Summary

• Traditional healthcare analytics platforms are built to store records, not analyze the connections between them. A healthcare graph database changes that.
• A healthcare graph database models patients, providers, claims, diagnoses, prescriptions, procedures, referrals, and billing entities as a connected network, making relationship-heavy queries operational rather than theoretical.
• Relational systems and claims warehouses fall short on three recurring problems: shallow fraud detection, disconnected cost analytics, and limited referral and journey intelligence.
• Graph outperforms flat analytics across four healthcare use cases: cost-of-care analysis, fraud and abuse detection, patient journey optimization, and referral network intelligence.
• Combined with AI, graph provides the connected context that healthcare models need for accurate risk prediction, explainable decision support, and grounded clinical AI assistants.

Healthcare produces some of the most relationship-rich data in any industry. Patients connect to providers, providers connect to referrers, claims connect to diagnoses, prescriptions connect to outcomes, and every care decision can influence cost, quality, and risk downstream. Yet most healthcare data still lives in systems that flatten those relationships into tables. That architectural mismatch is becoming one of the biggest obstacles to modern healthcare analytics and AI. 

Claims warehouses, EHR exports, and FHIR repositories are strong at storing records and supporting scheduled reporting. But they struggle with the connected questions healthcare leaders increasingly need to answer: Which provider networks drive avoidable readmissions? Which claim patterns suggest coordinated fraud? Which patient journeys produce the best outcomes per dollar? Which referral relationships shape prescribing or device adoption?

A healthcare graph database models patients, providers, claims, diagnoses, prescriptions, procedures, devices, pharmacies, and referrals as a connected network. That structure makes relationship-heavy analytics operational, not just possible. The challenge is no longer collecting healthcare data. It is understanding how that data connects across patients, providers, organizations, and time. 

You’ll learn:

• Why relational systems create analytical blind spots for fraud, cost, and referral intelligence
• What a healthcare graph database adds and how it differs structurally from relational systems
• Where graph outperforms flat analytics across four high-value use cases
• How graph and AI combine to power the next generation of healthcare decision support

What Is a Healthcare Graph Database?

A healthcare graph database stores clinical and administrative entities: patients, providers, claims, diagnoses, prescriptions, procedures, pharmacies, devices, and referrals. It stores them as a connected network rather than as rows in separate tables. Relationships between those entities are first-class data, making it possible to query across many connections in real time without relying on repeated joins. Healthcare graph databases are designed for relationship-heavy workloads: fraud detection, patient journey analysis, referral network mapping, cost-of-care analytics, and AI-grounded decision support. 

Relational systems store healthcare data in one table per entity type: providers, claims, diagnoses, prescriptions. To answer a question that spans those entities, an analyst must join those tables repeatedly. At payer scale, the queries that matter most – which providers share patients with a known high-risk biller, and which of those patients also filled prescriptions at the same pharmacy cluster – require assembling answers from five or more tables simultaneously. Those joins become increasingly expensive as the number of entities, relationships, and traversal depth grows, and they only get slower as the dataset grows.

A healthcare graph database stores those connections explicitly and queries them directly. The relationships are already in the data model, so following a chain across providers, patients, claims, and pharmacies is fast regardless of network size. That structural difference is why healthcare organizations turn to graph when their analytics platforms hit a ceiling: not because relational systems are poorly built, but because they were built for record storage, not network-level intelligence.

Graph vs. Flat Analytics: Key Differences for Healthcare

The most significant difference is the class of question each system can answer. Relational systems handle aggregate questions well. Graph handles network questions: which providers are connected through shared patients and shared billing entities to a known high-risk cluster? Those are fundamentally different analytical tasks.

Four Healthcare Use Cases Where Graph Outperforms

Cost-of-Care Analysis

A flat claims report explains what happened. A healthcare graph database helps explain why it happened. A healthcare graph database shows why. It connects patients to diagnoses, treating providers, medication histories, referral sequences, procedures, adverse events, and downstream claims in a single query. This surfaces patterns – avoidable specialist routing, prescription cascades, follow-up gaps – that flat reporting attributes to individual claims rather than to the network behavior producing them.

The U.S. healthcare system has little room for these blind spots. According to CMS National Health Expenditure data, U.S. health spending reached $5.3 trillion in 2024, or $15,474 per person. At that scale, even small shifts in provider network behavior, specialist routing, or prescription patterns create material financial exposure. Graph gives payers the visibility to target cost-containment programs at the specific providers and pathways driving excess spend, rather than applying broad controls across the full population.

Healthcare Fraud, Waste, and Abuse Detection

Healthcare fraud rarely appears as a single bad claim. It appears as a relationship pattern: billing collusion between providers, opioid prescribing networks, durable-medical-equipment kickback rings, or synthetic-patient schemes assembled across multiple billing entities. These patterns are invisible when every claim is evaluated in isolation.

Graph connects entities that appear unrelated in flat data: shared addresses, shared patients, repeated referral loops, and clusters of claims flowing through common billing entities. The Department of Justice’s 2025 healthcare fraud takedown, involving charges connected to more than $14.6 billion in alleged fraud, reflects exactly the kind of coordinated network behavior that graph is built to detect. For payers, that means shifting from reactive “pay and chase” investigation to earlier, pattern-based prevention.

Patient Journey Optimization

A patient moves across symptoms, diagnoses, providers, prescriptions, procedures, labs, claims, complications, and outcomes. A healthcare graph database models that journey as a connected path rather than a sequence of disconnected records. Payers and integrated providers can compare care pathways across similar patient populations, identify where high-risk patients fall out of follow-up, and find the routes that produce better outcomes at lower cost. Graph can reveal that two patients with similar diagnoses follow very different system routes: one to stable outcomes, one to repeated utilization and higher cost – a distinction flat reporting obscures.

This matters most in value-based care environments where reimbursement depends on demonstrating that care coordination is working. Graph gives care teams the operational visibility to identify high-risk patients before they deteriorate, close care gaps before they become readmissions, and prioritize interventions on the pathways where network behavior is driving avoidable cost.

Referral Network Intelligence

Referral networks shape care access, specialist utilization, prescribing behavior, and commercial influence. Graph shows which physicians refer within the network, which specialists create bottlenecks, where leakage occurs, and how referral behavior shifts over time. For pharmaceutical and medical-device teams, graph maps key opinion leaders, specialist hubs, and the influence networks that drive adoption decisions. The result is a shift from territory-based planning to network-based engagement: prioritizing providers whose relationships shape the broader system, not just those with the highest historical volume.

Graph + AI: How the Combination Powers Healthcare Analytics

Graph and AI are deeply complementary in healthcare. Graph provides the connected context that healthcare models need to reason accurately. AI provides the pattern recognition that turns relationship data into actionable clinical, operational, and commercial signals.

Graph enables richer features for risk and cost models: relationship-aware signals such as provider-network centrality, referral concentration, prescribing-cascade depth, and distance from known high-risk patterns improve models for readmission risk, cost prediction, and care-management prioritization. Graph machine learning extends this further – learning from the structure of provider-claim-patient relationships to identify emerging fraud configurations before a rule has been written.

GraphRAG addresses a different gap: healthcare AI assistants need grounded context, not just semantic similarity. A clinical AI assistant working from vector search alone can surface plausible answers drawn from similar-sounding documents. A healthcare graph database changes what retrieval means: instead of finding similar text, the system follows the actual relationships connecting the patient’s conditions, providers, medications, prior authorizations, and care history. GraphRAG connects that structured network to the retrieval process, improving traceability and reducing the risk of answers that sound reasonable but lack grounding in the patient’s actual record.

Across all of these applications, graph adds explainability: every AI-assisted recommendation can be traced back to the specific relationships that informed it. In healthcare, that is not optional – clinicians, compliance teams, and fraud investigators all need to understand why a decision was made, not just what it was.

Enterprise Use Cases: Where Healthcare Graph Applies

Graph applies across every segment of the healthcare ecosystem. Payers and health plans use it to detect claims fraud, model provider networks, and ground prior authorization AI in a patient’s full clinical and claims context. Integrated providers and health systems use it to map patient journeys, identify referral leakage, and find the care gaps that drive avoidable readmissions. Pharmaceutical and medical-device commercial teams use it to prioritize field-force engagement based on actual physician influence networks rather than territory. Government healthcare programs use it to surface fraud rings and opioid prescribing networks that span multiple billing entities.

For organizations working on identity and patient matching, graph complements entity resolution by linking fragmented records that refer to the same patient, provider, or billing entity. For organizations building richer member and patient views, it supports Customer 360 approaches adapted to healthcare contexts. Healthcare data modernization teams using FHIR will find that FHIR enables record exchange but does not solve relationship analytics – graph adds the intelligence layer on top.

Why Healthcare Analytics Needs an Enterprise Graph

Healthcare has always been a network. The technology used to analyze it is finally catching up.  The questions that drive cost control, fraud detection, care quality, and referral strategy all depend on understanding how patients, providers, claims, prescriptions, and outcomes connect to one another. Relational systems answer those questions poorly at scale. A healthcare graph database answers them directly, in real time, with the explainability that clinical oversight and compliance require.

As the industry moves toward value-based care, predictive risk management, and AI-grounded operations, the analytical advantage will go to organizations that treat their data as a network. TigerGraph provides the enterprise-grade healthcare graph database platform to make that possible: supporting fraud detection, cost-of-care analytics, patient journey optimization, referral intelligence, and AI-grounded decision support across the full healthcare ecosystem.

Ready to see what graph can do for your organization? Explore TigerGraph Healthcare and Life Sciences or request a demo to talk through your use case.

FAQ: Healthcare Graph Database

What is a healthcare graph database?

A healthcare graph database stores providers, patients, claims, prescriptions, referrals, diagnoses, procedures, and outcomes as a connected network. It enables the relationship-heavy queries that drive cost, fraud, journey, and referral analytics: queries that require following connections across many entities simultaneously, which relational systems handle poorly at scale.

How does a graph database improve healthcare analytics?

A graph database improves healthcare analytics by making relationships directly analyzable at scale. It surfaces referral patterns, fraud rings, patient journey paths, and cost drivers that flat claims warehouses struggle to identify in real time. The key difference is structural: graph stores connections as data, so queries spanning many entities do not require assembling the answer from multiple tables.

What are the use cases for graph databases in healthcare?

Common healthcare graph database use cases include cost-of-care analysis, fraud waste and abuse detection, patient journey optimization, referral network intelligence, clinical research, real-world evidence, and AI-grounded decision support. Each requires analyzing relationships across multiple entity types simultaneously, which is the structural task graph databases are built for.

How is graph used in healthcare fraud detection?

Graph is used in healthcare fraud detection by connecting providers, patients, claims, prescriptions, pharmacies, addresses, referrals, and billing entities in a single queryable network. This helps detect organized patterns such as billing collusion, opioid networks, kickback rings, and synthetic-patient schemes: patterns that appear unrelated in flat data but become visible when the connections are mapped.

What is the best graph database for healthcare?

The best healthcare graph database depends on scale, security, AI integration, and workload requirements. For enterprise healthcare workloads requiring real-time, multi-step relationship analytics across claims, providers, patients, and AI use cases, TigerGraph is purpose-built for those requirements, combining enterprise-scale graph analytics with AI-ready infrastructure and built-in support for explainable decision intelligence.

Summary

• Conventional supply chain analytics tools forecast risk at individual suppliers, shipments, or SKUs, but struggle to model how that risk spreads across multi-tier dependencies in real time.
• Graph databases model the supply chain as a connected network of entities and relationships, giving predictive models the structural context to trace how disruption cascades before it becomes a missed shipment.
• The gap between seeing a risk signal and understanding its downstream impact is where most supply chain analytics tools break down, and where graph makes the biggest difference.
• Graph is most valuable for multi-tier supplier risk mapping, cascade simulation, relationship-aware demand sensing, and AI-enriched disruption forecasting.
• Graph does not replace existing ERP, planning, or BI tools; it adds the relationship layer those systems need to turn isolated forecasts into network-aware predictions.

Most predictive analytics applied to a supply chain can warn leaders that a disruption is coming. The harder question is what happens next: which suppliers are exposed three tiers upstream, which products and customers are affected downstream, how the disruption will cascade through the network, and which recovery path reduces the most risk. Conventional supply chain analytics platforms often struggle here because they forecast individual records: a supplier, shipment, warehouse, or SKU in relative isolation. Graph databases add the missing relationship layer, modeling the supply chain as a connected network of dependencies and giving predictive models the context to evaluate how risk moves across that network in real time.

You’ll learn:

• Why conventional supply chain analytics hits a ceiling when disruption cascades across multiple tiers
• What graph databases add to supply chain predictive analytics, and how they work
• How graph and AI improve disruption forecasting together
• Where the approach applies across enterprise supply chain operations

Why Conventional Supply Chain Analytics Hits a Ceiling

Most enterprise supply chain analytics tools explain what has already happened. BI dashboards, ERP-embedded analytics, transportation systems, and planning platforms can show where delays occurred, which suppliers underperformed, and where inventory fell short. That visibility matters, but predictive analytics in supply chain now has to answer a harder question: when disruption starts, how will it move through the entire network?

Resilinc reported a 38% year-over-year increase in global supply chain disruptions in 2024, while McKinsey found that 82% of surveyed companies said new tariffs affected their supply chains. Many conventional platforms were not built for that level of volatility. They organize data around individual records: a supplier, shipment, warehouse, SKU, or region. They can forecast risk at one point in the chain, but struggle to model how that risk spreads through suppliers, manufacturers, logistics partners, inventory locations, products, and customers.

Consider a port closure. A tracking system may flag delayed containers. A planning system may identify shipments at risk. But the bigger question is what happens next: which tier-2 or tier-3 suppliers depend on that port, which production lines will be affected in sequence, which customer commitments are exposed in the next 72 hours, and which recovery option balances cost, speed, and compliance. In many organizations, that analysis still happens manually across spreadsheets, emails, and disconnected systems.

This creates three gaps. The first is visibility: suppliers, manufacturers, carriers, and retailers often run on different systems, making end-to-end transparency difficult. The second is adaptability: static planning models struggle when trade conditions and transportation availability change quickly. The third is context: compliance exposure may sit several tiers deep; demand shifts may depend on substitutions or shared components that a per-record forecast cannot see.

Graph databases address these gaps by adding the missing relationship layer that conventional supply chain predictive analytics often lacks. They do not replace existing tools; they give predictions the connected context needed to show what will happen, where it will spread, and how teams can intervene sooner.

What Is Graph-Based Supply Chain Analytics?

Graph-based supply chain analytics models the supply chain as a connected network of entities, including suppliers, manufacturers, warehouses, carriers, products, contracts, and risk signals, and the relationships between them. Unlike tabular systems that forecast risk for individual records, a graph database stores and queries those connections directly, enabling teams to trace how disruption, demand, and risk propagate across multiple tiers in real time.

A graph database represents the supply chain as a network of real entities: suppliers, manufacturers, warehouses, products, carriers, contracts, and risk signals. Each relationship connects two entities and defines how they interact. One supplier provides a component to a manufacturer. One carrier ships to a distribution center. Both entities and their relationships carry properties, such as lead time, capacity, cost, compliance status, or risk score. The database stores this connected model and makes the relationships directly queryable.

This model unlocks four capabilities that flat analytics miss. First, multi-tier dependency mapping: teams can trace a quality issue at a tier-4 supplier across subcomponents, manufacturers, and finished goods without manually assembling data from separate systems. Second, cascade simulation: when a port closes or a supplier fails, teams can model second- and third-order effects on production schedules and delivery promises before they become missed shipments. A graph database runs that cascade analysis across the live network in seconds, querying stored relationships directly rather than reconstructing them from joins. Third, relationship-aware demand sensing: demand for one SKU may affect related products through substitutions, complements, or shared components, and graph makes those links visible to prediction models. Fourth, real-time supplier risk scoring: a supplier with strong direct metrics may still carry hidden exposure through a distressed sub-supplier or constrained route.

Jaguar Land Rover used TigerGraph to reduce supply chain planning time from 3 weeks to 45 minutes.

Graph vs. Conventional Supply Chain Analytics: Key Differences

Graph-based supply chain analytics does not replace existing forecasting, planning, or BI tools. It adds the relationship layer those systems need to understand how risk, demand, and disruption move through the full network.

Cascade modeling and supplier risk scoring create the clearest commercial impact. When disruption occurs, graph helps teams see which production schedules, inventory positions, and customer commitments are exposed before the impact becomes visible in downstream systems. For supplier risk, graph adds context that individual scorecards miss: a supplier may look stable on its own but carry hidden exposure through a constrained route or distressed sub-supplier.

How Graph and AI Power Supply Chain Predictive Analytics Together

Graph databases and AI models solve complementary problems in supply chain forecasting. Graph provides structural context: how suppliers, facilities, products, routes, contracts, and risk signals are connected. AI provides pattern recognition: forecasting demand shifts, delay probability, supplier failure risk, and recovery outcomes. Together, they improve predictive analytics in the supply chain by giving models structural features that tabular data alone cannot provide.

Graph-computed features make ML models more predictive. When a supply chain is modeled as a connected network, analysts can derive structural features – dependency concentration, supplier criticality, distance to risk sources, alternative-path availability – that tabular data simply cannot produce. Graph Neural Networks take this further: rather than relying on hand-engineered features, GNNs learn directly from the structure of the supply chain itself, identifying relationship patterns that historically preceded shortages, delays, or supplier instability. And when graph analytics are combined with anomaly detection, supply chain visibility becomes real time: teams can monitor how risk patterns shift across the network and surface emerging disruptions before they appear as missed shipments or stockouts. TigerGraph’s ML tools support this entire graph ML pipeline – graph feature generation, GNN/XGBoost workflows with NVIDIA accelerated model training, and real-time in-database analytics. 

Enterprise Use Cases for Graph Supply Chain Analytics

Graph supply chain analytics applies wherever prediction depends on understanding how suppliers, products, facilities, routes, and risks are connected.

Multi-tier supplier risk and resilience planning. Manufacturers with complex global supply chains use graph to map dependencies across multiple supplier tiers in real time. When a geopolitical event, quality issue, or supplier financial stress emerges, the graph surfaces which components, production lines, and customer commitments are exposed, helping teams act before the risk appears as a late shipment or production delay.

Demand sensing and inventory optimization. Retailers and consumer goods companies use graph to model product relationships: substitutions, seasonal co-purchases, promotional bundles, and shared components. When demand changes for one product, graph-enhanced supply chain predictive analytics can update inventory predictions for related products instead of treating every SKU as an isolated forecast, reducing both stockouts and excess inventory.

Tariff and trade disruption modeling. As tariff policies or trade restrictions change, graph models can re-evaluate affected supplier relationships, routes, materials, and landed-cost assumptions across the network. Because the graph stores how every supplier, material, and route is connected, a policy change at the country level can be traced immediately to the specific products, orders, and customer commitments it affects.

Logistics and transportation route optimization. Graph models transportation networks as connected systems of ports, carriers, lanes, warehouses, and fulfillment centers. When a port closure or carrier capacity constraint occurs, teams can identify affected shipments, trace downstream order impact, and surface alternative routes faster than batch-driven planning allows.

Predictive maintenance in manufacturing supply chains. Manufacturers use graph to connect equipment, sensor signals, maintenance history, spare parts, suppliers, and production schedules. When a sensor anomaly signals a possible failure, graph analytics can estimate the supply chain impact of unplanned downtime and trigger proactive parts procurement before the line stops.

Why Effective Predictive Analytics in Supply Chain Requires an Enterprise-Grade Graph Platform

Conventional supply chain analytics can forecast risk at individual suppliers, shipments, and SKUs. But resilience depends on seeing how that risk moves across the network before it becomes a disruption. The organizations seeing the most value from predictive analytics in supply chain are not replacing their ERP or planning systems; they are adding a graph layer that connects those systems’ data into a queryable network, giving existing forecasting models the structural context they have always lacked.

TigerGraph provides the enterprise-grade graph platform built for this workload: real-time, multi-level supply chain analytics supporting disruption prediction, cascade simulation, and AI-enriched forecasting in one connected system.

Ready to see it in action? Request a demo or explore TigerGraph pricing and free trial options.

FAQs

What is predictive analytics in supply chain?

Predictive analytics in supply chain is the use of historical data, statistical models, machine learning, and AI to forecast future supply chain conditions. It helps organizations anticipate demand shifts, supplier failures, shipment delays, and disruption risks so they can act before those issues become operational failures. The harder challenge is modeling how risks propagate across a connected multi-tier network, not just at individual records.

How does a graph database improve supply chain predictive analytics?

A graph database improves supply chain predictive analytics by modeling the supply chain as a connected network of suppliers, facilities, products, routes, and risk signals. This lets prediction models account for how disruption cascades through multi-tier dependencies, which flat analytics tools often miss. TigerGraph enables feature generation across 10 or more levels of supply chain relationships, feeding that structural context into machine learning models to improve disruption prediction accuracy.

What is real-time supply chain visibility?

Real-time supply chain visibility is the ability to monitor the current state of the supply chain and understand how each supplier, shipment, facility, and route connects to the rest of the network. Graph databases are purpose-built for this because they store and query relationships directly, making it possible to detect how risk patterns are changing as those changes happen, not hours or days later.

What are the best supply chain analytics tools for disruption prediction?

The best supply chain analytics tools for disruption prediction support multi-tier dependency modeling, real-time relationship analysis, AI integration, and fast scenario simulation. BI dashboards and ERP analytics are useful for reporting, but they often lack the relationship-aware prediction needed to model disruption cascades accurately. Graph databases fill that gap by adding connected context to existing forecasting and planning systems.

How does AI improve supply chain analytics?

AI improves supply chain analytics by identifying patterns in demand, supplier performance, and disruption signals that are difficult to detect manually. When AI models are trained with graph-derived features, including supplier network position, dependency depth, and risk proximity, they gain structural context that tabular data alone misses. The combination of graph’s relationship intelligence and AI’s pattern recognition is what makes modern disruption prediction actionable at enterprise scale.

Summary

• Most Customer 360 initiatives consolidate customer records into a central repository but flatten the relationships that make those records meaningful: household membership, product journeys, shared devices, and referral chains disappear into wide tables.
• A graph database stores the customer view as a live, queryable network of identifiers, accounts, transactions, devices, and interactions, and answers multi-hop relationship queries across that network in milliseconds.
• CDPs and data warehouses are built for record consolidation and audience activation. A graph database adds the relationship layer that those platforms cannot build natively: real-time identity resolution, household analytics, and network-aware segmentation.
• A graph database for Customer 360 becomes the right complement to your existing stack when your queries require following chains of relationships across multiple entity types, household, device, product, journey, faster than a relational join can deliver at production scale.
• The most effective enterprise Customer 360 architectures combine a CDP or warehouse for record management and activation with a graph database for relationship intelligence and real-time decisioning..

Your Customer 360 dashboard consolidates what you know about customers. But it probably drops the relationships that make that knowledge actionable. If you can answer “what products does this customer hold?” but not “which household members are likely to churn together, and what is the fastest path to retaining them?”, your 360 view is built on a flat model that cannot reach the questions where customer value actually lives.

The structural reason is straightforward: warehouses and CDPs are built on relational or tabular models. They resolve a single customer record well. However, they struggle with the multi-hop relationship queries that drive next-best-action recommendations, household retention analytics, and real-time personalization at the touchpoint, because those queries require following chains of connections across entities, and relational joins become slow and expensive as the relationship depth increases.

A graph database for Customer 360 stores the customer view as a network and queries it as a network. That structural difference is what makes it the right complement, not replacement, for your existing CDP or warehouse stack.

You’ll learn:

• Why traditional Customer 360 architectures lose the relationships that drive customer value
• What graph databases add specifically to identity resolution, journey analytics, and real-time decisioning
• How graph compares with CDP and warehouse-based approaches across key dimensions
• How graph and AI combine to power personalization, churn prediction, and explainable recommendations
• Where graph-powered Customer 360 applies across banking, retail, telecom, insurance, and B2B

What Is a Graph Database for Customer 360?

In a Customer 360 context, a graph database represents each customer as a node. Every identifier that belongs to that customer, including email addresses, phone numbers, loyalty IDs, and device identifiers, is also a node, connected to the customer by a labeled relationship. Every account, product, transaction, household member, journey step, and service interaction is connected the same way.

The result is not a wide table with many columns. It is a queryable network. When a bank needs to ask “which customers in this household also hold a competitor product and have made a transaction in the past seven days?” that query follows a chain of relationships across four entity types: customer, household, product, and transaction. In a warehouse, that requires four sequential joins. At production scale, those joins are often too slow to influence a live customer touchpoint. In a graph database, the same query runs in milliseconds.

This is the structural advantage a graph database brings to Customer 360: not just a more complete picture of the customer, but a picture that can be queried in real time, at the moment a decision needs to be made.

Why Most Customer 360 Initiatives Fall Short

Most enterprise Customer 360 initiatives consolidate customer records from CRM systems, transactional platforms, marketing tools, and service applications into a central repository, typically a data warehouse, lakehouse, or CDP. While this creates a consolidated record, it rarely creates a connected one.

Warehouses and CDPs are built on relational or tabular models that resolve a single customer record but flatten relationships such as household membership, shared devices, multi-account ownership, referral chains, and customer journey paths. Those relationships can technically be expressed as joins in relational databases, but they become expensive at scale. Multi-hop relationship queries that drive the most valuable customer insights cannot be executed effectively in real time using joins alone.

Consider a concrete example: a bank wants to identify household members who are also customers, have recent transactions including a competitor’s product, and have engaged with a specific marketing campaign. Running that query in a warehouse environment requires several deep joins across multiple tables. In production, those queries take minutes, and are often avoided altogether, causing the cross-sell opportunity to be missed entirely.

Beyond the join problem, Customer 360 programs also hit three structural walls:

• Identity fragmentation: customers exist as multiple partial records across systems. Resolving them into a single identity is difficult in tables; resolving households and connected accounts is harder still.
• Loss of behavioral and product context: purchase, browsing, and service interaction data is collapsed into aggregate features rather than preserved as a connected journey, eliminating the patterns that drive next-best-action recommendations.
• Latency between insight and action: even when the 360 view is built, querying it for a specific real-time decision is too slow to influence the touchpoint at the moment a customer is present.

Graph databases are purpose-built to solve these issues by resolving identities through relationship queries, preserving behavioral and product context as connected paths, and returning answers to multi-hop queries in milliseconds.

What a Graph Database Adds to Customer 360

Here are four specific capabilities that a graph database for Customer 360 unlocks:

• Real-time identity and household resolution: Graph queries unify fragmented records across channels, devices, and household members in real time, producing the single customer view that flat resolution approaches deliver only as a batch artifact. Entity resolution through graph is continuous rather than periodic.
• Connected journey analytics: Every touchpoint a customer has across web, mobile, store, and service becomes a node connected to the customer. The full journey can be queried as a path rather than reconstructed from session logs. This means the system can recognize that a customer who browsed a product on mobile, abandoned checkout, and then called service about a related product is not two separate signals but a single connected intent.
• Network-aware segmentation and propensity: Segments and propensity scores reflect not just the individual customer but the household, product network, and behavioral graph in which the customer sits. Customers with similar relationship patterns are identified through graph neighborhood analysis, a structurally different and more accurate approach than feature-vector proximity alone.
• Real-time decisioning at the touchpoint: When a customer arrives at a website, calls a service center, or opens an app, the graph returns the most relevant connected context, including recent journey steps, household activity, and product affinities, in milliseconds. The touchpoint reflects who the customer is right now, not who they were at last night’s batch run.

TigerGraph‘s massively parallel processing enables deep link analytics across billions of relationships without performance degradation, supporting real-time multi-hop customer queries at enterprise scale. This is the architecture that turns a Customer 360 view from a reporting artifact into an operational capability.

Graph vs. CDP and Warehouse-Based Customer 360: Key Differences

A graph database for Customer 360 is not a replacement for CDPs, warehouses, or activation platforms. Graph acts as the relationship layer that gives those platforms the connected intelligence they cannot build natively.

The most significant difference in practice is query depth combined with latency. A CDP can tell you what a customer has done. A graph database can tell you how that customer is connected to every other entity in your data, and answer that question fast enough to act on it at a live touchpoint. These are complementary strengths. Enterprises that stop at the CDP have the record. Enterprises that add a graph have the network.

Graph + AI: How the Combination Powers Real-Time Customer Intelligence

AI is no longer optional for enterprise Customer 360. The challenge is that AI models are only as good as the features they are trained on, and attribute-only features miss the structural signals that predict customer behavior most accurately. Graph’s relationship context is the missing input that makes customer AI materially better.

Four ways graph enriches customer AI:

• Graph-computed features for ML: household behavioral similarity, multi-hop product affinity, and journey-stage centrality measurably improve the accuracy of churn, propensity, and recommendation models compared to attribute-only feature sets. The relationship context graph provides ML models with signals that flat tables cannot generate.
• Graph Neural Networks for recommendations and propensity: GNNs learn from the structural patterns in the customer-product-journey graph, capturing substitution, complement, and influence patterns that collaborative filtering and tabular models miss. A GNN trained on a retail customer graph can detect that customers who buy product A and product C frequently add product B within 30 days, even when no explicit “frequently bought together” signal exists in the transaction record.
• GraphRAG for customer-facing AI: when a bank, retailer, or service organization deploys an AI assistant, GraphRAG grounds its responses in the customer’s actual relationship graph, their accounts, household, and recent interactions, eliminating the generic responses that erode trust. A customer asking “What is the best account for my family’s savings?” gets an answer informed by their household structure, not a generic product description.
• Explainable personalization: every recommendation, offer, or next-best-action can be traced to the specific relationship path in the graph that justified it, satisfying both customer transparency expectations and the explainability standards regulators apply to consequential decisions in financial services and healthcare.

Enterprise Use Cases: Where Graph-Powered Customer 360 Applies

Customer 360 graph databases deliver measurable value across every major industry that manages complex customer relationships at scale.

Banking and financial services: Banks use graph to unify the household-and-account view across retail, wealth, and business banking, surfacing cross-sell opportunities that single-account views miss entirely. A relationship manager asking “Which of my business clients also have personal accounts, and what products do their household members hold?” gets an answer in real time rather than waiting for a next-day report. Graph also powers real-time personalization across web, mobile, and call center channels, so a customer’s most recent interactions are always part of the decisioning context. Xandr, working at the intersection of consumer data and media, uses TigerGraph to combine consumer data across 15 properties for cross-property user journey tracking, a structurally similar challenge to household analytics in banking.

Retail and consumer goods: Retailers use graph to connect customer identity across loyalty programs, e-commerce platforms, and physical store channels, giving the merchandising and personalization team a single, relationship-aware customer record rather than three disconnected profiles. Graph models product affinity across substitution and complement patterns, not just co-purchase frequency. A customer who buys running shoes is connected to other customers who later added running socks and hydration gear; that network pattern drives more accurate next-product recommendations than purchase history alone. Retailers that replace attribute-based segments with network-aware segmentation consistently see measurable lifts in recommendation click-through and average basket size.

Telecommunications and subscription services: telcos use graph to unify customer, account, and device relationships across individual and family plans, enabling household-level retention analytics that single-subscriber models miss. When one family member’s contract approaches renewal, graph can surface the risk that other household members will follow, and flag the retention intervention before the first line cancels. Graph also supports plan optimization across the connected account, identifying upgrade or consolidation opportunities based on actual usage patterns across the household rather than individual subscriber metrics.

Insurance and wealth management: insurers and wealth firms use graph to model the household, beneficiary, advisor, and policy network, a relationship structure that single-policy or single-account views cannot represent. A wealth advisor asking “which of my clients have a beneficiary relationship with a client at risk of lapsing a life policy?” is asking a multi-hop relationship question. Graph answers it; a flat system cannot. Relationship-aware servicing and next-best-action recommendations built on the full household network consistently outperform those built on individual policy records.

B2B and account-based marketing: B2B firms use graph to map the buying group inside each target account: stakeholders, influencers, decision-makers, and their relationships to each other and to prior engagements. A sales team asking “who in this account is connected to a champion at one of our existing customers?” is asking a relationship question. Graph surfaces that connection; a lead-level CRM record cannot. Personalized journeys built on the actual connected structure of the buying organization convert at higher rates than those built on lead-score attributes alone.

The Relationship Layer That Makes Customer 360 Work

A Customer 360 view built on a graph database is not a more complex version of what a CDP delivers. It is a structurally different capability: one that stores relationships as first-class data, queries them in real time, and makes the full customer network available at every touchpoint. For enterprises managing customers across households, channels, and products, that difference is where competitive advantage actually lives. 

Explore TigerGraph’s Customer 360 solution to see how it fits your stack, or request a demo to see the architecture in action.

FAQs

What is Customer 360?

Customer 360 is a unified view of a customer across every system, channel, and interaction an enterprise has with them. The most valuable Customer 360 implementations preserve the relationships behind customer records, including household connections, product journeys, and shared devices, not just consolidated fields from multiple source systems.

Why use a graph database for Customer 360?

A graph database for Customer 360 stores the customer view as a network of identifiers, accounts, households, journeys, and products, and queries that network in real time. Flat data models can consolidate records but cannot efficiently answer the multi-hop relationship queries that drive household analytics, real-time personalization, and network-aware segmentation at enterprise scale.

What is the difference between a graph Customer 360 and a CDP?

A CDP consolidates customer records and orchestrates activation across marketing and service channels. A graph database adds the relationship layer underneath: multi-hop identity resolution, household analytics, connected journey queries, and real-time decisioning that a CDP cannot provide natively. The two work together. The CDP manages the record and the activation workflow; the graph powers the relationship intelligence.

Does TigerGraph support real-time Customer 360 at enterprise scale?

TigerGraph is purpose-built for real-time, multi-hop Customer 360 at enterprise scale. It supports identity resolution, household analytics, journey intelligence, ML feature generation, GNN-based recommendations, and GraphRAG for AI-grounded customer experiences, all within a single system. Its massively parallel processing architecture handles deep relationship queries across billions of connections without performance degradation.

How does graph improve customer recommendations?

Graph encodes the product, household, and journey relationships that drive recommendation accuracy. Graph Neural Networks trained on a customer-product-journey graph capture substitution, complement, and influence patterns that collaborative filtering and tabular models miss. Every recommendation produced is also explainable: each suggestion traces back to the specific relationship path that justified it, which matters for both customer trust and regulatory compliance.

How Graph Could Have Exposed Suspicious Loans for Zions and Western Alliance Banks

When two U.S. regional banks, Zions Bancorp and Western Alliance, reported massive loan losses tied to the same guarantors and investment groups, markets reacted with alarm. The lawsuits allege shared borrowers, misrepresented collateral, and overlapping assets worth hundreds of millions of dollars. What happened? 

The data existed. What was missing was connection.

Traditional systems tracked loans, guarantors, and collateral independently. Each bank saw its own records, but none could visualize the larger borrower network forming across institutions. A graph-based approach would have made those links visible long before defaults turned into litigation.

Let’s break down how that looks.

Fragmented Risk and the Blind Spots of Traditional Systems

Every financial institution monitors loan performance, but those records often live in separate silos—lending, property, legal, and risk. Each system captures transactions in isolation, without modeling the relationships between entities across different contexts. 

In the Zions and Western Alliance cases, the same borrowers appeared as partners, guarantors, and investors in multiple datasets, yet no alerts fired.

Tabular databases record values, not relationships. They can show exposure within one dataset but cannot connect patterns spanning different banks or loan portfolios. 

Fraud thrives in those gaps. 

Shared guarantors, co-owned shell entities, and recycled collateral remain hidden until they collapse into losses.

Fraudsters exploit this fragmentation. They distribute their activities across institutions, jurisdictions, and asset classes, knowing that each system only sees a fragment of the pattern.
Common blind spots include:

• Borrowers using multiple corporate entities across banks.
• Duplicate collateral pledged in different loan portfolios.
• Overlapping guarantors who appear legitimate individually but are suspicious as a network.
• Regulatory filings that mention shared assets but sit in separate repositories.

The result is that multiple banks finance the same at-risk borrowers without realizing they are funding a connected scheme.

A Graph Model Reveals What Spreadsheets Miss

Graph technology replaces linear inspection with connected reasoning. 

Each borrower, guarantor, property, and fund becomes a node. Each link, shared ownership, co-signing, and litigation becomes an edge. This structure lets investigators and analysts trace multi-hop relationships that traditional databases flatten or overlook.

If Zions and Western Alliance had modeled their portfolios as graphs, a few queries could have exposed:

• Reused property collateral across institutions.
• Guarantors appearing in unrelated loans.
• Funds or LLCs acting as intermediaries between borrowers.
• Shared assets appearing in separate loan pools.

What spreadsheets treat as isolated rows, a graph shows as an interconnected cluster. Patterns that took months of forensic review would have appeared instantly through community detection or entity resolution algorithms.

Here’s how that looks:

From Detection to Reasoning

Fraud analytics built on isolated data can only detect anomalies after the fact. It tells you that something went wrong, but not why or how it spread. Graph analytics changes that by combining structural relationships with behavioral context, turning detection into reasoning.

• Entity resolution links records that appear under different names or ownership structures, matching addresses, phone numbers, or legal filings to the same real-world individuals or entities. A borrower might surface under multiple LLCs across different institutions, but graph connections expose that single identity.
• Community detection identifies clusters of entities that are connected in many ways, such as guarantors appearing across multiple banks or merchants sharing the same payment gateways. These hidden communities often form the backbone of organized fraud networks.
• Path analysis maps how one default or fraudulent action could cascade through shared assets, intermediaries, or guarantors. It identifies a failure but also shows the chain reaction that precedes it.

Together, these techniques create reasoning in motion. It’s a system that doesn’t just see transactions but understands their relationships. The result is actionable foresight. Insights are delivered in real-time, helping institutions respond before losses escalate.

Combining Graph and AI for Real-Time Insight

Traditional AI models can flag anomalies statistically, but they rarely understand why they occur. Pairing AI with graph reasoning bridges that gap. A hybrid graph + vector database allows AI to evaluate both similarity and connection at once:

• Graph traversal exposes how entities are actually related.
• Vector similarity finds semantically related information in documents, filings, or communications.
• Hybrid queries combine both, enabling cross-domain reasoning across structured and unstructured data.

This architecture supports real-time, explainable detection, letting investigators trace every flagged event back through the relationships that caused it. That transparency builds regulatory confidence and accelerates trust in AI-driven investigations.

From Reaction to Prevention

The Zions and Western Alliance disclosures showed how quickly uncertainty spreads once hidden exposure becomes visible. The larger takeaway, though, is about the urgency of connected risk intelligence.

With real-time graph analytics, financial institutions can:

• Detect shared borrowers or guarantors across loan portfolios.
• Model how a single failure could cascade through other lenders.
• Identify overlapping collateral and shell entities.
• Trigger early warnings as new entities connect to known risk clusters.

Graph reasoning can’t rewrite history, but it helps prevent repetition by making every transaction part of a connected, explainable network.

Why TigerGraph Powers Connected Fraud Detection

TigerGraph provides the graph database infrastructure that enables this level of insight. Its parallel computation engine analyzes billions of relationships per hour, correlating structured and unstructured data from multiple systems in real time.

With pre-built solution kits for fraud detection, AML, and customer intelligence, TigerGraph helps banks deploy connected-data analytics in days rather than months. Its hybrid graph + vector architecture brings relational reasoning and semantic search together, delivering faster insight, traceable results, and scalability across high-volume environments.

By uniting graph structure with AI reasoning, TigerGraph transforms risk management from reactive reporting to proactive intelligence, helping institutions uncover exposure before it becomes loss.

Summary

The Zions and Western Alliance lawsuits exposed the vulnerability of traditional systems to fragmented data. Fraud doesn’t hide in numbers; it hides in relationships.

Graph-based reasoning makes those connections visible in time to act. With TigerGraph’s hybrid graph + vector architecture, financial institutions gain a unified, explainable view of exposure. This reduces systemic risk and ensures that what once seemed invisible becomes immediately clear. 

Ready to Unlock Your Data’s Hidden Value? Reach out today to join thousands of developers and data scientists using TigerGraph’s leading graph analytics platform to solve complex problems with connected data. And start experimenting and prototyping at no cost, with a free TigerGraph Savanna. 

Frequently Asked Questions

How could banks have spotted overlapping borrowers or collateral earlier?

Traditional databases track each loan separately, so overlapping guarantors, shared properties, or recycled LLCs remain invisible.
A graph database connects every borrower, guarantor, and asset as part of a single network. By querying relationships instead of rows, analysts can instantly surface clusters of entities that appear across institutions—revealing shared exposure before defaults occur.

Why do traditional loan systems fail to catch coordinated fraud?

Conventional systems monitor transactions, not relationships. Each application, loan, or filing sits in its own silo, so suspicious links—like co-owned shell companies or duplicate collateral—go unnoticed. Graph analytics bridges these silos by showing how entities interact, exposing fraud rings that thrive precisely because legacy tools can’t “see sideways.”

What makes graph reasoning more powerful than anomaly detection alone?

Anomaly detection flags that something looks wrong. Graph reasoning explains why. By mapping how entities connect, a graph model shows the causal path between a suspicious borrower, guarantor, or fund. This transforms detection into explainable reasoning, helping investigators and regulators trace exactly how risk propagated through the network.

How does combining graph and AI improve financial investigations?

AI models excel at spotting statistical outliers; graphs reveal real-world relationships. When paired, graph + vector AI can uncover both semantic and structural similarities—linking documents, filings, and transactions that reference the same people or entities. The result is faster, more accurate insights with full transparency into every decision.

What advantages does TigerGraph bring to connected risk intelligence?

TigerGraph’s parallel MPP architecture and hybrid graph + vector database analyze billions of relationships in real time. Banks can run complex multi-hop queries across portfolios to detect hidden exposure, overlapping assets, and related guarantors—transforming fragmented risk data into proactive, explainable intelligence that prevents losses before they start.

Smarter Threat Detection Starts with Connected Security Analytics

Cyber threats evolve faster than most organizations can respond. Every day introduces new exploits, new identities, and new entry points. Yet most security systems still treat each log, alert, and anomaly as an isolated event. Without context, detection becomes guesswork.

Modern cybersecurity use cases powered by graph analytics replace guesswork with understanding. 

Graphs connect users, devices, and events into living maps of relationships. Analysts can see how one compromised credential spreads through a system, how lateral movement unfolds, and how separate alerts belong to the same campaign. The result is faster, smarter, and more explainable defense.

What are Cybersecurity Use Cases?

A cybersecurity use case defines a recurring scenario where connected data enhances detection, prevention, and investigation. In hybrid enterprises where cloud assets, IoT devices, and remote users constantly interact, relationships determine risk.

Graphs model these relationships in real time. Each node represents a user, endpoint, or application; each edge represents how they interact. This connected view allows teams to answer complex security questions that traditional systems cannot:
• Who accessed what, when, and from where?
• How are different alerts related across systems?
• Which devices or accounts bridges for malicious activity?

Common cyber security use case examples include intrusion detection, insider threat analysis, vulnerability prioritization, and incident response—all rooted in contextual awareness.

Key Cybersecurity Use Cases and Scenarios

• Threat Detection and Response
  Graphs correlate activity across firewalls, SIEM logs, and identity platforms. When a compromised account attempts privilege escalation and connects to a sensitive endpoint, the graph shows the entire attack path in seconds. 

Analysts can see not just the alert, but the context—who else that account communicated with, and what systems were affected. This reduces mean time to detection from hours to minutes.

• Insider Threat Detection
  Traditional monitoring tools evaluate users in isolation. Graph analytics links behaviors across departments, access rights, and peer groups.

It reveals when an employee, contractor, or compromised identity behaves abnormally compared to others with similar privileges. Analysts can see relationships between suspicious downloads, unexpected file access, and lateral communications—reducing false positives common in rule-only systems.

• Phishing and Credential Abuse
  Credential reuse and phishing remain the most common attack vectors. By connecting email metadata, domain reputation, login history, and geolocation, graphs reveal coordinated campaigns behind single incidents. 

Analysts can visualize clusters of accounts interacting with known malicious domains or IPs, exposing a broader campaign before damage spreads.

• Vulnerability and Patch Management
  Graphs help security teams prioritize what truly matters. 

Instead of ranking issues solely by CVSS score, graph analytics shows which vulnerabilities sit on the shortest path to high-value assets. It connects systems, applications, and dependencies, revealing risk in context. This turns long static patch lists into real-time visual maps of exposure.

• Fraud and Financial Crime Integration
  Cybersecurity and fraud are deeply connected. Graph analytics bridges the two by linking IP addresses, devices, and payment accounts.

Illustrative example: When a compromised mobile device logs into multiple bank accounts, the graph reveals that it’s acting as a common node between otherwise unrelated users—potential evidence of a mule or credential-stuffing attack.

• Incident Investigation and Forensics
  After a breach, time matters. Graphs provide instant lineage from the first alert to the final point of exfiltration.

Analysts can trace how attackers moved, what systems they touched, and which accounts they used. Instead of sifting through unconnected logs, they follow a clear, visual path showing cause and effect.

These cybersecurity use cases illustrate how connected data transforms isolated events into actionable insight.

How Security Analytics Use Cases Improve Detection?

Traditional Security Information and Event Management (SIEM) systems rely on linear event correlation, missing the multi-step logic of modern attacks.

Graph-powered security analytics interprets paths rather than timestamps, uncovering relationships that span users, devices, and timeframes. It reveals how small, routine events combine into a single coordinated breach.

By restoring context, graph analytics converts event review into reasoning. Analysts can distinguish coincidence from coordination, noise from narrative.

Industry-Specific Cybersecurity Use Cases

Each sector faces unique attack surfaces, yet all share one constant: context determines defense. The following are illustrative examples, not real deployments.

Finance:
Banks and payment processors face credential theft, account takeovers, and synthetic identity fraud. Graph analytics links transactions, device fingerprints, and access history, showing how small anomalies cluster into organized financial attacks. This helps analysts isolate shared identifiers among multiple accounts, and trace fraudulent patterns before they scale.

Healthcare:
Protecting patient data requires visibility across complex, often disconnected, systems. Graphs map relationships between care providers, EHR applications, and connected medical devices. They help detect unauthorized access or lateral movement inside hospital networks without interrupting clinical workflows.

Telecommunications:
With millions of users and devices, telecom networks are prime targets for SIM swaps, rogue access points, and insider manipulation. Graphs connect subscriber profiles, device IDs, and location data to detect abnormal relationships—such as repeated SIM changes linked to the same billing details or IP range.

Manufacturing:
Industrial IoT brings efficiency but also exposure. Graphs monitor relationships between PLCs, sensors, and production systems. They reveal anomalies like machines communicating outside expected cycles or commands originating from unauthorized devices—signs of malware or sabotage.

Public Sector:
Government agencies combine cyber intelligence, public data, and national infrastructure. Graph analytics aids entity resolution and case correlation. It helps analysts identify when two seemingly unrelated investigations share common digital infrastructure, like the same command-and-control servers.

Across all industries, one principle holds: relationships define risk. When those relationships are visible, response becomes proactive instead of reactive.

Building a Graph-Driven Security Program

Adopting graph analytics does not mean replacing existing defenses—it means enhancing them. Graph technology complements SIEM, SOAR, and endpoint solutions by adding the context those systems were never built to process. A mature rollout follows deliberate, progressive steps.

• Identify your most valuable data sources.
  Start with the systems that already collect rich behavioral data—your SIEM, IAM, EDR, and network monitoring tools. These contain the signals that, when connected in a graph, reveal coordinated attacks and hidden lateral movement. Prioritizing them first gives your team immediate visibility into the areas that matter most.
• Define your common entities.
  Consistency makes context possible. Every user, device, IP, domain, and process should follow the same definitions across all data feeds. This unified schema becomes the foundation for accurate queries, clear visualizations, and reliable investigations. Without it, duplication and confusion creep in fast.
• Use graph algorithms to uncover patterns.
  Once relationships are mapped, algorithms turn them into insight. Community detection exposes clusters of compromised accounts. Centrality measures highlight which systems are most critical—or most at risk. Similarity analysis connects activities that may share the same threat origin. Together, these techniques transform disconnected data into actionable intelligence.
• Automate investigations and response.
  Automation turns knowledge into speed. Prebuilt graph queries can instantly retrace lateral-movement paths, connect indicators of compromise, and group related alerts. Analysts stop chasing isolated incidents and start resolving full attack campaigns in record time.
• Combine graph analytics with artificial intelligence.
  Machine learning enhances graph reasoning by spotting subtle deviations from normal behavior. The two technologies strengthen each other: the graph explains why something is suspicious, while AI predicts what might happen next. The result is an adaptive, transparent defense that learns and improves as threats evolve.
• Maintain continuous feedback and governance.
  Graph-driven security is a living system. Teams should regularly refine entity definitions, update algorithms, and review permissions. Governance keeps the model accurate, compliant, and resilient—so it adapts as your business, data, and threat landscape change.

When these elements converge, cybersecurity shifts from event management to knowledge management. Every connection strengthens understanding, and every investigation improves the model.

Regulatory and Business Impact

Explainability is not only a best practice—it is a regulatory requirement. Graph analytics offers full traceability of how alerts connect and why each decision was made. That transparency supports compliance frameworks such as NIST, ISO 27001, and regional data-protection mandates.

Operationally, connected context reduces false positives, shortens investigation cycles, and fosters collaboration among IT, fraud, and risk teams. Business leaders gain measurable ROI through efficiency, audit readiness, and reduced incident impact.

Illustrative benchmark: Enterprises implementing graph-powered security analytics have reported reductions in false positives of up to 50 percent and investigation time cut from hours to minutes—improvements that translate directly to cost savings and resilience.

How Does TigerGraph Support Cybersecurity Analytics?

TigerGraph provides the enterprise-grade graph database foundation that powers contextual cybersecurity analytics. Its native parallel architecture supports real-time traversal across billions of connections, enabling analysts to detect lateral movement, credential abuse, and insider threats in seconds.

Security teams use TigerGraph to integrate security analytics use cases seamlessly with existing SIEM, SOAR, and threat-intelligence systems. The platform delivers correlation, context, and explainability while meeting strict regulatory requirements.

Because it scales dynamically across hybrid architectures, TigerGraph empowers organizations to unify detection, response, and compliance under one connected framework. Every relationship adds visibility. Every query produces traceable insight.

Summary

Modern cybersecurity depends on context. Graph-based security analytics links users, devices, and events into a single network of understanding.

From insider threats to advanced persistent attacks, graphs turn data into defense—revealing how incidents unfold, where they intersect, and how to stop them faster.

TigerGraph enables this transformation at enterprise scale. It provides the speed, scalability, and explainability required for connected, compliant, and continuously improving cybersecurity.

Visit TigerGraph.com to see how graph-powered security analytics helps organizations protect what matters most.

We are thrilled to announce the appointment of two outstanding executives to TigerGraph’s leadership team. Mike Crane joins us as Chief Customer Officer, and Paige Leidig takes on the role of Chief Marketing Officer. These game-changing appointments reflect our commitment to putting customers at the heart of everything we do, expanding our market presence, and driving sustainable growth through excellence in leadership.

Mike brings over two decades of experience in transforming customer success and services. Mike will lead our efforts to enhance the overall customer experience by establishing impactful, long-term relationships with our clients. He will focus on enabling positive business outcomes that drive loyalty, adoption, and growth for TigerGraph’s customers. 

Paige brings over 20 years of enterprise marketing leadership and an impressive track record of driving growth, building brands, and elevating market awareness for category-defining organizations.  Paige will lead our marketing organization with a mission to enhance brand visibility and communicate our unique value proposition to key verticals, use cases, and regions. His leadership will drive growth and showcase the transformational impact we deliver to our customers and the market alike. 

The addition of Mike and Paige to TigerGraph’s leadership team is a pivotal step in our pursuit in accelerating toward our vision and key goals of reaching and empowering more organizations with our industry-leading graph analytics platform, collaborating with global leaders to enable breakthrough business outcomes, driving loyalty and adoption by prioritizing exceptional customer experiences, and building long-term strategies to ensure consistent and scalable success. 

TigerGraph is committed to delivering powerful graph analytics and AI solutions that help organizations unlock hidden insights for critical use cases including fraud, money laundering, entity resolution, supply chain and more in order to achieve unmatched competitive advantage. With Mike and Paige steering critical components of our mission, we are confident in our ability to meet and exceed the evolving needs of our customers and the market. 

We look forward to the incredible impact Mike and Paige will make on our customers, our team, and the market.

Today, we welcome Rajeev Shrivastava as our new CEO at TigerGraph! Rajeev is a highly accomplished technology executive with a wealth of experience in the software industry. We’re thrilled to have him on board to lead TigerGraph on a mission to unlock the full potential of data through our groundbreaking graph database platform.

Rajeev joins us from Google, where he served as GM and Product Lead for an AI-first Customer Conversation Platform. With his deep expertise in data analytics and AI, Rajeev’s leadership will be instrumental in helping TigerGraph continue to innovate and deliver the power of graphs to our customers.

He is passionate about our platform’s ability to reveal deep connections and insights from vast amounts of data to gain understanding of customer behavior, preferences and needs.

Recently, I had an opportunity to chat with Rajeev about his background, experience, and what attracted him to TigerGraph. Here are some highlights from our conversation:

Q: Rajeev, can you tell us a little bit about your background?

Rajeev: Certainly! My journey through the technology landscape, including leadership roles at Google, NICE and Rackspace, has equipped me with a deep understanding of the critical role that technology plays in driving business success. My career has taken me through some of the most dynamic and innovative technology companies,  where I led strategic initiatives and product development efforts. I hold an MBA in Strategy and Finance from the Wharton School, University of Pennsylvania, and a Bachelor’s degree in Civil Engineering from Delhi College of Engineering, University of Delhi. Outside of work, I enjoy spending time with my family, exploring new technologies, and staying active through various outdoor activities.

Q: What attracted you to TigerGraph?

Rajeev: TigerGraph’s unique position in the graph analytics and AI space, in terms of scale and depth, was a significant draw for me. The ability to uncover deep connections and insights from vast amounts of data is unparalleled. I was also impressed by the talented team and the strong foundation that has been laid. The potential for growth and innovation here is immense, and I am excited to lead TigerGraph into its next chapter.

Q: What is your vision for TigerGraph’s future?

Rajeev: My vision for TigerGraph revolves around three key pillars: innovation, customer success, and growth.

We will continue to push the envelope, developing cutting-edge solutions that leverage the power of graph technology. This will empower our customers to solve complex problems to improve business outcomes while enhancing customer experience and satisfaction.

Our customers are at the heart of everything we do. We will focus on delivering exceptional value, ensuring that our solutions drive tangible outcomes to help our customers achieve their goals. By listening to their needs and understanding their challenges, we will build stronger, more productive relationships.

We will expand our market presence, forge strategic partnerships and cultivate a high-performance culture to drive sustainable growth. By scaling our business and investing in our teams, we will ensure that TigerGraph continues to thrive and that more customers can benefit from our innovations.

I am excited about the journey ahead and the opportunity to work alongside the talented team at TigerGraph. Together, we will build on the strong foundation that has been laid and chart a course for sustained growth and innovation, with a relentless focus on customer success.

We are excited about the journey ahead with Rajeev leading the way. Welcome to TigerGraph, Rajeev!