets and policies for Iceberg.
• puppygraph: a PuppyGraph instance, which is a graph analytics engine to provide graph database service directly on relational data.

Step 3: Data Import

Now we'll import our data into Apache Iceberg tables. Connect to the Spark-SQL shell:

docker exec -it spark-iceberg spark-sql

Execute the provided SQL commands to create and populate the tables. The commands handle:

• Creating appropriate table schemas
• Importing data from Parquet files
• Handling data type conversions
• Establishing proper relationships between entities

CREATE DATABASE brazil_e_commerce;

CREATE EXTERNAL TABLE brazil_e_commerce.olist_customers (
  customer_unique_id        STRING,
  customer_zip_code_prefix  STRING,
  customer_city             STRING,
  customer_state            STRING
) USING iceberg;

CREATE EXTERNAL TABLE brazil_e_commerce.olist_geolocation (
  geolocation_zip_code_prefix   STRING,
  geolocation_lat               DOUBLE,
  geolocation_lng               DOUBLE,
  geolocation_city              STRING,
  geolocation_state             STRING
) USING iceberg;

CREATE EXTERNAL TABLE brazil_e_commerce.olist_order_items (
  unique_item_id        STRING,
  order_id              STRING,
  order_item_id         INT,
  product_id            STRING,
  seller_id             STRING,
  shipping_limit_date   TIMESTAMP,
  price                 FLOAT,
  freight_value         FLOAT
) USING iceberg;

CREATE EXTERNAL TABLE brazil_e_commerce.olist_order_payments (
  payment_id            STRING,
  order_id              STRING,
  payment_sequential    INT,
  payment_type          STRING,
  payment_installments  INT,
  payment_value         FLOAT
) USING iceberg;

CREATE EXTERNAL TABLE brazil_e_commerce.olist_order_reviews (
  review_id                 STRING,
  order_id                  STRING,
  review_score              INT,
  review_comment_title      STRING,
  review_comment_message    STRING,
  review_creation_date      TIMESTAMP,
  review_answer_timestamp   TIMESTAMP
) USING iceberg;

CREATE EXTERNAL TABLE brazil_e_commerce.olist_orders (
  order_id                      STRING,
  customer_unique_id            STRING,
  order_status                  STRING,
  order_purchase_timestamp      TIMESTAMP,
  order_approved_at             TIMESTAMP,
  order_delivered_carrier_date  TIMESTAMP,
  order_delivered_customer_date TIMESTAMP,
  order_estimated_delivery_date TIMESTAMP
) USING iceberg;

CREATE EXTERNAL TABLE brazil_e_commerce.olist_products (
  product_id                    STRING,
  product_category_name         STRING,
  product_description_lenght    INT,
  product_name_lenght           INT,
  product_photos_qty            INT,
  product_weight_g              INT,
  product_length_cm             INT,
  product_height_cm             INT,
  product_width_cm              INT
) USING iceberg;

CREATE EXTERNAL TABLE brazil_e_commerce.olist_sellers (
  seller_id                 STRING,
  seller_zip_code_prefix    STRING,
  seller_city               STRING,
  seller_state              STRING
) USING iceberg;

CREATE EXTERNAL TABLE brazil_e_commerce.product_category_name_translation (
  product_category_name         STRING,
  product_category_name_english STRING
) USING iceberg;

INSERT INTO brazil_e_commerce.olist_customers
SELECT customer_unique_id, customer_zip_code_prefix, customer_city, customer_state
FROM (
    SELECT *,
           ROW_NUMBER() OVER (PARTITION BY customer_unique_id ORDER BY customer_id) as row_num
    FROM parquet.`/parquet_data/olist_customers_dataset.parquet`
) AS filtered_data
WHERE row_num = 1;


INSERT INTO brazil_e_commerce.olist_geolocation 
SELECT * FROM parquet.`/parquet_data/olist_geolocation_dataset.parquet`;

INSERT INTO brazil_e_commerce.olist_order_items 
SELECT order_id || '-' || order_item_id as unique_item_id,
       order_id, 
       order_item_id,
       product_id,
       seller_id,
       CAST(shipping_limit_date AS TIMESTAMP), 
       price, 
       freight_value 
FROM parquet.`/parquet_data/olist_order_items_dataset.parquet`;

INSERT INTO brazil_e_commerce.olist_order_payments 
SELECT order_id || '-' || payment_sequential as payment_id, * 
FROM parquet.`/parquet_data/olist_order_payments_dataset.parquet`;

INSERT INTO brazil_e_commerce.olist_order_reviews 
SELECT review_id, 
       order_id,
       review_score,
       review_comment_title,
       review_comment_message,
       CAST(review_creation_date AS TIMESTAMP), 
       CAST(review_answer_timestamp AS TIMESTAMP)
FROM parquet.`/parquet_data/olist_order_reviews_dataset.parquet`;

INSERT INTO brazil_e_commerce.olist_orders 
SELECT a.order_id, 
       b.customer_unique_id,
       a.order_status,
       CAST(a.order_purchase_timestamp AS TIMESTAMP), 
       CAST(a.order_approved_at AS TIMESTAMP),
       CAST(a.order_delivered_carrier_date AS TIMESTAMP), 
       CAST(a.order_delivered_customer_date AS TIMESTAMP), 
       CAST(a.order_estimated_delivery_date AS TIMESTAMP)
FROM parquet.`/parquet_data/olist_orders_dataset.parquet` a
JOIN parquet.`/parquet_data/olist_customers_dataset.parquet` b
ON a.customer_id = b.customer_id;

INSERT INTO brazil_e_commerce.olist_products 
SELECT * FROM parquet.`/parquet_data/olist_products_dataset.parquet`;

INSERT INTO brazil_e_commerce.olist_sellers 
SELECT * FROM parquet.`/parquet_data/olist_sellers_dataset.parquet`;

INSERT INTO brazil_e_commerce.product_category_name_translation 
SELECT * FROM parquet.`/parquet_data/product_category_name_translation.parquet`;

Step 4: Modeling the Graph

Access the PuppyGraph Web UI at http://localhost:8081 using the default credentials:

• Username: puppygraph
• Password: puppygraph123

Upload the schema using the provided schema.json file, which defines the schema of the knowledge graph. This schema extracts vertices and edges from the relational tables.

Feel free to check out the graph data in the dashboard.

Step 5: Querying the Knowledge Graph

Navigate to the Query panel on the left side. The Gremlin Query tab offers an interactive environment for querying the graph using Gremlin. Let's explore some practical queries that demonstrate the power of our enterprise knowledge graph.

• City-wise Sales Analysis

g.V().hasLabel('Order')
  .out('OrderToSeller').hasLabel('Seller')
  .map{ it.get().value('seller_city') + ", " + it.get().value('seller_state') }
  .groupCount()
  .order(local).by(values, desc)
  .unfold()
  .limit(10)
  .project('location', 'count')
    .by(keys)
    .by(values)

• Product Category Performance

g.E().hasLabel('OrderToProduct')
  .inV().hasLabel('Product').has('product_category_name')
  .groupCount().by('product_category_name')
  .order(local).by(values, desc)
  .unfold()
  .limit(10)
  .project('category', 'count')
    .by(keys)
    .by(values)

• Seller Sales Ranking in 2017

g.V().hasLabel('Order').has('purchase_timestamp', between('2017-01-01', '2018-01-01'))
  .outE('OrderToSeller')
  .group().by('seller_id')
    .by(values('price').sum())
  .order(local).by(values, desc)
  .unfold()
  .project('seller_id', 'total_sales')
    .by(keys)
    .by(values)

• Product Sales Volume Ranking (by São Paulo's Customers)

g.V().hasLabel('Customer').has('customer_city', 'sao paulo')
  .out('CusToOrder')
  .out('OrderToProduct')
  .groupCount().by(id)
  .order(local).by(values, desc)
  .unfold()
  .project('product_id', 'sales_count')
    .by(keys)
    .by(values)

• Seller Rating Ranking

g.V().hasLabel('Seller')
  .as('seller')
  .in('OrderToSeller')
  .in('ReviewToOrder')
  .has('review_score')
  .group()
    .by(select('seller'))
    .by(__.values('review_score').mean().coalesce(__.identity(), __.constant(0)))
  .unfold()
  .project('seller', 'reviews')
    .by(select(keys))
    .by(select(values))
  .order()
    .by('reviews', desc)
  .limit(10)

Step 6: Cleanup

When finished, stop and remove the services:

sudo docker compose down --volumes --remove-orphans

Conclusion

Enterprise knowledge graphs demonstrate the power of graph-based analysis in data integration and insights, offering organizations a comprehensive view of their business relationships and operations. While traditional approaches require complex ETL processes and dedicated graph databases, organizations can now build similar capabilities using PuppyGraph without the complexity of traditional implementations.

Interested in trying PuppyGraph? Download the forever free PuppyGraph Developer Edition, or book a free demo today with our graph expert team.