What Are the Best Graph Database Use Cases in 2025?

Your business runs on connections. Every day, customers interact with products, employees collaborate across teams, and suppliers integrate with your processes. These relationships drive innovation and growth. But there’s a problem — most organizations store this connected data in traditional databases that weren’t built to handle relationships.

What happens? As your data grows, queries slow down. Important patterns stay hidden in separate systems. And the connections that make your business thrive get lost in rigid database tables.

Let’s explore how graph databases help leading companies solve these challenges and get more value from their connected data.

Why Traditional Databases Fall Short

For decades, we’ve tried to fit connected data in relational databases, organizing everything into rigid columns and rows. While this structured approach made sense in an era of expensive storage and limited computing power, it now comes at a steep cost. Modern data often represents complex relationships that don’t naturally fit into tables, forcing organizations to sacrifice flexibility and insight.

Reconstructing data relationships through JOINs and integration is costly and inefficient. This approach only reveals patterns you specifically search for, while missing countless other valuable connections hidden in your data.

The relational model costs more than time and effort — it costs opportunities. Hidden patterns and insights that could transform your business stay trapped in disconnected data structures.

What Makes Graph Databases Different?

Graph databases store data the way you naturally think about it — as a network of connected entities.

Instead of tables, you have the property graph model, which consists of:

• Nodes: Represent things in your data (people, products, orders)
• Relationships: Show how nodes connect to each other
• Properties: Store information about both nodes and relationships

Unlike traditional relational databases, graph databases store relationships natively alongside the data elements. This model delivers three key benefits.

1. Performance stays consistent even as your data grows because there’s no need for expensive join operations. Applications can traverse millions of connections quickly, enabling real-time capabilities that are impossible with traditional databases. For example, Dun & Bradstreet cut their query times from days to seconds after switching to a graph database.
2. Flexibility allows you to add new nodes and relationships without disrupting existing applications. Your data model can evolve naturally with your business needs, making it much easier to connect data across systems and adapt to changing requirements.
3. Insights that would otherwise stay hidden in traditional systems are revealed in graph databases. By storing relationships directly, graph databases make it easy to discover patterns, understand context, and analyze how changes flow through your network of connections. Organizations use these insights to detect fraud, optimize operations, and deliver better customer experiences.

The Seven Core Graphs Your Business Needs

Every organization has seven key areas where connected data drives value:

1. Customer Graph connects every customer interaction, preference, and journey into a complete view. It reveals how customers influence each other, which product combinations drive value, and what builds lasting loyalty. 
2. Transaction Graph maps financial flows and payment patterns, helping organizations spot fraud, understand behavior, and manage risk exposure.
3. Employee Graph reveals how your organization really works, showing collaboration patterns, skill networks, and knowledge flows. 
4. Product Graph unites offerings, components, and market data to drive smarter decisions about your portfolio. When supply chain issues arise, it shows exactly which products and customers could be impacted.
5. Process Graph exposes how work flows through your organization, mapping operations, workflows, and dependencies. 
6. Supplier Graph connects vendors, materials, and logistics routes into a comprehensive supply chain view, helping teams optimize planning and respond quickly to disruptions. 
7. Network & Security Graph maps your technology landscape, showing system dependencies and access patterns to protect assets and ensure compliance.

Essential Graph Design Patterns

Success with graph databases comes from understanding and applying proven patterns that solve real business problems:

• Knowledge Graph: Create intuitive domain models that integrate multi-source data (unstructured and structured) with rich relationships.
• Pattern Matching: Find diverse patterns in connected data faster and more flexibly — including rings, cycles, clusters, and hub-spoke structures.
• Path Finding: Find the least-costly paths and most efficient routes between points, including commonly traveled paths and optimal trees.
• Anomaly Detection: Identify rare “needle-in-the-stack” patterns in relationship structures that indicate unusual behavior.
• Entity Resolution: Link and unify records that represent the same real-world entity across different systems and sources, using both data similarity and relationship context for matching.
• Digital Twin: Create virtual models with multiple layers (physical, logical, business) to analyze impacts and plan changes.
• Machine Learning: Use graph structure features to improve model performance through node embedding, clustering, and link prediction.
• Predictive Modeling: Build better predictions using relationships for next actions, future connections, and behavior patterns.
• Metadata Management: Capture and connect metadata with flexible schema to ensure consistent understanding across organizations.

Top 10 Graph Database Use Cases in 2025

Let’s look at how leading organizations are using graph databases to solve complex real-world challenges:

Customer 360: Build Complete Customer Understanding

Traditional customer data platforms struggle with a fundamental problem: they break customer relationships into separate tables. Marketing data lives in one place, support history in another, and purchase records somewhere else. As organizations collect more data across touchpoints, getting a complete view of customer behavior becomes nearly impossible. Marketing teams can’t see how support interactions affect buying decisions. Sales teams miss important signals about customer preferences. And customers face fragmented experiences as they move between channels.

Graph databases change this by connecting all customer touchpoints naturally. Instead of joining tables, you follow relationship paths that show how customers interact with your products, influence each other, and move through their journey. This connected approach reveals patterns that would stay hidden in traditional systems.

Key graph design patterns in action:

• Entity Resolution: Link and unify customer records across systems
• Knowledge Graph: Connect customer data with business context
• Pattern Matching: Find customer segments and behavior patterns

How Hästens Connected 275 Global Stores to Transform Customer Experience

Luxury bed maker Hästens needed to unify customer data across 275 global stores. Their traditional systems created walls between SAP, Salesforce, and other platforms, leading to slow response times and disconnected customer experiences. 

Their graph solution connected everything — store visits, online interactions, purchase history, and product preferences. Now they get customer insights in seconds instead of hours, cut catalog delivery time from 4 weeks to 2-3 days, and create targeted campaigns based on actual behavior. Most importantly, customers enjoy consistent, personalized experiences across all channels. The result? Stronger relationships built on complete customer understanding.

Master Data Management: Single Source of Truth

Managing business data across departments has always been a challenge for enterprises. Customer information, product details, and operational data live in silos. Legacy MDM tools weren’t built for today’s complex data landscape, forcing teams to cobble together information from scattered sources, often with conflicting versions of truth. When data quality suffers, so does your ability to make sound business decisions.

Graph databases flip this challenge on its head by letting data flow naturally between departments. Think of a graph database as a living map of your organization’s data — you can trace how information moves between sales, marketing, and other teams in real time. Need to understand how a change in one system affects others? Just follow the connections.

Key graph design patterns in action:

• Entity Resolution: Match and merge duplicate records and items
• Knowledge Graph: Create a domain model of the organization
• Path Finding: Traverse the enterprise graph to find the quickest path

NASA’s Knowledge Graph Accelerates the Mission to Mars

NASA has been collecting decades of mission-critical operational data since the 1950s. But accessing that information was challenging due to silos between departments and within individual groups, products, and programs. Engineers spent weeks searching for vital information across disconnected databases, creating risks for mission planning and preventing teams from learning from past experiences. 

After implementing their graph MDM solution, NASA’s scattered mission data finally worked as one connected system. Instead of digging through separate databases for weeks, engineers now quickly find links between past missions, procedures, and incident reports in seconds. All those hard-learned lessons from previous missions are now right at their fingertips, helping teams plan safer missions for the future.

Supply Chain Management: End-to-End Visibility and Control

Ask any supply chain manager about their biggest headache, and they’ll tell you it’s keeping track of everything. Today’s supply chains are a maze of suppliers, manufacturers, warehouses, and shops, all needing to work in perfect sync. Products change hands dozens of times before reaching customers, and one hiccup anywhere in the chain can throw everything off balance. The problem? The databases most companies use simply weren’t built to handle this web of connections. When something goes wrong, teams waste precious time piecing together information from different systems just to figure out what’s going on.

Graph databases transform supply chain management by creating a digital twin of your entire network. Every product, supplier, facility, and shipment becomes part of an interconnected view that shows how your operation works together. This complete visibility helps you spot potential problems, identify alternate suppliers, and adapt quickly when conditions change.

Key graph design patterns in action:

• Digital Twin: Model physical and logical supply chain layers
• Dependency Management: Track component and supplier relationships
• Path Finding: Optimize routes and identify bottlenecks

Global Automaker Reimagines Customer Purchasing with End-to-End Visibility

A global automaker needed to transform customer purchasing by enabling orders at any point in their supply chain. Traditional systems couldn’t track the complex network of suppliers, components, and configurations needed for this flexibility.

Their graph solution maps millions of connections between parts, suppliers, and assembly steps. It shows exactly how changes affect the entire production process and helps sales teams confidently offer more purchasing options. When supply chain disruptions occur, they quickly understand the impact and adjust their operations. The result? Better customer choice and more efficient operations through complete supply chain visibility.

Real-Time Recommendations: Personalized Experiences at Scale

Creating relevant recommendation engines requires processing vast amounts of data about products, users, and behaviors. Traditional databases struggle because they need complex operations to connect all this information. As product catalogs grow and customer interactions increase, recommendations become slower and less accurate. Organizations end up choosing between recommendation quality and system performance.

Graph databases solve this challenge by using the additional context from relationships for real-time recommendations. User preferences, product attributes, and past behaviors form a natural network that reveals patterns. This way, the recommendations engine adapt to customer preferences as they evolve. When someone’s shopping habits change or they’re browsing in a new context, the system adjusts seamlessly — without the performance lag you’d see in traditional databases.

Key graph design patterns in action:

• Pattern Matching: Find similar users and products
• Knowledge Graph: Add domain context to recommendations
• Machine Learning: Improve recommendation accuracy

WestJet Transforms Flight Booking Experience for 30M Travelers

Every year, WestJet helps 30 million travelers navigate a complex maze of flight options. Finding the right combination of routes and times is tricky enough for one passenger — now multiply that by millions. Their previous systems just couldn’t keep up, often leaving customers waiting while the system searched for available flights.

Their graph application processes 5 million relationships connecting 500,000 nodes to deliver instant suggestions. This made schedule updates 530% faster and enables route recommendations based on individual preferences, timing, and seasonal changes. The system balances customer satisfaction with network efficiency, helping travelers find ideal flights while optimizing WestJet’s operations. Most importantly, customers receive relevant suggestions that account for their specific needs and circumstances.

Fraud Detection: Prevent Financial Crime in Real Time

Fraudsters no longer work alone; they coordinate across hundreds of accounts, entities, and transactions to conceal their illicit operations. When investigators look at individual transactions, everything seems normal. It’s only when you take a step back and see the whole picture that the patterns and fraud become clear.

Graph databases excel at revealing these hidden fraud patterns by showing the full web of suspicious activity. Each transaction connects to accounts, people, and other transactions, exposing coordinated fraud rings that single-transaction analysis would miss. This network view helps organizations stop fraud before money moves, rather than trying to recover funds after the fact.

Key graph design patterns in action:

• Pattern Matching: Detect known fraud rings and suspicious cycles
• Path Finding: Track money flows through multiple accounts
• Anomaly Detection: Spot unusual transaction patterns

Zurich Insurance Saves 50,000 Investigation Hours and Stops Fraud Before It Happens

Zurich Insurance’s fraud team manually dug through thousands of claims and policies to spot suspicious activity, wasting hours that could have been spent stopping actual fraud.

Their graph solution changed everything and made that possible. Now investigators see the whole picture instantly — who’s connected to whom, which claims share suspicious patterns, and how money moves between cases. Queries that used to take minutes now come back in milliseconds. This saves their team 5-10 minutes on every case, adding up to 50,000 hours saved each year. The real win? They catch fraudulent claims before money goes out the door, protecting both Zurich and their honest customers while making sure legitimate claims are paid quickly.

Network & IT Operations: Manage Complex Systems

Managing modern IT infrastructure requires understanding complex system dependencies and relationships. Traditional tools struggle to map connections between applications, services, and infrastructure components. This limited visibility makes it difficult to assess change impacts, optimize resources, and prevent disruptions. IT teams spend precious time manually mapping connections to understand the impact and plan changes.

Graph databases excel at mapping technology landscapes as interconnected systems. They show your network and IT infrastructure exactly as they exist in the real world. Teams can see at a glance how servers, applications, databases, and services link together. This clear view makes life easier for IT teams: they can spot potential problems before users notice anything wrong, and when issues do pop up, they can trace them to the source without playing detective across multiple systems.

Key graph design patterns in action:

• Digital Twin: Model infrastructure layers
• Dependency Management: Map system relationships
• Path Finding: Trace service dependencies

BT Group Enables Lightning-Fast Inventory Management With Digital Twin

BT Group needed to manage inventory for the hardware components powering their vast network – 20,000 cell sites, 1,900 ethernet exchanges, and 150,000 circuits. Their traditional systems couldn’t efficiently track dependencies or visualize impacts, creating operational bottlenecks. 

Their graph-based Service and Resource Inventory Management System (SRIMS) connects all network elements in a comprehensive digital twin. The system processes over 50,000 product availability checks daily and handles 5,000+ order reports per hour. Capacity planning time dropped by 50%, while human decision points decreased by 60%. This visibility transformed how BT Group manages infrastructure changes, ensuring reliability while enabling innovation.

Identity and Access Management: Secure Complex Enterprise Resources

Security teams face a growing headache in today’s enterprises: managing who gets access to what. The identity management tools most companies use weren’t built for modern organizations, where roles and permissions constantly evolve. Simple changes, like updating team access for a new project, turn into days of careful checking to prevent security gaps. Meanwhile, the business needs to move quickly.

Graph databases approach identity and access management in a fundamentally different way. They create a clear map of your organization’s access patterns — showing how users, roles, and resources connect in the real world. Security teams can see exactly who has access to what and why, making it easier to maintain strong controls while quickly adapting to business changes.

Key graph design patterns in action:

• Dependency Management: Track relationships between identities, roles, and resources
• Pattern Matching: Detect policy violations and validate access rules
• Entity Resolution: Unify user identities across systems

Comcast Creates Personalized and Secure Smart Homes

Comcast faced a unique challenge: creating personalized security and smart home experiences for over 30 million customers. Traditional systems couldn’t effectively model the complex relationships between users, their devices, and their access preferences. 

Their solution was the Xfinity profile graph, which maps rich connections between people, locations, devices, and permissions. This graph approach enables granular control of access rights – parents can pause children’s devices at dinner time, provide limited internet access for guests, and set up notifications when family members arrive home. The system supports unique experiences tailored to each household while maintaining appropriate security boundaries. Most importantly, Comcast can continuously innovate by simply attaching new data types and paradigms to existing profiles without disrupting the entire system.

Risk & Compliance: Protect Data Privacy and Ensure Compliance

Compliance teams often spend a huge amount of time mapping out where sensitive data lives, how it moves between systems, and whether it meets regulatory requirements. When potential violations are found, it becomes a race against time to understand the full scope of exposure and meet reporting deadlines.

Graph databases create a comprehensive view of risk and compliance landscapes by connecting all elements of your security landscape. Instead of reviewing isolated logs and access records, teams see a complete picture of how data moves and who can access it. This connected view reveals potential vulnerabilities and compliance issues before they cause problems while making it easier to track regulatory requirements across systems. Financial institutions, healthcare providers, and government agencies use this connected approach to protect sensitive data while maintaining regulatory compliance such as GDPR, FRTB, and CCPA.

Key graph design patterns in action:

• Dependency Management: Track regulatory impact and data flows
• Pattern Matching: Identify risk patterns
• Anomaly Detection: Find suspicious behavior

UBS Uses Data Lineage to Improve Risk Management and Drive Compliance

UBS needed to comply with the Basel Committee on Banking Supervision standard 239 (BCBS 239) while strengthening data governance across their global operations. Traditional tools couldn’t effectively track data lineage through multiple systems, making regulatory reporting time-consuming and error-prone. 

Their graph solution synchronizes with existing systems to map relationships between data elements, transforming how they manage regulatory compliance. The bank can now trace lineage of all metrics company-wide across dozens of levels of entities and dependencies. Real-time queries and visualizations make reporting faster and more accurate, while deep data lineage visibility enables better impact analysis and governance. Most importantly, UBS can now demonstrate compliance with confidence while adapting quickly to new regulatory requirements.

Social Network Analysis: Understand Human Connections

Social platforms need to process billions of connections between users, content, and interactions in real time. Traditional databases buckle under the complexity of relationship queries at this scale. Feed updates slow down, connections are missed, and user engagement suffers. As networks grow, finding relevant content becomes a major technical hurdle.

Graph databases provide the ideal foundation for social networks because they’re built specifically for managing relationships. Instead of forcing social connections into tables, they’re stored naturally. Each like, share, follow, and comment enriches the network while maintaining fast performance, even with millions of users.

Key graph design patterns in action:

• Pattern Matching: Detect communities and influence networks
• Machine Learning: Predict emerging relationships
• Path Finding: Discover connection paths

Adobe Behance Connects 18M Creatives in Milliseconds

Adobe’s Behance community demonstrates how this works at scale. Their creative professional network connects 18 million members sharing portfolios and interactions. Their previous system took up to 30 minutes to update activity feeds, creating frustrating delays. 

Their graph solution transformed this experience, powered by just three database instances. Now feed updates complete in 100 milliseconds, and new users see relevant content within 400 milliseconds of signing up. The platform handles massive daily interaction volumes while maintaining consistent performance. Most importantly, creative professionals discover meaningful work and connections instantly, driving stronger engagement across the community.

Generative AI: Make AI Smarter with Context

Traditional GenAI applications struggle with accuracy and context when processing enterprise data. Vector-only approaches miss crucial connections between facts, while lack of context limits trust in AI responses. Organizations need GenAI systems that combine the power of large language models with a deep understanding of their domain knowledge. This challenge particularly impacts industries where AI decisions require complete context and clear explanations.

Graph databases enhance GenAI capabilities by providing the rich context needed for reliable results. By unifying knowledge graphs, vector search, and data science capabilities through the GraphRAG approach, organizations can ground AI responses in factual knowledge. This connected approach helps AI systems understand relationships between entities, follow reasoning paths across multiple hops, and explain their conclusions with clear evidence trails. The result is more accurate, contextual responses that users can trust for high-stakes decisions.

Key graph design patterns in action:

• Knowledge Graph: Ground AI responses in domain data
• Path Finding: Enable multi-hop reasoning
• Machine Learning: Enhance accuracy with graph algorithms

Data² Builds Leading GenAI Analytics Platform with GraphRAG

Data² needed to empower analysts making critical decisions across defense and intelligence sectors. Traditional AI solutions couldn’t connect diverse data sources or explain their reasoning, leading to uncertainty in high-stakes situations. 

Their reView platform combines knowledge graphs with GraphRAG, connecting structured and unstructured data into a unified knowledge base. When analysts ask questions, the system doesn’t just search for keywords – it traverses relationship paths to understand complex contexts and provide evidence-based answers. Analysts can trace exactly how the system reached each conclusion by following the graph connections, building trust in the AI’s recommendations. This approach cut analyst workload by 50% while maintaining decision accuracy in critical situations. Most importantly, the platform helps analysts uncover hidden patterns while providing clear, explainable insights grounded in factual data.

Frequently Asked Questions

When should you use a graph database? 

Consider a graph database when:

• Your data’s value lies in relationships and connections
• Queries need to traverse multiple relationships quickly
• Traditional database joins become too complex or slow
• Data models need to evolve frequently
• Real-time pattern detection is crucial
• You need to understand how entities influence each other

How do graph databases compare to relational databases? 

Unlike relational databases that store data in separate tables connected through joins, graph databases store relationships directly alongside the data. This means:

• No complex join operations needed to connect data
• Faster queries as data relationships grow
• More intuitive data modeling
• Easier adaptation to changing business requirements
• Better performance for relationship-intensive queries

What problems do graph databases solve? 

Graph databases address challenges that traditional databases struggle with, including:

• Complex queries requiring multiple table joins
• Real-time relationship analysis
• Flexible data models that need to evolve quickly
• Performance issues with deeply connected queries
• Pattern detection across large datasets

What industries benefit most from graph databases? 

While graph databases deliver value across all sectors. However, they’re particularly transformative in:

• Financial services: Fraud detection, risk assessment, compliance
• Retail: Customer analytics, recommendations, supply chain
• Healthcare: Patient journey mapping, research connections
• Manufacturing: Supply chain optimization, digital twins
• Technology: Infrastructure management, social networks
• Government: Public service delivery, fraud prevention, security
• Transportation: Route optimization, scheduling, fleet management
• Telecommunications: Network management, service optimization, infrastructure planning

Getting Started With Graph Databases

As the creator of the property graph model and the most widely deployed enterprise-grade graph database platform, Neo4j Graph Database has helped thousands of organizations transform how they use connected data. Here’s what we’ve learned about starting successfully:

1. Identify Your Use Case: Success with graph databases starts with understanding your data relationships. Start where relationships in your data create clear value. Look for areas where you’re joining multiple tables, tracking complex dependencies, or trying to find hidden patterns.
2. Model Your Data: Map out the nodes, relationships, and properties that matter for your specific challenge. Unlike rigid table structures, graph databases let your data model evolve naturally as your needs change.
3. Start Small: Begin with a focused project that shows clear value. Many organizations start with a subset of data to prove the concept before expanding. This helps build confidence and expertise within your team.
4. Scale with Confidence: Add more use cases and data as you prove success. Neo4j’s enterprise-grade platform scales horizontally and vertically to handle growing data volumes while maintaining performance.