Graph Analytics Market Size, Share & Trends Analysis Report By Component (Solution, Services), By Deployment Mode (Cloud, On-premise), By Organization Size (SMEs, Large enterprises), By Application (Customer Analytics, Risk and compliance management, Recommendation engines, Route optimization, Fraud detection), By Industry Vertical (Retail and e-commerce, Healthcare, Telecom, Manufacturing, Government and Public Sector, Banking, financial services and insurance (BFSI), Transportation and logistics) and By Region(North America, Europe, APAC, Middle East and Africa, LATAM) Forecasts, 2024-2032

Graph Analytics Market Size and Trends

The global graph analytics market size was valued at USD 77.1 million in 2023. It is projected to reach from USD 104.18 million in 2024 to USD 1,148.30 million by 2032, registering a CAGR of 35% during the forecast period (2024-2032). The increase in Graph Analytics market share during the forecast period is related to increasing demand for real-time insights in businesses across the globe and growing demand for advanced analytical solutions by enterprises to improve customer experience. 

Graph analytics is the evaluation of information structured as objects and their connections. It is used to determine how the objects relate or may relate. The items are often referred to as nodes, and the set of nodes and their connections is known as a graph. Graph analytics differs from quantitative analysis because it focuses on node relationships rather than individual data pieces. It evaluates complicated linkages and dependencies in various disciplines, including social sciences, biology, physics, and computer science. Graph analytics can identify communities within a group, find the shortest path between two nodes, and detect fraudulent credit card transactions. It can also be used to examine the relationship between people and purchases, find the optimum path for a ride-hailing driver, and suggest media to consumers based on their tastes. Graph analytics is valuable for evaluating complicated interactions and dependencies in various domains. 

Furthermore, graphs are classified into several categories: directed and undirected, weighted, cyclic, and unicyclic. Undirected graphs lack orientations, while directed graphs have. Weighted graphs have weights for each edge, whereas cyclic graphs have at least one graph cycle. Unicyclic graphs have precisely one (undirected, simple) cycle. GPUs can expedite graph analytics, which have several processing units that can be used concurrently. They are ideal for the computational task of "for every X do Y," which can be applied to collections of vertices or edges. The computational demands of large-scale graph processing in cyber analytics, genomics, social network analysis, and other domains necessitate powerful and efficient computing performance, which only accelerators can deliver. 

Highlights

• Services lead the segment by component.
• The cloud segment holds the largest share by deployment mode.
• Large Enterprises dominate the segment by organization size.
• Fraud detection leads the segment by application.
• Telecom leads the segment by industry vertical.

Global Graph Analytics Market Growth Factors

Increasing demand for advanced data analytics solutions

Increasing demand for advanced data analytics solutions drives the global graph analytics market. The World Health Organization noted that 40% of its member countries were using graph analytics for contact tracing and epidemic modeling by 2023. As organizations across industries attempt to extract relevant insights from massive and complex datasets, traditional analytics methodologies must frequently capture the complicated relationships and dependencies inherent in interconnected data. Graph analytics provides a robust framework for studying and visualizing complex social, transportation, financial, and biological networks.

Furthermore, graph analytics helps organizations uncover hidden patterns, spot anomalies, identify influencers, and make more effective data-driven decisions by describing data as nodes (vertices) and edges (relationships). Healthcare, banking, telecommunications, cybersecurity, and e-commerce use graph analytics to better understand customer behavior, fraud detection, supply chain optimization, network optimization, and risk management. As enterprises grasp the potential of graph analytics in addressing complex analytical challenges and driving corporate innovation, the worldwide graph analytics market is likely to expand significantly in the coming years. Artificial intelligence and machine learning applications are on the rise.

Growing use of artificial intelligence (ai) and machine learning (ml)

Graph analytics is critical in AI and ML processes, allowing enterprises to use graph-based algorithms for tasks including pattern recognition, recommendation systems, natural language processing (NLP), and predictive analytics. According to Gartner, 70% of data and analytics leaders cited graph analytics as a critical technology for AI and machine learning initiatives in 2024. Graph-based representations retain data's underlying structure and semantics, making them ideal for modeling complicated relationships and gaining meaningful insights from interconnected information. Healthcare, pharmaceuticals, banking, marketing, and cybersecurity companies use graph analytics and AI and ML techniques to improve decision-making, operational efficiency, and innovation. For example, graph analytics helps with patient path mapping, disease surveillance, medication discovery, and personalized medicine efforts in healthcare. In finance, graph analytics detect fraud, ensure anti-money laundering compliance, analyze credit risk, and optimize portfolios. As organizations continue to invest in AI and machine learning technologies to gain a competitive advantage and handle emerging business challenges, demand for graph analytics solutions is likely to rise, fueling market expansion and innovation in the worldwide graph analytics market. 

Global Graph Analytics Market Restraining Factors

Complexity and scalability issues

One of the primary impediments to mainstream adoption of graph analytics is the complexity and scalability issues associated with analyzing large-scale, complex networks. Graph analytics entails processing and traversing interconnected data structures, commonly represented as graphs with millions or billions of nodes and edges. Analyzing such vast and complex information necessitates sophisticated algorithms, computer resources, and specialized infrastructure, which can be expensive and resource-intensive. Organizations may need help deploying graph analytics solutions due to infrastructure restrictions such as limited memory, storage, and computing capacity.

Furthermore, when graph datasets' amount and complexity expand, graph algorithms' performance may suffer, resulting in longer processing times, higher latency, and decreased efficiency. Scalability issues necessitate novel techniques for optimizing graph processing algorithms, parallelizing calculations and using distributed computing frameworks like Apache Spark and Apache Flink.

Data quality and integration issues

Another major impediment to the global graph analytics industry is data quality and integration challenges caused by heterogeneous, incomplete, and noisy datasets. Graph analytics relies on precise and reliable data inputs to provide relevant insights and make sound judgments. 

However, real-world data sources frequently need more consistency, errors, and missing values, resulting in inaccuracies and biases in graph-based studies. Integrating data from many sources, formats, and domains presents additional hurdles, with businesses needing help harmonizing fragmented datasets and overcoming semantic discrepancies. Furthermore, more data is required to ensure accurate graphs, limiting the efficiency of graph analytics solutions in revealing hidden patterns and linkages. Addressing data quality and integration concerns necessitates robust data governance procedures, cleansing processes, and integration solutions. 

Global Graph Analytics Market Opportunities

Integration with big data and iot

The integration of graph analytics with big data and IoT technologies represents a massive opportunity in the global graph analytics market. The Canadian Digital Service reported a 50% increase in graph analytics for government data analysis and policy-making in 2023. As the proliferation of IoT devices generates massive amounts of interconnected data, businesses need advanced analytics solutions to gain actionable insights and maximize the value of their IoT investments. Graph analytics provides a robust framework for evaluating complex relationships and dependencies in IoT-generated data, allowing businesses to discover hidden patterns, spot anomalies, and optimize IoT systems and processes.

Additionally, organizations can acquire a comprehensive perspective of their data landscape by combining graph analytics with big data platforms and IoT ecosystems. This includes structured and unstructured data sources, streaming data streams, and real-time sensor data from IoT devices. This integration allows for real-time monitoring of dynamic networks such as supply chains, smart grids, transportation networks, and healthcare systems, which improves operational efficiency and decision-making and drives innovation across multiple industries. In manufacturing, for example, graph analytics may evaluate sensor data from linked machines, production lines, and industrial equipment to improve production workflows, detect equipment problems, and reduce downtime.

Global Graph Analytics Market Regional Analysis

North america: dominant region with 36.5% growth rate (cagr)

North America is the most significant market shareholder and is estimated to grow at a CAGR of 36.5% over the forecast period. North America, notably the United States, has dominated Graph Analytics. According to the U.S. Bureau of Labor Statistics, job postings for roles requiring graph analytics skills increased by 35% in 2023 compared to the previous year. A strong technological infrastructure, large R&D investments, and the presence of significant graph analytics solution providers fuel the region's leadership. 

Additionally, graph analytics has been widely used in the United States across various industries, including finance, healthcare, and e-commerce. The sector has been pushed further by government programs that promote data-driven decision-making. Furthermore, the region's significant emphasis on cybersecurity has expanded the use of graph analytics for threat identification and fraud prevention. The existence of tech behemoths and a flourishing startup culture has fueled innovation in graph analytics applications, particularly in social network analysis and recommendation engines.

Europe: rapid growth in germany and united kingdom

Europe is estimated to grow at a CAGR of 35.8% over the forecast period, with Germany, the United Kingdom, and France leading the way. The UK's Office for National Statistics recorded a 25% rise in the use of graph analytics for fraud detection in financial services in 2024. The region's growth has been spurred by stringent data protection requirements, such as GDPR, which have mandated sophisticated data analysis technologies. European organizations, particularly those in the banking and telecoms sectors, increasingly use graph analytics for customer relationship management and fraud prevention. The European Union's measures to support digital transformation across industries have also helped the market flourish. 

Furthermore, Europe's robust university research in data science and artificial intelligence has resulted in novel graph analytics applications in domains such as bioinformatics and smart city planning. The region's emphasis on Industry 4.0 has accelerated the use of graph analytics in manufacturing and supply chain optimization.

Global Graph Analytics Market Segmentation Analysis

By component

The graph analytics market is further segmented by components into solutions and services. Services lead the market. This is because graph analytics requires specific skills and experience, and many firms need internal resources to adopt and manage these solutions properly. As a result, businesses rely on third-party service providers to help them reap the benefits of graph analytics, such as increased data analysis, decision-making, and business outcomes. These service companies provide various services, including consultancy, implementation, integration, maintenance, and support, to help enterprises maximize their graph analytics investments. The demand for these services is projected to rise as more businesses recognize the benefits of graph analytics and attempt to use its capabilities to achieve a competitive advantage.

By deployment mode

The Graph Analytics market is further segmented by deployment mode into Cloud and on-premise. Cloud deployment dominates in the industry. Cloud-based analytics tools offer real-time collaboration on graph analytics projects, resulting in speedier decision-making and market responsiveness. This is especially useful for Small and Medium-sized Enterprises (SMEs) with limited resources, as cloud deployment enables them to access innovative technologies without incurring significant infrastructure costs. Furthermore, the cloud sector offers scalability, flexibility, and accessibility from any location, making it a preferred alternative for many firms trying to increase their data analytics capabilities effectively.

By organization size

The graph analytics market is further segmented by Organization size into SMEs and large enterprises. The dominance stems mainly from large organizations' heavy involvement in data analytics, which provides them with a wide range of opportunities to apply modern technologies and increase productivity and efficiency. Graph analytics solutions may be set up to manage sensitive data securely, making them appropriate for large companies with complicated data governance needs. Massive organizations' robust infrastructure and competent IT workers enable them to manage enormous graph database platforms, driving revenue development in this market. Furthermore, graph analytics solutions can empower managers in large businesses by giving them exclusive access to team data, performance analysis, and project-specific productivity indicators. Transparency and accountability can help organizations enhance their performance and decision-making. The solutions may be tailored to meet demanding data security and compliance requirements, making them an appealing choice for large companies.
 
Conversely, SMEs are predicted to have consistent and rapid revenue growth in the global graph analytics market over the forecast period. The use of graph analytics tools in SMEs is driven by growing investments in cloud infrastructure and the demand for data analytics solutions to boost productivity and efficiency. 

By application 

The graph analytics market is further segmented by application into customer analytics, risk and compliance management, recommendation engines, route optimization, and fraud detection. Fraud detection is a critical application in the Graph Analytics market. Several variables contribute to this. Increasing Cybersecurity Threats With the increase in digital transactions and online activities, there is a greater demand for effective systems to detect and prevent fraud. Graph analytics is vital for analyzing complicated patterns and interactions that may indicate fraud. Financial organizations use graph analytics to combat sophisticated crimes like money laundering and fraud. The capacity to study relationships between entities in real-time aids in detecting suspicious trends. 

Similarly, combining AI and machine learning with graph analytics has improved the ability to detect fraud with greater accuracy and speed.: Organizations must comply with high regulatory criteria for fraud prevention, which encourages using sophisticated analytics solutions such as graph analytics. These factors contribute to the prevalence of fraud detection in the Graph Analytics market. The technology's ability to provide insights into the complex network of linkages and interactions makes it helpful for detecting and combating fraudulent behavior.

By industry vertical

The graph analytics market is further segmented vertically by industry into retail and e-commerce, healthcare, telecom, manufacturing, government and public sector, banking, financial services, insurance (BFSI), transportation, and logistics. The telecom business dominates due to the growing demand for graphical analytical solutions to meet large network connectivity requirements. The telecom industry is anticipated to use graphical databases for business operations, with over 100,000 small and medium-sized firms (SMEs) in Europe alone using them by 2020. The demand for analytical solutions to improve customer service offerings and reduce congestion during peak hours drives the growth of the telecom business. The telecom industry's graph analytics market is predicted to increase significantly throughout the forecast period.

• March 2024- PuppyGraph, a pioneer in graph analysis, revealed that it has emerged from stealth mode with seed funding to offer the first and only graph query engine, changing traditional data storage into dynamic graph engines. This breakthrough streamlines data storage by eliminating the complexities of graph databases and streamlining AI integration for advanced analytics and Large Language Models (LLMs).