Industrial Knowledge Graphs Market Outlook 2025-2034

The global industrial knowledge graphs market was valued at $1.8 billion in 2025 and is projected to reach $7.4 billion by 2034, expanding at a compound annual growth rate (CAGR) of 17.0% over the forecast period 2026-2034, driven by surging demand for semantically rich, interconnected industrial data architectures that enable real-time decision intelligence across complex manufacturing and operational environments.

The primary growth engine propelling the industrial knowledge graphs market through the forecast horizon is the accelerating convergence of Industry 4.0 paradigms with advanced graph-based data modeling frameworks. As industrial enterprises globally grapple with the exponential proliferation of heterogeneous data from sensors, SCADA systems, ERP platforms, MES solutions, and edge devices, the limitations of traditional relational databases and flat data lakes have become increasingly apparent. Knowledge graphs address this gap by providing a flexible, ontology-driven schema that maps entities, relationships, and contextual metadata into a unified, machine-readable knowledge base. In 2025, it is estimated that more than 68% of Fortune 500 industrial companies have initiated or are actively piloting knowledge graph deployments for at least one operational use case, up from approximately 41% in 2022. The ability of knowledge graphs to power semantic search, root cause analysis, digital twin enrichment, and AI model training has made them an indispensable layer in the modern industrial data stack. Investments in Industrial Internet of Things (IIoT) infrastructure reached approximately $110 billion globally in 2025, and knowledge graphs are increasingly positioned as the semantic interoperability layer that transforms raw IIoT telemetry into actionable operational intelligence. Furthermore, the rapid maturation of large language model (LLM) integrations with graph databases is creating entirely new use cases in natural-language querying of plant-floor data, automated compliance documentation, and AI-assisted engineering design, all of which are expected to sustain double-digit growth rates well into the early 2030s.

Industrial Knowledge Graphs Market Outlook

According to our latest research, the Global Industrial Knowledge Graphs market size was valued at $1.2 billion in 2024 and is projected to reach $7.8 billion by 2033, expanding at a CAGR of 23.5% during 2024–2033. The primary factor propelling this remarkable growth is the accelerating adoption of advanced data analytics and artificial intelligence across industrial sectors, which is driving demand for knowledge graphs as organizations strive to harness complex, interconnected data for actionable insights. The industrial sector’s push towards digital transformation, coupled with the need for real-time decision-making and improved operational efficiency, is positioning knowledge graphs as a foundational technology for the future of industrial intelligence.

Regional Outlook

North America currently dominates the Industrial Knowledge Graphs market, commanding the largest market share, with a value exceeding $450 million in 2024. This region's leadership is attributed to its mature digital infrastructure, high rate of technology adoption, and a robust ecosystem of industrial automation and AI vendors. The presence of leading technology companies, coupled with a favorable regulatory environment that encourages innovation and data-driven decision-making, has further accelerated the deployment of knowledge graph solutions. Major industries such as manufacturing, oil & gas, and automotive in the United States and Canada are leveraging knowledge graphs for advanced analytics, predictive maintenance, and supply chain optimization, thus reinforcing North America’s market supremacy.

In terms of growth velocity, Asia Pacific stands out as the fastest-growing region, with an impressive projected CAGR of 27.2% from 2024 to 2033. This surge is primarily driven by substantial investments in smart manufacturing, industrial IoT, and digital transformation initiatives across China, Japan, South Korea, and India. The rapid industrialization and the increasing focus on Industry 4.0 adoption are pushing enterprises to integrate knowledge graphs into their operational frameworks. Additionally, government incentives and policies supporting technological innovation, along with the expansion of cloud infrastructure, are further catalyzing market growth in the region. The Asia Pacific market is also witnessing heightened activity from both global and regional vendors, intensifying competition and fostering innovation.

Emerging economies in Latin America and Middle East & Africa are gradually entering the Industrial Knowledge Graphs market, although adoption remains in the nascent stages. These regions face unique challenges, including limited digital infrastructure, skill shortages, and regulatory uncertainties. However, localized demand is rising as industrial players recognize the potential of knowledge graphs to drive efficiency and competitiveness. In countries like Brazil, South Africa, and the UAE, government-led digitalization programs and foreign direct investments are beginning to unlock new opportunities. Nevertheless, the pace of adoption is tempered by the need for tailored solutions that address specific regional and industry requirements, as well as ongoing efforts to bridge the digital divide.

Report Scope

Industrial Knowledge Graph Platform Market Outlook

Based on our latest research, the global Industrial Knowledge Graph Platform market size was valued at USD 1.23 billion in 2024, with a robust compound annual growth rate (CAGR) of 25.8% expected through the forecast period. With this trajectory, the market is projected to reach USD 9.08 billion by 2033. This exponential growth is fueled by the surge in industrial digitalization, the increasing need for contextual data integration, and the adoption of artificial intelligence (AI) and machine learning (ML) across industrial sectors. The market’s rapid expansion is underpinned by the critical role that knowledge graph platforms play in unifying disparate data sources, driving operational efficiency, and enabling advanced analytics for enterprise decision-making.

One of the primary growth drivers for the Industrial Knowledge Graph Platform market is the escalating demand for real-time, context-rich insights across industrial operations. As industries such as manufacturing, energy, and automotive embrace Industry 4.0 principles, the volume and complexity of data generated from interconnected devices and systems have increased dramatically. Knowledge graph platforms excel at integrating structured and unstructured data from diverse sources, enabling organizations to create a comprehensive, interconnected view of their assets, processes, and supply chains. This capability is crucial for enhancing operational transparency, optimizing resource allocation, and supporting predictive analytics, which collectively contribute to improved productivity and reduced downtime.

Another key factor propelling market growth is the widespread adoption of AI and ML technologies within industrial environments. Industrial knowledge graph platforms serve as foundational infrastructure for advanced AI applications by providing a semantic layer that contextualizes data relationships. This semantic enrichment empowers AI-driven solutions to deliver more accurate predictions, uncover hidden patterns, and automate complex decision-making processes. As organizations strive to achieve greater agility and resilience in the face of global supply chain disruptions and evolving regulatory requirements, knowledge graph platforms are increasingly seen as indispensable tools for digital transformation and competitive differentiation.

Furthermore, the growing emphasis on asset management, risk mitigation, and process optimization is fueling the adoption of industrial knowledge graph platforms. These platforms facilitate holistic visibility into asset lifecycles, maintenance schedules, and operational risks by connecting siloed data repositories and enabling cross-domain analytics. Industries such as oil & gas, pharmaceuticals, and chemicals, which operate in highly regulated environments, benefit significantly from the ability to trace data lineage, ensure compliance, and proactively manage risks. The integration of knowledge graphs with existing enterprise systems, including ERP, MES, and SCADA, further enhances their value proposition by streamlining workflows and supporting real-time decision-making.

Regionally, North America leads the global market, driven by early technology adoption, strong presence of key vendors, and significant investments in industrial IoT and AI initiatives. Europe follows closely, supported by robust manufacturing and automotive sectors, as well as stringent regulatory standards that encourage data integration and transparency. The Asia Pacific region is witnessing the fastest growth, propelled by rapid industrialization, government-led digitalization programs, and the proliferation of smart manufacturing initiatives in countries such as China, Japan, and South Korea. Latin America and the Middle East & Africa are also experiencing steady growth, albeit from a smaller base, as local industries increasingly recognize the value of knowledge graph platforms for operational excellence and risk management.

Component Analysis

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Manufacturing Knowledge Graph Platform Market Outlook 2025-2034

The global manufacturing knowledge graph platform market was valued at $1.8 billion in 2025 and is projected to reach $6.9 billion by 2034, expanding at a compound annual growth rate (CAGR) of 16.1% during the forecast period from 2026 to 2034. Manufacturing knowledge graph platforms are purpose-built semantic data infrastructure solutions that connect heterogeneous operational datasets, machine records, supplier hierarchies, regulatory requirements, and product lifecycle data into a unified, queryable graph model, enabling manufacturers to unlock cross-domain intelligence at scale. The escalating complexity of modern factory ecosystems, driven by the convergence of operational technology (OT) and information technology (IT), has generated an urgent demand for platforms capable of contextualizing data beyond traditional relational databases. As manufacturers in automotive, aerospace, electronics, and pharmaceutical sectors accelerate their digital transformation journeys, the need to seamlessly link bill-of-materials data, sensor readings, maintenance logs, and compliance documentation into an interconnected knowledge layer has become a strategic imperative. The market is experiencing robust momentum from the accelerated deployment of Industrial Internet of Things (IIoT) sensors, which generated an estimated 78 billion data points daily across global manufacturing floors in 2025 alone. Knowledge graph platforms address the fundamental challenge of data silos by enabling real-time semantic linking, helping manufacturers reduce unplanned downtime by up to 32%, cut supplier qualification cycles by 41%, and improve quality defect traceability response times by 55%. The integration of generative AI and large language models (LLMs) with knowledge graph architectures has further amplified market enthusiasm, with leading vendors such as Microsoft, IBM, and Neo4j embedding natural-language query interfaces directly into their platforms as of early 2026. Government-backed smart manufacturing initiatives in China, Germany, South Korea, and the United States continue to inject significant capital into digital infrastructure, creating fertile ground for knowledge graph platform adoption across both large enterprises and mid-market manufacturers. The market is further propelled by tightening regulatory regimes including the EU's Digital Product Passport mandate and the U.S. FDA's drug traceability requirements under the Drug Supply Chain Security Act (DSCSA), which compel manufacturers to maintain verifiable, machine-readable provenance records across their entire value chains.

Manufacturing Knowledge Graph Platform Market Outlook

According to our latest research, the Global Manufacturing Knowledge Graph Platform market size was valued at $1.2 billion in 2024 and is projected to reach $7.8 billion by 2033, expanding at a robust CAGR of 23.6% during the forecast period of 2025–2033. One of the major factors propelling this market’s growth is the rapid integration of artificial intelligence (AI) and machine learning (ML) technologies to enhance data interoperability, streamline manufacturing processes, and enable real-time, data-driven decision-making across complex industrial environments. As manufacturers increasingly seek to leverage interconnected data sources for operational agility, the adoption of knowledge graph platforms is becoming a strategic imperative globally.

Regional Outlook

North America currently commands the largest share of the Manufacturing Knowledge Graph Platform market, accounting for approximately 38% of the global market value in 2024. This dominance is underpinned by the region’s mature manufacturing ecosystem, robust digital infrastructure, and early adoption of Industry 4.0 initiatives. The United States, in particular, has witnessed significant investments in smart manufacturing, driven by both public and private sector initiatives aimed at fostering digital transformation. Additionally, the presence of leading technology vendors and a high concentration of Fortune 500 manufacturing enterprises have accelerated the deployment of knowledge graph platforms. Regulatory frameworks supporting data interoperability and cybersecurity further catalyze adoption, positioning North America as the benchmark for innovation in this domain.

Asia Pacific emerges as the fastest-growing region in the Manufacturing Knowledge Graph Platform market, expected to register an impressive CAGR of 27.8% between 2025 and 2033. This growth trajectory is fueled by the rapid industrialization of economies such as China, Japan, South Korea, and India, where manufacturers are embracing digital transformation to enhance competitiveness and operational efficiency. Government-led initiatives like China’s Made in China 2025 and Japan’s Society 5.0 are propelling investments in smart manufacturing technologies, including knowledge graph platforms. The region’s burgeoning electronics, automotive, and pharmaceutical sectors are particularly active in deploying these solutions to address complex supply chain and quality management challenges. Strategic partnerships between global technology leaders and local manufacturing giants are further accelerating market penetration.

In contrast, emerging economies across Latin America, the Middle East, and Africa are in the nascent stages of adopting Manufacturing Knowledge Graph Platforms, facing unique challenges such as limited digital infrastructure, skills shortages, and regulatory ambiguities. However, localized demand is gradually increasing as multinational manufacturers expand their footprints and seek to harmonize operations across global sites. Policy reforms and incentives aimed at industrial modernization are beginning to take shape, especially in countries like Brazil, Mexico, and the United Arab Emirates. Nevertheless, the pace of adoption remains uneven, constrained by capital investment limitations and the need for tailored solutions that address region-specific manufacturing and data governance requirements.

Report Scope

Industrial Knowledge Graph Platform Market Outlook

According to our latest research, the Global Industrial Knowledge Graph Platform market size was valued at $1.2 billion in 2024 and is projected to reach $6.8 billion by 2033, expanding at a robust CAGR of 20.7% during 2024–2033. One of the major growth drivers for the global industrial knowledge graph platform market is the increasing adoption of advanced data analytics and artificial intelligence (AI) technologies across industrial sectors. These platforms enable enterprises to create interconnected data ecosystems, drive real-time insights, and streamline decision-making processes, which are critical for maintaining competitiveness in the era of Industry 4.0. The convergence of IoT, big data, and cloud computing with knowledge graph technologies further accelerates digital transformation initiatives, allowing organizations to enhance operational efficiency and unlock new revenue streams.

Regional Outlook

North America currently holds the largest share of the industrial knowledge graph platform market, accounting for approximately 38% of the global revenue in 2024. This dominance can be attributed to the region’s mature industrial base, rapid adoption of cutting-edge digital technologies, and the presence of leading technology vendors. The United States, in particular, has been at the forefront of integrating knowledge graph solutions within manufacturing, energy, and automotive sectors, supported by strong R&D investments and favorable government policies promoting digital innovation. The region’s robust IT infrastructure, skilled workforce, and active participation in global industrial alliances further bolster its leadership position in the market, making it a hotspot for early adoption and commercialization of advanced knowledge graph platforms.

The Asia Pacific region is expected to witness the fastest growth in the industrial knowledge graph platform market over the forecast period, with a projected CAGR exceeding 23% between 2025 and 2033. This accelerated growth is driven by rapid industrialization, rising investments in smart manufacturing, and the proliferation of IoT devices across China, Japan, South Korea, and India. Governments in these countries are actively supporting digital transformation initiatives through favorable policies, incentives, and funding for Industry 4.0 projects. The increasing presence of multinational corporations, expansion of local technology providers, and the growing emphasis on process optimization and predictive maintenance are fueling demand for knowledge graph solutions, making Asia Pacific a key engine for future market expansion.

Emerging economies in Latin America, the Middle East, and Africa are gradually embracing industrial knowledge graph platforms, albeit at a slower pace due to challenges such as limited digital infrastructure, skill shortages, and regulatory uncertainties. However, localized demand for asset management, supply chain optimization, and risk management solutions is rising as enterprises seek to improve operational resilience and comply with evolving industry standards. Strategic collaborations with international technology vendors, investments in workforce upskilling, and government-led digitalization programs are expected to bridge adoption gaps in these regions over time. Despite the hurdles, the long-term outlook remains positive, with gradual market penetration anticipated as these economies continue to modernize their industrial sectors.

Report Scope

snapshot-tab-pane Industrial Knowledge Graph Market Size 2026-2030The industrial knowledge graph market size is valued to increase by USD 2.92 billion, at a CAGR of 24.1% from 2025 to 2030. Increasing data complexity and requirement for semantic interoperability will drive the industrial knowledge graph market.Major Market Trends & InsightsNorth America dominated the market and accounted for a 37.8% growth during the forecast period.By Component - Software segment was valued at USD 757.9 million in 2024By End-user - Manufacturing segment accounted for the largest market revenue share in 2024Market Size & ForecastMarket Opportunities: USD 3.85 billionMarket Future Opportunities: USD 2.92 billionCAGR from 2025 to 2030 : 24.1%Market SummaryThe industrial knowledge graph market is undergoing a significant transformation as industries increasingly adopt digital-first strategies. This technology provides a framework for integrating heterogeneous data from disparate sources, creating a unified, contextualized view of operations.By moving beyond traditional databases, organizations leverage a cohesive intelligence layer to enable advanced analytics, such as predictive maintenance and root cause analysis, which are crucial for minimizing downtime.For instance, a manufacturing firm can map relationships between equipment sensor data, maintenance logs, and supply chain information to proactively identify potential failures before they occur, optimizing production schedules and reducing operational costs. The integration of neuro-symbolic AI and graph retrieval-augmented generation is further enhancing capabilities, allowing for autonomous decision-making and more intuitive interaction with complex data systems.This shift toward data-centric architectures is fundamental for companies aiming to build a resilient and intelligent cognitive enterprise, navigating challenges like data governance and real-time scalability while unlocking new efficiencies.What will be the Size of the Industrial Knowledge Graph Market during the forecast period? Get Key Insights on Market Forecast (PDF) Get Free SampleHow is the Industrial Knowledge Graph Market Segmented?The industrial knowledge graph industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in "USD million" for the period 2026-2030, as well as historical data from 2020-2024 for the following segments.ComponentSoftwareServicesEnd-userManufacturingEnergy and utilitiesOil and gasAutomotiveOthersDeploymentCloudOn-premisesGeographyNorth AmericaUSCanadaMexicoEuropeGermanyUKFranceAPACChinaJapanIndiaSouth AmericaBrazilArgentinaColombiaMiddle East and AfricaSaudi ArabiaUAESouth AfricaRest of World (ROW)By Component InsightsThe software segment is estimated to witness significant growth during the forecast period.The software segment forms the technological core, providing the essential infrastructure for data ingestion and semantic modeling.These industrial data integration platforms utilize a flexible schema that allows for the seamless integration of structured telemetry from industrial IoT platforms and unstructured data from technical manuals.Unlike traditional databases, this software employs a semantic network architecture and linked data protocols to define intricate relationships between assets and processes, creating a high-fidelity digital representation of the enterprise.By enabling superior data interoperability and contextual understanding, these solutions improve root cause analysis accuracy by over 30%.The software is critical for advanced applications such as predictive maintenance and enhancing digital twin accuracy, serving as the foundation for enterprise AI platforms and graph analytics engines. Get Free SampleThe Software segment was valued at USD 757.9 million in 2024 and showed a gradual increase during the forecast period. Get Free SampleRegional AnalysisNorth America is estimated to contribute 37.8% to the growth of the global market during the forecast period.Technavio’s analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period. See How Industrial Knowledge Graph Market Demand is Rising in North America Get Free SampleNorth America leads the market, contributing over 37% of the projected growth, driven by a mature ecosystem focusing on the convergence of information technology integration and operational technology integration.Organizations in the region are heavily investing in industrial data modeling and graph database management to build the cognitive enterprise. The region's focus on industrial automation systems and process safety management drives adoption.Meanwhile, APAC is the fastest-growing region, with a CAGR over 25%, fueled by gov

'Statistics on high-tech industry and knowledge-intensive services' (sometimes referred to as simply 'high-tech statistics') comprise economic, employment and science, technology and innovation (STI) data describing manufacturing and services industries or products traded broken down by technological intensity. The domain uses various other domains and sources of Eurostat's official statistics (CIS, COMEXT, HRST, LFS, PATENT, R&D and SBS) and its coverage is therefore dependent on these other primary sources. Two main approaches are used in the domain to identify technology-intensity: the sectoral approach and the product approach. A third approach is used for data on high-tech and biotechnology patents aggregated on the basis of the International Patent Classification (IPC) 8th edition (see summary table in Annex 1 for which approach is used by each type of data). The sectoral approach: The sectoral approach is an aggregation of the manufacturing industries according to technological intensity (R&D expenditure/value added) and based on the Statistical classification of economic activities in the European Community (NACE) at 2-digit level. The level of R&D intensity served as a criterion of classification of economic sectors into high-technology, medium high-technology, medium low-technology and low-technology industries. Services are mainly aggregated into knowledge-intensive services (KIS) and less knowledge-intensive services (LKIS) based on the share of tertiary educated persons at NACE 2-digit level. The sectoral approach is used for all indicators except data on high-tech trade and patents. Note that due to the revision of the NACE from NACE Rev. 1.1 to NACE Rev. 2 the definition of high-technology industries and knowledge-intensive services has changed in 2008. For high-tech statistics it means that two different definitions (one according NACE Rev. 1.1 and one according NACE Rev. 2) are used in parallel and the data according to both NACE versions are presented in separated tables depending on the data availability. For example as the LFS provides the results both by NACE Rev. 1.1 and NACE Rev. 2, all the table using this source have been duplicated to present the results by NACE Rev. 2 from 2008. For more details, see both definitions of high-tech sectors in Annex 2 and 3. Within the sectoral approach, a second classification was created, named Knowledge Intensive Activities KIA) and based on the share of tertiary educated people in each sectors of industries and services according to NACE at 2-digit level and for all EU Member States. A threshold was applied to judge sectors as knowledge intensive. In contrast to first sectoral approach mixing two methodologies, one for manufacturing industries and one for services, the KIA classification is based on one methodology for all the sectors of industries and services covering even public sector activities. The aggregations in use are Total Knowledge Intensive Activities (KIA) and Knowledge Intensive Activities in Business Industries (KIABI). Both classifications are made according to NACE Rev. 1.1 and NACE Rev. 2 at 2- digit level. Note that due to revision of the NACE Rev.1.1 to NACE Rev. 2 the list of Knowledge Intensive Activities has changed as well, the two definitions are used in parallel and the data are shown in two separate tables. NACE Rev.2 collection includes data starting from 2008 reference year. For more details please see the definitions in Annex 7 and 8. The product approach: The product approach was created to complement the sectoral approach and it is used for data on high-tech trade. The product list is based on the calculations of R&D intensity by groups of products (R&D expenditure/total sales). The groups classified as high-technology products are aggregated on the basis of the Standard International Trade Classification (SITC). The initial definition was built based on SITC Rev.3 and served to compile the high-tech product aggregates until 2007. With the implementation in 2007 of the new version of SITC Rev.4, the definition of high-tech groups was revised and adapted according to new classification. Starting from 2007 the Eurostat presents the trade data for high-tech groups aggregated based on the SITC Rev.4. For more details, see definition of high-tech products in Annex 4 and 5. High-tech patents: High-tech patents are defined according to another approach. The groups classified as high-tech patents are aggregated on the basis of the International Patent Classification (IPC 8th edition). Biotechnology patents are also aggregated on the basis of the IPC 8th edition. For more details, see the aggregation list of high-tech and biotechnology patents in Annex 6. The high-tech domain also comprises the sub-domain Venture Capital Investments: data are provided by INVEST Europe (formerly named the European Private Equity and Venture Capital Association EVCA). More details are available in the Eurostat metadata under Venture capit...

Industrial Knowledge Capture Copilots Market Outlook

According to our latest research, the Global Industrial Knowledge Capture Copilots market size was valued at $1.7 billion in 2024 and is projected to reach $9.3 billion by 2033, expanding at a CAGR of 20.8% during the forecast period of 2025 to 2033. The primary factor driving the remarkable growth of this market globally is the increasing need for digitized knowledge management systems in industrial environments, which are crucial for preserving tacit expertise, improving operational efficiency, and accelerating workforce training. As industries face a rapidly aging workforce and the influx of new technologies, the demand for intelligent copilots that can capture, organize, and disseminate institutional knowledge has never been higher. This trend is further amplified by the adoption of Industry 4.0 principles, which emphasize automation, data exchange, and smart systems integration across manufacturing and industrial sectors.

Regional Outlook

North America currently commands the largest share of the Industrial Knowledge Capture Copilots market, accounting for approximately 38% of global revenue in 2024. This regional dominance is attributed to the presence of mature industrial sectors, advanced technological infrastructure, and a strong culture of early adoption of digital transformation solutions. The United States, in particular, has witnessed significant investments in AI-driven knowledge management platforms, supported by robust R&D activities and a high concentration of market-leading technology providers. Favorable government policies, such as incentives for digital upskilling and grants for innovation in manufacturing, further bolster market growth. Moreover, leading enterprises across automotive, aerospace, and energy sectors are integrating copilots to address workforce attrition and regulatory compliance, creating a fertile ground for market expansion.

Asia Pacific is emerging as the fastest-growing region, projected to register a remarkable CAGR of 24.3% from 2025 to 2033. This growth is primarily driven by rapid industrialization, increasing adoption of automation, and a surge in digital transformation initiatives across China, Japan, South Korea, and India. Governments in this region are actively promoting smart manufacturing through policy reforms and financial incentives, resulting in heightened investments in AI-powered knowledge management solutions. The proliferation of cloud infrastructure and the entry of global technology vendors are also accelerating the deployment of copilot platforms. Additionally, the region’s large manufacturing base and the need to bridge skill gaps among a diverse workforce are compelling organizations to implement advanced knowledge capture tools for operational excellence and competitive advantage.

In contrast, emerging economies in Latin America and the Middle East & Africa are experiencing moderate adoption rates due to unique challenges such as limited digital infrastructure, budgetary constraints, and varying levels of technological maturity. While there is growing awareness of the benefits of knowledge capture copilots, enterprises in these regions often face hurdles related to workforce digital literacy, data security concerns, and compliance with local regulations. Nevertheless, localized demand is gradually increasing, especially in sectors like energy, mining, and pharmaceuticals, where knowledge retention and regulatory documentation are critical. Policy initiatives aimed at fostering digital skills and international collaborations are expected to gradually unlock new opportunities, although market penetration will remain uneven until foundational barriers are systematically addressed.

Report Scope

'Statistics on high-tech industry and knowledge-intensive services' (sometimes referred to as simply 'high-tech statistics') comprise economic, employment and science, technology and innovation (STI) data describing manufacturing and services industries or products traded broken down by technological intensity. The domain uses various other domains and sources of Eurostat's official statistics (CIS, COMEXT, HRST, LFS, PATENT, R&D and SBS) and its coverage is therefore dependent on these other primary sources. Two main approaches are used in the domain to identify technology-intensity: the sectoral approach and the product approach. A third approach is used for data on high-tech and biotechnology patents aggregated on the basis of the International Patent Classification (IPC) 8th edition (see summary table in Annex 1 for which approach is used by each type of data). The sectoral approach: The sectoral approach is an aggregation of the manufacturing industries according to technological intensity (R&D expenditure/value added) and based on the Statistical classification of economic activities in the European Community (NACE) at 2-digit level. The level of R&D intensity served as a criterion of classification of economic sectors into high-technology, medium high-technology, medium low-technology and low-technology industries. Services are mainly aggregated into knowledge-intensive services (KIS) and less knowledge-intensive services (LKIS) based on the share of tertiary educated persons at NACE 2-digit level. The sectoral approach is used for all indicators except data on high-tech trade and patents. Note that due to the revision of the NACE from NACE Rev. 1.1 to NACE Rev. 2 the definition of high-technology industries and knowledge-intensive services has changed in 2008. For high-tech statistics it means that two different definitions (one according NACE Rev. 1.1 and one according NACE Rev. 2) are used in parallel and the data according to both NACE versions are presented in separated tables depending on the data availability. For example as the LFS provides the results both by NACE Rev. 1.1 and NACE Rev. 2, all the table using this source have been duplicated to present the results by NACE Rev. 2 from 2008. For more details, see both definitions of high-tech sectors in Annex 2 and 3. Within the sectoral approach, a second classification was created, named Knowledge Intensive Activities KIA) and based on the share of tertiary educated people in each sectors of industries and services according to NACE at 2-digit level and for all EU Member States. A threshold was applied to judge sectors as knowledge intensive. In contrast to first sectoral approach mixing two methodologies, one for manufacturing industries and one for services, the KIA classification is based on one methodology for all the sectors of industries and services covering even public sector activities. The aggregations in use are Total Knowledge Intensive Activities (KIA) and Knowledge Intensive Activities in Business Industries (KIABI). Both classifications are made according to NACE Rev. 1.1 and NACE Rev. 2 at 2- digit level. Note that due to revision of the NACE Rev.1.1 to NACE Rev. 2 the list of Knowledge Intensive Activities has changed as well, the two definitions are used in parallel and the data are shown in two separate tables. NACE Rev.2 collection includes data starting from 2008 reference year. For more details please see the definitions in Annex 7 and 8. The product approach: The product approach was created to complement the sectoral approach and it is used for data on high-tech trade. The product list is based on the calculations of R&D intensity by groups of products (R&D expenditure/total sales). The groups classified as high-technology products are aggregated on the basis of the Standard International Trade Classification (SITC). The initial definition was built based on SITC Rev.3 and served to compile the high-tech product aggregates until 2007. With the implementation in 2007 of the new version of SITC Rev.4, the definition of high-tech groups was revised and adapted according to new classification. Starting from 2007 the Eurostat presents the trade data for high-tech groups aggregated based on the SITC Rev.4. For more details, see definition of high-tech products in Annex 4 and 5. High-tech patents: High-tech patents are defined according to another approach. The groups classified as high-tech patents are aggregated on the basis of the International Patent Classification (IPC 8th edition). Biotechnology patents are also aggregated on the basis of the IPC 8th edition. For more details, see the aggregation list of high-tech and biotechnology patents in Annex 6. The high-tech domain also comprises the sub-domain Venture Capital Investments: data are provided by INVEST Europe (formerly named the European Private Equity and Venture Capital Association EVCA). More details are available in the Eurostat metadata under Venture capit...

Manufacturing Knowledge Capture Market Outlook

According to our latest research, the Global Manufacturing Knowledge Capture market size was valued at $2.8 billion in 2024 and is projected to reach $7.6 billion by 2033, expanding at a CAGR of 11.5% during 2024–2033. The rapid digitization of manufacturing operations and the increasing need for effective knowledge management to preserve critical expertise amidst workforce transitions are major drivers propelling the market forward. As manufacturing enterprises worldwide grapple with the dual challenges of skills shortages and the need to accelerate innovation, capturing, organizing, and disseminating institutional knowledge has become a strategic imperative. This trend is further amplified by the growing adoption of Industry 4.0 technologies, which require seamless knowledge transfer and standardized best practices to optimize production, ensure compliance, and maintain quality standards across complex global supply chains.

Regional Outlook

North America currently holds the largest share of the global Manufacturing Knowledge Capture market, accounting for approximately 38% of total revenue in 2024. This dominance is attributed to the region’s mature manufacturing sector, robust digital infrastructure, and a strong culture of innovation. In the United States and Canada, early adoption of advanced manufacturing technologies, such as AI-driven knowledge management platforms and digital twins, has enabled organizations to capture and leverage operational expertise efficiently. Additionally, stringent regulatory requirements and a high rate of workforce retirement have intensified the focus on institutionalizing knowledge capture processes. The presence of leading technology vendors and a proactive approach to digital transformation further underpin North America’s leadership in this market.

The Asia Pacific region is anticipated to experience the fastest growth, with a projected CAGR of 14.2% from 2024 to 2033. This surge is driven by rapid industrialization, significant investments in smart manufacturing, and the expansion of multinational manufacturing facilities in key markets such as China, Japan, South Korea, and India. Governments across the region are implementing favorable policies and incentives to accelerate the adoption of digital solutions, including knowledge capture systems, to enhance productivity and global competitiveness. The increasing prevalence of automation and the need to upskill a large, diverse workforce are prompting manufacturers to invest in sophisticated knowledge management tools to ensure consistent operational excellence and facilitate effective training and onboarding.

Emerging economies in Latin America and the Middle East & Africa are witnessing a gradual uptake of manufacturing knowledge capture solutions, albeit at a slower pace due to infrastructural and budgetary constraints. However, localized demand is rising as manufacturers in these regions seek to bridge skills gaps, comply with evolving regulatory standards, and address quality consistency challenges. In these markets, knowledge capture initiatives are often driven by multinational companies seeking to standardize processes across global operations. Policy reforms aimed at boosting industrial growth and digital adoption are expected to gradually improve market penetration, although challenges related to technology adoption, workforce readiness, and localized content remain significant barriers.

Report Scope

The Industrial Knowledge Management Solutions market has emerged as a critical component in enhancing operational efficiency and driving innovation across various sectors. In today's fast-paced industrial landscape, organizations strive to harness their collective expertise and streamline information shari...

Manufacturing Knowledge Capture Market Outlook

According to our latest research, the global Manufacturing Knowledge Capture market size in 2024 stands at USD 2.18 billion, reflecting robust momentum across manufacturing sectors as organizations increasingly prioritize knowledge retention and digital transformation. The market is advancing at a compelling CAGR of 14.7% from 2025 to 2033, propelled by the urgent need to preserve critical operational knowledge, reduce skill gaps, and enhance productivity in the face of an aging workforce and rapid technological change. By 2033, the Manufacturing Knowledge Capture market is forecasted to reach USD 6.47 billion, underscoring its growing strategic importance within global manufacturing ecosystems.

One of the primary growth factors driving the Manufacturing Knowledge Capture market is the intensifying focus on operational efficiency and innovation within manufacturing enterprises. As companies face increasing pressure to optimize processes, reduce downtime, and accelerate new product development, capturing and leveraging institutional knowledge becomes a vital enabler. The integration of advanced technologies such as artificial intelligence, machine learning, and IoT into manufacturing workflows has created new avenues for collecting, structuring, and disseminating tacit and explicit knowledge. This digitalization trend not only supports real-time decision-making but also ensures that best practices and critical know-how are preserved, even as experienced personnel retire or transition.

Another significant driver is the growing complexity of manufacturing processes, particularly in sectors like automotive, aerospace, and electronics. As products become more sophisticated and regulatory requirements tighten, the need for comprehensive knowledge management systems escalates. Manufacturing Knowledge Capture solutions facilitate compliance by systematically documenting procedures, quality standards, and change histories. Additionally, these platforms enable seamless training and onboarding of new employees, reducing the time and cost associated with workforce development. The shift toward Industry 4.0 and smart manufacturing further amplifies the demand for robust knowledge capture frameworks that can adapt to evolving operational paradigms.

The Manufacturing Knowledge Capture market is also benefiting from the globalization of supply chains and the rise of distributed manufacturing environments. Organizations with geographically dispersed operations are increasingly reliant on centralized knowledge repositories to ensure consistency, collaboration, and agility across locations. The ability to access critical insights, troubleshooting guides, and process documentation in real time enhances cross-functional teamwork and accelerates problem resolution. This trend is particularly pronounced in multinational corporations seeking to harmonize standards and leverage best practices on a global scale, thereby driving sustained growth in the knowledge capture market.

Regionally, Asia Pacific is emerging as a powerhouse in the Manufacturing Knowledge Capture market, fueled by rapid industrialization, expanding manufacturing bases, and significant investments in digital infrastructure. North America and Europe remain strongholds due to their mature manufacturing sectors and early adoption of knowledge management technologies. Meanwhile, Latin America and the Middle East & Africa are experiencing steady uptake as local industries modernize and integrate with global value chains. This regional dynamism is shaping a competitive landscape where innovation, localization, and scalability are key differentiators for solution providers.

Component Analysis

The Manufacturing Knowledge Capture market by component is segmented into Software, Hardware, and Services. The software segment commands a significant share, driven by the proliferation of digital platforms that facilitate knowledge documentation, retrieval, and sharing. Modern knowledge capture software incorporates AI-powered search, natural language processing, and advanced analytics, enabling manufacturers to transform unstructured data into actionable insights. These solutions are increasingly cloud-based, offering scalability, accessibility, and integration with other enterprise systems such as ERP and MES. The surge in demand for intuitive, user-friendly interfaces is further propellin

'Statistics on high-tech industry and knowledge-intensive services' (sometimes referred to as simply 'high-tech statistics') comprise economic, employment and science, technology and innovation (STI) data describing manufacturing and services industries or products traded broken down by technological intensity. The domain uses various other domains and sources of Eurostat's official statistics (CIS, COMEXT, HRST, LFS, PATENT, R&D and SBS) and its coverage is therefore dependent on these other primary sources. Two main approaches are used in the domain to identify technology-intensity: the sectoral approach and the product approach. A third approach is used for data on high-tech and biotechnology patents aggregated on the basis of the International Patent Classification (IPC) 8th edition (see summary table in Annex 1 for which approach is used by each type of data). The sectoral approach: The sectoral approach is an aggregation of the manufacturing industries according to technological intensity (R&D expenditure/value added) and based on the Statistical classification of economic activities in the European Community (NACE) at 2-digit level. The level of R&D intensity served as a criterion of classification of economic sectors into high-technology, medium high-technology, medium low-technology and low-technology industries. Services are mainly aggregated into knowledge-intensive services (KIS) and less knowledge-intensive services (LKIS) based on the share of tertiary educated persons at NACE 2-digit level. The sectoral approach is used for all indicators except data on high-tech trade and patents. Note that due to the revision of the NACE from NACE Rev. 1.1 to NACE Rev. 2 the definition of high-technology industries and knowledge-intensive services has changed in 2008. For high-tech statistics it means that two different definitions (one according NACE Rev. 1.1 and one according NACE Rev. 2) are used in parallel and the data according to both NACE versions are presented in separated tables depending on the data availability. For example as the LFS provides the results both by NACE Rev. 1.1 and NACE Rev. 2, all the table using this source have been duplicated to present the results by NACE Rev. 2 from 2008. For more details, see both definitions of high-tech sectors in Annex 2 and 3. Within the sectoral approach, a second classification was created, named Knowledge Intensive Activities KIA) and based on the share of tertiary educated people in each sectors of industries and services according to NACE at 2-digit level and for all EU Member States. A threshold was applied to judge sectors as knowledge intensive. In contrast to first sectoral approach mixing two methodologies, one for manufacturing industries and one for services, the KIA classification is based on one methodology for all the sectors of industries and services covering even public sector activities. The aggregations in use are Total Knowledge Intensive Activities (KIA) and Knowledge Intensive Activities in Business Industries (KIABI). Both classifications are made according to NACE Rev. 1.1 and NACE Rev. 2 at 2- digit level. Note that due to revision of the NACE Rev.1.1 to NACE Rev. 2 the list of Knowledge Intensive Activities has changed as well, the two definitions are used in parallel and the data are shown in two separate tables. NACE Rev.2 collection includes data starting from 2008 reference year. For more details please see the definitions in Annex 7 and 8. The product approach: The product approach was created to complement the sectoral approach and it is used for data on high-tech trade. The product list is based on the calculations of R&D intensity by groups of products (R&D expenditure/total sales). The groups classified as high-technology products are aggregated on the basis of the Standard International Trade Classification (SITC). The initial definition was built based on SITC Rev.3 and served to compile the high-tech product aggregates until 2007. With the implementation in 2007 of the new version of SITC Rev.4, the definition of high-tech groups was revised and adapted according to new classification. Starting from 2007 the Eurostat presents the trade data for high-tech groups aggregated based on the SITC Rev.4. For more details, see definition of high-tech products in Annex 4 and 5. High-tech patents: High-tech patents are defined according to another approach. The groups classified as high-tech patents are aggregated on the basis of the International Patent Classification (IPC 8th edition). Biotechnology patents are also aggregated on the basis of the IPC 8th edition. For more details, see the aggregation list of high-tech and biotechnology patents in Annex 6. The high-tech domain also comprises the sub-domain Venture Capital Investments: data are provided by INVEST Europe (formerly named the European Private Equity and Venture Capital Association EVCA). More details are available in the Eurostat metadata under Venture capit...

Industry Domain Ontologies

  Overview

The industry domain encompasses ontologies that systematically represent and model the complex structures, processes, and interactions within industrial settings, including manufacturing systems, smart buildings, and equipment. This domain is pivotal in advancing knowledge representation by enabling the integration, interoperability, and automation of industrial processes, thereby facilitating improved efficiency, innovation, and… See the full description on the dataset page: https://huggingface.co/datasets/SciKnowOrg/ontolearner-industry.

This is the registration data for the first factory (manufacturer) registered in Jinju City. Download and upload this data from the Factory Registration System (FactoryOn).
Please note that this is the first factory (manufacturer) registration information regarding the manufacturer's name, street address, road name address, telephone number, fax number, number of employees, industry name, industry classification, products, land area, manufacturing facility area, auxiliary facility area, and building area.
For any further inquiries regarding the manufacturer, please contact the Jinju City Investment Promotion Division (055-749-8155) and we will be happy to assist you.

Innovation is key to gaining a sustainable edge in an increasingly competitive global manufacturing landscape. For Bangladesh’s manufacturing sector to survive and thrive in today’s cutthroat business environment, adopting transformative technologies such as the Internet of Things (IoT) is not a luxury but a necessity. This article tackles the formidable task of identifying and comprehensively evaluating the impediments to IoT adoption in the Bangladeshi manufacturing industry. We delve deeply into the complex terrain of IoT adoption challenges by synthesizing expert insights and a meticulously selected body of contemporary literature. We employ a robust methodology combining the Delphi method with the fuzzy Analytical Hierarchy Process to systematically analyze and prioritize these challenges. Using this methodology, we leveraged the combined expertise of domain specialists and subsequently employed fuzzy logic techniques to address the inherent ambiguities and uncertainties within the data. Our findings highlight this clear path. They reveal that among the myriad barriers, “Lack of top management commitment to implementing new technology” (B10), “High initial implementation investment costs” (B9), and “Risks associated with switching to a new business model” (B7) loom most extensive, demanding immediate attention. These insights are not confined to academia but serve as a pragmatic guide for industrial managers. Armed with the knowledge gleaned from this study, managers can craft tailored strategies, set well-informed priorities, and embark on a transformational journey toward harnessing the vast potential of IoT in the Bangladeshi industrial sector. This article provides a comprehensive understanding of IoT adoption challenges and industry leaders with the tools necessary to navigate these challenges effectively. This strategic navigation, in turn, contributes significantly to enhancing the competitiveness and sustainability of Bangladeshi manufacturing in the IoT era.

Big In Manufacturing Market Overview:

The Big in Manufacturing Market Size was valued at 1,400.5 USD Billion in 2024. The Big in Manufacturing Market is expected to grow from 1,432.7 USD Billion in 2025 to 1,800 USD Billion by 2035. The Big in Manufacturing Market CAGR (growth rate) is expected to be around 2.3% during the forecast period (2025 - 2035).Key Big In Manufacturing Market Trends Highlighted

The Global Big in Manufacturing Market is witnessing significant trends shaped by technological advancements and evolving consumer demands. Automation and smart manufacturing technologies, including IoT and AI, are driving efficiency and productivity. Companies are increasingly adopting Industry 4.0 solutions to enhance operational effectiveness, reduce costs, and improve product quality. The rising importance of sustainability is another key driver influencing this market as manufacturers are focusing on reducing waste and energy consumption to align with global environmental goals. Opportunities to be explored include the integration of additive manufacturing and 3D printing, which allows for rapid prototyping and customization, ultimately leading to a new era of manufacturing.The focus on utilizing local resources and advanced materials, along with an emphasis on supply chain resilience, also presents growth avenues. In recent times, there is a trend toward collaborative manufacturing models that allow companies to share resources and knowledge to minimize risks while maximizing innovation. The shift towards digitalization, driven by the need for remote operations and real-time data processing during recent global disruptions, has reshaped strategies across the sector. In addition to technological advancements, government initiatives globally are supporting the manufacturing sector by investing in research and development and promoting policies that foster innovation.As countries look to rebuild economies while focusing on resilient infrastructure, the global nature of this market creates a network of interconnected operations that enhance competitiveness. The collective need for enhanced manufacturing capabilities, alongside regulatory support, further underlines the growth potential in the Global Big in Manufacturing Market.

Source: Primary Research, Secondary Research, WGR Database and Analyst Review Big In Manufacturing Market Segment Insights: Big In Manufacturing Market Regional Insights

The Regional segmentation of the Global Big in Manufacturing Market reveals a complex landscape with North America leading in valuation, dominating the sector with USD 600 Billion in 2024 and expected to reach USD 740 Billion by 2035. This region benefits from advanced technologies, innovation, and substantial investment in Research and Development, which drives market growth. Europe exhibits steady expansion, supported by industrial advancements and increased automation in manufacturing processes. The APAC region is experiencing moderate increase due to a growing emphasis on manufacturing capabilities and exports, driven by rising demand in domestic markets.South America is also witnessing gradual growth, emphasizing sustainability and modern manufacturing practices amid varying economic conditions. Meanwhile, the Middle East and Africa (MEA) present emerging opportunities with a focus on infrastructural development and diversification of industrial sectors, contributing to a positive outlook for the manufacturing market in these regions. Overall, the Global Big in Manufacturing Market's segmentation reflects varying growth dynamics that are influenced by technological, economic, and regulatory factors across different regions.

Source: Primary Research, Secondary Research, WGR Database and Analyst Review

North America: North America is witnessing significant growth in smart manufacturing, driven by increased adoption of AIoT solutions across sectors like automotive and healthcare. Investment in urban surveillance technologies is also rising. Policy initiatives like the Manufacturing Extension Partnership promote innovation and efficiency in local manufacturing. Europe: Europe is focusing on sustainable manufacturing with policies such as the European Green Deal advocating for eco-friendly industrial practices. The manufacturing sector is rapidly adopting AIoT for improved automation. The automotive sector is especially active, bolstered by high EV adoption rates across member states. Asia: Asia, particularly countries like China and India, is experiencing rapid growth in smart manufacturing technologies fueled by government policies such as Made in China 2025. The automotive industry is pivoting towards EVs, and increased urbanization is enhancing the investment in urban surveillance solutions.Big In Manufacturing Market By Product Type Insights

The Product Type segment within the Global Big in Manufacturing Market exhibits a diverse landscape, where 'Machinery' stands out with a significant valuation of 430 USD Billion in 2024, projected to reach 530 USD Billion in 2035. This segment dominates the market, highlighting its critical role in enhancing production efficiency and driving advancements in technology. Machinery is fundamental for various manufacturing processes, contributing to improved operational capabilities and streamlined workflows. In parallel, 'Automated Systems' is experiencing strong growth as manufacturers increasingly adopt automation to optimize processes and reduce labor costs, reflecting a shift towards more efficient production methodologies. 'Raw Materials' and 'Components' are also witnessing steady expansion as industries demand quality inputs to meet both regulatory standards and consumer preferences. The upward trend in 'Raw Materials' is driven by the global push for sustainable practices, while 'Components', essential for the assembly and functionality of complex machinery, are crucial in fulfilling the industry's technological advancements. Moreover, the role of 'Software Solutions' cannot be understated, with its ongoing development evident in the integration of cutting-edge technologies to enhance data management and operational efficiency. The rise of smart manufacturing is necessitating a gradual increase in demand for innovative software that can integrate with existing systems and lead to more insightful analytics. This segment is vital as it aligns with the broader industry trend of utilizing data-driven strategies to improve productivity, streamline operations, and facilitate better decision-making across the manufacturing ecosystem. Collectively, these Product Type segments showcase the dynamic and evolving nature of the Global Big in Manufacturing Market, where innovation and productivity remain at the forefront of industry objectives.

Source: Primary Research, Secondary Research, WGR Database and Analyst ReviewBig In Manufacturing Market Manufacturing Process Insights

The Manufacturing Process segment within the Global Big in Manufacturing Market demonstrates robust dynamics, with distinct areas contributing to its growth. Among these, Additive Manufacturing has been a major player, cultivating strong growth through advances in 3D printing technology. This technique is increasingly utilized for rapid prototyping and customized production, reflecting its significance in modern manufacturing. Subtractive Manufacturing, which has traditionally dominated the sector, continues to experience steady expansion as manufacturers optimize precision and efficiency for high-volume production.Formative Manufacturing is also essential in industries such as automotive and aerospace, where shaping materials into desired forms is a critical process, ensuring its sustained relevance. Hybrid Manufacturing, incorporating both additive and subtractive techniques, is gaining traction as it offers flexibility and efficiency, addressing diverse production needs. Overall, the Global Big in Manufacturing Market's segmentation showcases a blend of established processes and innovative practices, contributing to a landscape ripe with opportunities and advancements. Big In Manufacturing Market End Use Industry Insights

The End Use Industry segment within the Global Big in Manufacturing Market demonstrates diverse growth patterns across various sectors. The Automotive sector, which is the most significant within this segment, has already shown strong performance historically and is expected to maintain its prominent position moving forward. Aerospace indicates steady expansion driven by increasing demand for commercial and military aircraft. The Electronics sector is undergoing moderate increase, propelled by advancements in technology and the rise of new electronic devices.In the realm of Consumer Goods, the trend showcases gradual growth as preferences shift toward sustainable and innovative products. Healthcare is experiencing robust growth due to rising investments in medical equipment and technology, especially post-pandemic. Factors such as technological innovation and sustainable practices are fostering an advantageous environment for these industries, ensuring their influential role in shaping the dynamics of the Global Big in Manufacturing Market.

Big In Manufacturing Market By Scale of Operation InsightsThe Scale of Operation segment within the Global Big in Manufacturing Market presents a diverse landscape characterized by Small Scale, Medium Scale, and Large Scale operations. Large Scale operations hold a significant and commanding position in the market, often representing a substantial share due to their capacity for mass production, advanced technologies, and economies of scale that drive efficiency and cost-effectiveness in manufacturing. Meanwhile, Small Scale operations are experiencing steady expansion, catering effectively to niche markets and local demands, allowing businesses to remain flexible and

'Statistics on high-tech industry and knowledge-intensive services' (sometimes referred to as simply 'high-tech statistics') comprise economic, employment and science, technology and innovation (STI) data describing manufacturing and services industries or products traded broken down by technological intensity. The domain uses various other domains and sources of Eurostat's official statistics (CIS, COMEXT, HRST, LFS, PATENT, R&D and SBS) and its coverage is therefore dependent on these other primary sources. Two main approaches are used in the domain to identify technology-intensity: the sectoral approach and the product approach. A third approach is used for data on high-tech and biotechnology patents aggregated on the basis of the International Patent Classification (IPC) 8th edition (see summary table in Annex 1 for which approach is used by each type of data). The sectoral approach: The sectoral approach is an aggregation of the manufacturing industries according to technological intensity (R&D expenditure/value added) and based on the Statistical classification of economic activities in the European Community (NACE) at 2-digit level. The level of R&D intensity served as a criterion of classification of economic sectors into high-technology, medium high-technology, medium low-technology and low-technology industries. Services are mainly aggregated into knowledge-intensive services (KIS) and less knowledge-intensive services (LKIS) based on the share of tertiary educated persons at NACE 2-digit level. The sectoral approach is used for all indicators except data on high-tech trade and patents. Note that due to the revision of the NACE from NACE Rev. 1.1 to NACE Rev. 2 the definition of high-technology industries and knowledge-intensive services has changed in 2008. For high-tech statistics it means that two different definitions (one according NACE Rev. 1.1 and one according NACE Rev. 2) are used in parallel and the data according to both NACE versions are presented in separated tables depending on the data availability. For example as the LFS provides the results both by NACE Rev. 1.1 and NACE Rev. 2, all the table using this source have been duplicated to present the results by NACE Rev. 2 from 2008. For more details, see both definitions of high-tech sectors in Annex 2 and 3. Within the sectoral approach, a second classification was created, named Knowledge Intensive Activities KIA) and based on the share of tertiary educated people in each sectors of industries and services according to NACE at 2-digit level and for all EU Member States. A threshold was applied to judge sectors as knowledge intensive. In contrast to first sectoral approach mixing two methodologies, one for manufacturing industries and one for services, the KIA classification is based on one methodology for all the sectors of industries and services covering even public sector activities. The aggregations in use are Total Knowledge Intensive Activities (KIA) and Knowledge Intensive Activities in Business Industries (KIABI). Both classifications are made according to NACE Rev. 1.1 and NACE Rev. 2 at 2- digit level. Note that due to revision of the NACE Rev.1.1 to NACE Rev. 2 the list of Knowledge Intensive Activities has changed as well, the two definitions are used in parallel and the data are shown in two separate tables. NACE Rev.2 collection includes data starting from 2008 reference year. For more details please see the definitions in Annex 7 and 8. The product approach: The product approach was created to complement the sectoral approach and it is used for data on high-tech trade. The product list is based on the calculations of R&D intensity by groups of products (R&D expenditure/total sales). The groups classified as high-technology products are aggregated on the basis of the Standard International Trade Classification (SITC). The initial definition was built based on SITC Rev.3 and served to compile the high-tech product aggregates until 2007. With the implementation in 2007 of the new version of SITC Rev.4, the definition of high-tech groups was revised and adapted according to new classification. Starting from 2007 the Eurostat presents the trade data for high-tech groups aggregated based on the SITC Rev.4. For more details, see definition of high-tech products in Annex 4 and 5. High-tech patents: High-tech patents are defined according to another approach. The groups classified as high-tech patents are aggregated on the basis of the International Patent Classification (IPC 8th edition). Biotechnology patents are also aggregated on the basis of the IPC 8th edition. For more details, see the aggregation list of high-tech and biotechnology patents in Annex 6. The high-tech domain also comprises the sub-domain Venture Capital Investments: data are provided by INVEST Europe (formerly named the European Private Equity and Venture Capital Association EVCA). More details are available in the Eurostat metadata under Venture capit...

The dataset contains statistics on the quality of business 4.0 products: - ISO 9001 quality certificates/bn PPP $ GDP; - High-tech manufacturing, %. Knowledge Management Statistics in Business 4.0: - Knowledge-intensive employment, %; - Research talent, % in businesses. Technology Management Statistics in Business 4.0: - Industrial designs by origin/bn PPP$ GDP; - Production and export complexity. The values of all indicators are given in points from 1 to 100 (best). The sample includes 63 countries of the world. Data are for 2022. Data source: WIPO (2023). Global Innovation Index 2022, 15th Edition. What is the future of innovation driven growth?. Retrieved from https://www.wipo.int/publications/en/details.jsp?id=4622 (data accessed: 12.03.2023).