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市場調查報告書
商品編碼
2141496

建築業巨量資料分析：市場機會、成長要素、產業趨勢分析及2026-2035年預測

Big Data Analytics in Construction Market Opportunity, Growth Drivers, Industry Trend Analysis, and Forecast 2026 - 2035
出版日期: | 出版商: Global Market Insights Inc. | 英文 290 Pages | 商品交期: 2-3個工作天內

價格
簡介目錄

全球建築業巨量資料分析市場預計到 2025 年將達到 103 億美元，年複合成長率為 10.9%，到 2035 年將達到 289 億美元。

市場範圍
開始年份 2025
預測期 2026-2035
初始市場規模 103億美元
預測金額 289億美元
複合年成長率 10.9%

隨著建築公司加速採用數位化技術以改善專案規劃、提升營運視覺和整體執行效率，市場持續成長。專案產生的資訊量不斷增加，推動了對能夠將複雜資料集轉化為可執行商業智慧的高階分析解決方案的需求。建築公司越來越依賴互聯的數位化環境來監控生產力、最佳化資源分配、改善專案進度並在整個專案生命週期中加強財務管理。貫穿整體施工營運的持續數據收集顯著提升了對能夠提供更快洞察並支援明智決策的平台的需求。對數位化專案管理、預測分析和智慧自動化的日益關注進一步推動了市場擴張。各組織正加大整合分析能力的投資，以協助提高營運效率，同時最大限度地減少延誤和不必要的支出。同時，快速的基礎設施建設和多個地區廣泛的數位轉型措施持續為建築業巨量資料數據分析市場的長期成長創造有利條件，使數據驅動的決策成為現代建築管理的重要組成部分。

預計到2025年，400G細分市場將佔據82.3%的市場佔有率，並在2026年至2035年間以11.2%的複合年成長率成長。解決方案細分市場佔據最大的銷售佔有率，這主要得益於對旨在集中和分析來自多個數位來源的建築數據的軟體平台的投資增加。建築公司持續優先考慮能夠將專案資訊整合到整合營運環境中的智慧分析解決方案，使相關人員能夠提高規劃準確性、監控專案績效、加強成本控制、最佳化進度安排、提高勞動生產力並支援資產生命週期管理。對整合軟體生態系統日益成長的需求使得基於解決方案的產品和服務成為整體市場銷售額的最大貢獻者。

預計到2025年，雲端解決方案的市佔率將達到64.6%，並在2026年至2035年間以擴充性 %的複合年成長率成長。隨著企業越來越傾向於選擇可擴展的訂閱式平台，雲端技術的普及持續推動市場發展。這些平台能夠提供集中式資料儲存、即時存取、進階分析功能以及分散式企劃團隊之間的無縫協作。持續向雲端優先經營模式轉型、軟體生態系統的擴展、網路安全安全功能的提升以及平台的不斷改進，正在推動承包商、專案業主和基礎設施開發人員更廣泛地採用雲端解決方案。更低的部署成本、更高的營運柔軟性、自動化更新以及更快的部署速度，進一步促進了對雲端分析解決方案的支援。

預計到2025年，亞太地區建築業的巨量資料分析市場規模將達9億美元。持續的基礎設施投資、不斷擴大的數位轉型（DX）舉措以及數據驅動型施工管理方法的日益普及，將繼續推動市場成長。標準化數位化施工框架的廣泛應用，正在加速結構化專案資訊的利用，使建築公司能夠在大規模開發專案中提升營運效率、專案協調能力和資料管理水準。預計公共和私人部門對數位化施工技術的持續投資將進一步鞏固中國在區域市場的領先地位。

目錄

第1章：調查方法

第2章執行摘要

第3章 行業洞察

  • 產業生態系分析
    • 供應商情況
    • 利潤率分析
    • 成本結構
    • 每個階段增加的價值
    • 影響價值鏈的因素
    • 中斷
  • 影響產業的因素
    • 促進因素
      • 物聯網、連網設備和智慧施工現場技術的日益普及，正在產生大量的建築數據。
      • 對即時專案績效監控、預測分析和成本最佳化的需求日益成長。
      • 政府主導的數位化建設、智慧基礎設施計畫以及BIM的日益強制使用，正在加速分析技術的普及應用。
      • BIM、數位孿生和人工智慧分析平台的整合正在整個建築生命週期中逐步推進。
    • 產業潛在風險與挑戰
      • 實施成本高昂，且與原有IT基礎設施。
      • 建設產業資料科學和分析人才短缺
    • 市場機遇
      • 中小型建築公司轉型為數據驅動型營運蘊藏著巨大的未開發潛力。
      • 擴展雲端原生分析平台，專門用於建設產業。
      • 預測分析與自主施工機械的融合
      • 基於企劃為基礎的實施中「分析即服務」模式的成長
  • 成長潛力分析
  • 技術與創新展望
    • 最新科技趨勢
    • 新技術
  • 價格分析
    • 2022-2025年歷史價格趨勢分析
    • 定價策略：按業務類型分類
  • 監理情勢
    • 北美洲
      • 加州消費者隱私法案 (CCPA) 和加州隱私權法案 (CPRA) - 資料隱私合規性
      • 美國國家標準與技術研究院 (NIST) 網路安全框架 2.0
      • 聯邦資訊安全現代化法案（FISMA）對政府建設項目的影響
      • 根據《基礎設施投資和就業法》(IIJA)實施數位化項目的要求
    • 歐洲
      • 一般資料保護規則（GDPR）
      • 網路與資訊安全指令 2 (NIS2)
      • 歐盟公共採購指令和BIM實施要求
      • 歐盟資料法與資料管治框架
      • 歐盟人工智慧法案旨在打擊高風險人工智慧應用
    • 亞太地區
      • 中國個人資料保護法（PIPL）
      • 中國的資料安全法（DSL）和網路安全法（CSL）
      • 印度2023年數位個人資料保護法（DPDP法案）
      • 新加坡《個人資料保護法》(PDPA) 和新加坡建設局 (BCA) 的 BIM 要求
      • 日本的《個人資訊保護法》（APPI）與國土交通省的BIM政策
    • 拉丁美洲
      • 巴西通用資料保護法（LGPD）
      • 巴西的BIM戰略與聯邦BIM法規
      • 墨西哥《私人企業個人資料保護聯邦法》（LFPDPPP）
      • 各國關於數位基礎設施和公共採購的法規
    • 中東和非洲
      • 阿拉伯聯合大公國個人資料保護法（PDPL）
      • 杜拜市政府強制要求在大型建設項目中使用BIM技術。
      • 沙烏地阿拉伯的《個人資料保護法》（PDPL）和「2030願景」數位建設舉措
      • 南非個人資料保護法（POPIA）
      • 海灣合作理事會數位政府與智慧基礎設施合規框架
  • 波特的分析
  • PESTEL 分析
  • 專利分析
  • 成本細分分析
  • 人工智慧和生成式人工智慧對市場的影響
    • 利用人工智慧改造現有經營模式
    • 按細分市場分類的生成式人工智慧用例和部署藍圖
    • 風險、限制和監管考量
  • 預測假設和情境分析
    • 基本案例：驅動複合年成長率的關鍵宏觀經濟與產業變量
    • 樂觀情境：宏觀經濟與產業的順風
    • 悲觀情景：宏觀經濟放緩或產業逆風

第4章 競爭情勢

  • 介紹
  • 企業市佔率分析
    • 北美洲
    • 歐洲
    • 亞太地區
    • 拉丁美洲
    • 中東和非洲
  • 主要市場公司的競爭分析
  • 競爭定位矩陣
  • 主要進展
    • 併購
    • 夥伴關係和聯盟
    • 新產品發布
    • 業務拓展計劃及資金籌措
  • 按公司規模進行基準測試
    • 排名分類標準與遴選標準
    • 按銷售額、地區和創新能力分類的層級定位矩陣。

第5章 市場估計與預測：依組件分類，2022-2035年

  • 解決方案
    • 資料管理和整合工具
    • 分析平台
    • 報表和儀表板解決方案
    • BIM整合分析軟體
    • ETL（擷取、轉換、載入）工具
  • 服務
    • 專業服務
    • 託管服務

第6章 市場估算與預測：依部署模式分類，2022-2035年

  • 基於雲端的
  • 現場
  • 混合

第7章 市場估計與預測：依技術分類，2022-2035年

  • 預測分析
  • 機器學習/人工智慧
  • 數據視覺化
  • 物聯網整合分析
  • 其他

第8章 市場估算與預測：依建築類型分類，2022-2035年

  • 住宅
  • 商業建築
  • 基礎設施和土木工程項目
  • 工業建築

第9章 市場估計與預測：依應用領域分類，2022-2035年

  • 專案管理
  • 設計與規劃
  • 施工管理
  • 風險管理
  • 成本最佳化
  • 品管/安全
  • 供應鏈管理
  • 運作/維護

第10章 市場估價與預測：依最終用戶分類，2022-2035年

  • 總承包商
  • 分包商
  • 專案業主/開發商
  • 建築師和工程師
  • 政府機構
  • 其他

第11章 市場估價與預測：按地區分類，2022-2035年

  • 北美洲
    • 美國
    • 加拿大
  • 歐洲
    • 德國
    • 英國
    • 法國
    • 義大利
    • 西班牙
    • 俄羅斯
    • 挪威
    • 荷蘭
    • 瑞典
  • 亞太地區
    • 中國
    • 印度
    • 日本
    • 澳洲
    • 韓國
    • 新加坡
    • 泰國
    • 印尼
    • 越南
  • 拉丁美洲
    • 巴西
    • 墨西哥
    • 阿根廷
  • 中東和非洲
    • 南非
    • 沙烏地阿拉伯
    • 阿拉伯聯合大公國
    • 土耳其

第12章：公司簡介

  • 世界公司
    • Oracle
    • Autodesk
    • Procore Technologies
    • Trimble
    • Bentley Systems
    • Hexagon
    • Nemetschek
    • Dassault Systemes
    • SAP
    • RIB Software
  • 本地公司
    • OpenSpace
    • CMiC
    • InEight
    • Buildots
    • Kahua
    • PlanRadar
    • Trackunit
    • Doxel
    • Alice Technologies
    • AVEVA
簡介目錄
Product Code: 11356

The Global Big Data Analytics in Construction Market was valued at USD 10.3 billion in 2025 and is estimated to grow at a CAGR of 10.9% to reach USD 28.9 billion by 2035.

Market Scope
Start Year2025
Forecast Year2026-2035
Start Value$10.3 Billion
Forecast Value$28.9 Billion
CAGR10.9%

The market continues to gain momentum as construction companies accelerate the adoption of digital technologies to improve project planning, operational visibility, and overall execution. The growing volume of project-generated information is driving demand for advanced analytics solutions capable of transforming complex datasets into actionable business intelligence. Construction firms increasingly rely on connected digital environments to monitor productivity, optimize resource allocation, improve project scheduling, and strengthen financial oversight throughout the project lifecycle. Continuous data collection across construction operations has significantly expanded the need for platforms that deliver faster insights and support informed decision-making. The rising emphasis on digital project management, predictive analytics, and intelligent automation is further reinforcing market expansion. Organizations are increasingly investing in integrated analytics capabilities that support higher operational efficiency while minimizing delays and unnecessary expenditures. At the same time, rapid infrastructure development and broader digital transformation initiatives across multiple regions continue to create favorable conditions for long-term growth in the big data analytics in construction market, making data-driven decision-making an essential component of modern construction management.

The 400G segment accounted for a 82.3% share in 2025 and is anticipated to grow at a CAGR of 11.2% between 2026 and 2035. The solutions segment generates the largest revenue share, supported by increasing investments in software platforms designed to centralize and analyze construction data from multiple digital sources. Construction companies continue to prioritize intelligent analytics solutions that combine project information into unified operational environments, allowing stakeholders to improve planning accuracy, monitor project performance, strengthen cost management, optimize scheduling, enhance workforce productivity, and support asset lifecycle management. The growing preference for integrated software ecosystems positions solution-based offerings as the leading contributor to overall market revenue.

The cloud-based segment held a 64.6% share in 2025 and is forecast to grow at a CAGR of 12.5% from 2026 through 2035. Cloud deployment continues to lead the market as organizations increasingly favor scalable subscription-based platforms that provide centralized data storage, real-time accessibility, advanced analytics capabilities, and seamless collaboration across distributed project teams. The ongoing transition toward cloud-first business models, expanding software ecosystems, stronger cybersecurity capabilities, and continuous platform enhancements is encouraging broader adoption among contractors, project owners, and infrastructure developers. Lower implementation costs, greater operational flexibility, automatic updates, and faster deployment timelines further support the growing preference for cloud-based analytics solutions.

Asia Pacific Big Data Analytics In Construction Market reached USD 0.9 billion in 2025. Market growth continues to benefit from sustained infrastructure investments, expanding digital transformation initiatives, and increasing adoption of data-driven construction management practices. The widespread implementation of standardized digital construction frameworks has accelerated the use of structured project information, enabling construction organizations to improve operational efficiency, project coordination, and data management across large-scale developments. Continued public and private sector investments in digital construction technologies are expected to strengthen China's leadership within the regional market.

Major companies operating in the global big data analytics in construction market include Oracle, Autodesk, Procore Technologies, Trimble, Bentley Systems, Hexagon, Nemetschek, Dassault Systemes, SAP, and RIB Software. Companies operating in the big data analytics in construction market are strengthening their market position by expanding cloud-based software portfolios, integrating artificial intelligence and machine learning capabilities, and enhancing interoperability across digital construction platforms. Market participants continue to invest in product innovation to improve project intelligence, predictive analytics, and operational visibility throughout the construction lifecycle. Strategic partnerships with technology providers, engineering firms, and infrastructure developers are helping companies broaden their customer reach while delivering more comprehensive digital solutions. Businesses are also focusing on expanding subscription-based service models, improving cybersecurity features, and developing scalable platforms that support enterprise-wide deployments.

Table of Contents

Chapter 1 Research Methodology

  • 1.1 Research approach
  • 1.2 Quality Commitments
    • 1.2.1 GMI AI policy & data integrity commitment
      • 1.2.1.1 Source consistency protocol
  • 1.3 Research Trail & Confidence Scoring
    • 1.3.1 Research Trail Components
    • 1.3.2 Scoring Components
  • 1.4 Data Collection
    • 1.4.1 Partial list of primary sources
  • 1.5 Data mining sources
    • 1.5.1 Paid sources
      • 1.5.1.1 Sources, by region
  • 1.6 Base estimates and calculations
    • 1.6.1 Base year calculation for any one approach
  • 1.7 Forecast model
    • 1.7.1 Quantified market impact analysis
      • 1.7.1.1 Mathematical impact of growth parameters on forecast
  • 1.8 Research transparency addendum
    • 1.8.1 Source attribution framework
    • 1.8.2 Quality assurance metrics
    • 1.8.3 Our commitment to trust

Chapter 2 Executive Summary

  • 2.1 Industry 360° synopsis, 2022 – 2035
  • 2.2 Key market trends
    • 2.2.1 Regional
    • 2.2.2 Component
    • 2.2.3 Deployment Model
    • 2.2.4 Technology
    • 2.2.5 Construction
    • 2.2.6 Application
    • 2.2.7 End User
  • 2.3 TAM Analysis, 2026-2035
  • 2.4 CXO perspectives: Strategic imperatives

Chapter 3 Industry Insights

  • 3.1 Industry ecosystem analysis
    • 3.1.1 Supplier landscape
    • 3.1.2 Profit margin analysis
    • 3.1.3 Cost structure
    • 3.1.4 Value addition at each stage
    • 3.1.5 Factor affecting the value chain
    • 3.1.6 Disruptions
  • 3.2 Industry impact forces
    • 3.2.1 Growth drivers
      • 3.2.1.1 Rising Adoption of IoT, Connected Equipment & Smart Job Site Technologies Generating Massive Construction Data Volumes
      • 3.2.1.2 Growing Demand for Real-Time Project Performance Monitoring, Predictive Analytics & Cost Optimization
      • 3.2.1.3 Expansion of Government-Led Digital Construction, Smart Infrastructure & BIM Mandates Accelerating Analytics Adoption
      • 3.2.1.4 Increasing Integration of BIM, Digital Twins & AI-Driven Analytics Platforms Across the Construction Lifecycle
    • 3.2.2 Industry pitfalls and challenges
      • 3.2.2.1 High Implementation Costs & Legacy IT Infrastructure Incompatibility
      • 3.2.2.2 Shortage of Data Science & Analytics Talent in the Construction Workforce
    • 3.2.3 Market opportunities
      • 3.2.3.1 Untapped Potential in SME Construction Firms Transitioning to Data-Driven Operations
      • 3.2.3.2 Expansion of Cloud-Native Analytics Platforms Tailored for Construction Use Cases
      • 3.2.3.3 Integration of Predictive Analytics with Autonomous Construction Equipment
      • 3.2.3.4 Growth of Analytics-as-a-Service Models for Project-Based Deployments
  • 3.3 Growth potential analysis
  • 3.4 Technology and innovation landscape
    • 3.4.1 Current technological trends
    • 3.4.2 Emerging technologies
  • 3.5 Pricing Analysis (Driven by primary research)
    • 3.5.1 Historical Price Trend Analysis, 2022-2025
    • 3.5.2 Pricing Strategy by Player Type
  • 3.6 Regulatory landscape
    • 3.6.1 North America
      • 3.6.1.1 California Consumer Privacy Act (CCPA) & California Privacy Rights Act (CPRA) – Data Privacy Compliance
      • 3.6.1.2 National Institute of Standards and Technology (NIST) Cybersecurity Framework 2.0
      • 3.6.1.3 Federal Information Security Modernization Act (FISMA) for Government Construction Projects
      • 3.6.1.4 Infrastructure Investment and Jobs Act (IIJA) Digital Project Delivery Requirements
    • 3.6.2 Europe
      • 3.6.2.1 General Data Protection Regulation (GDPR)
      • 3.6.2.2 Network and Information Security Directive 2 (NIS2)
      • 3.6.2.3 EU Public Procurement Directive & BIM Adoption Requirements
      • 3.6.2.4 EU Data Act & Data Governance Framework
      • 3.6.2.5 EU AI Act for High-Risk AI Applications
    • 3.6.3 Asia Pacific
      • 3.6.3.1 China's Personal Information Protection Law (PIPL)
      • 3.6.3.2 China's Data Security Law (DSL) & Cybersecurity Law (CSL)
      • 3.6.3.3 India's Digital Personal Data Protection (DPDP) Act, 2023
      • 3.6.3.4 Singapore Personal Data Protection Act (PDPA) & BCA BIM Requirements
      • 3.6.3.5 Japan Act on the Protection of Personal Information (APPI) & MLIT BIM Policies
    • 3.6.4 Latin America
      • 3.6.4.1 Brazil General Data Protection Law (LGPD)
      • 3.6.4.2 Brazil BIM BR Strategy & Federal BIM Decrees
      • 3.6.4.3 Mexico Federal Law on Protection of Personal Data Held by Private Parties (LFPDPPP)
      • 3.6.4.4 National Digital Infrastructure & Public Procurement Regulations
    • 3.6.5 Middle East & Africa
      • 3.6.5.1 UAE Personal Data Protection Law (PDPL)
      • 3.6.5.2 Dubai Municipality BIM Mandate for Major Construction Projects
      • 3.6.5.3 Saudi Personal Data Protection Law (PDPL) & Vision 2030 Digital Construction Initiatives
      • 3.6.5.4 South Africa Protection of Personal Information Act (POPIA)
      • 3.6.5.5 GCC Digital Government & Smart Infrastructure Compliance Frameworks
  • 3.7 Porter’s analysis
  • 3.8 PESTEL analysis
  • 3.9 Patent analysis (Driven by primary research)
  • 3.10 Cost breakdown analysis
  • 3.11 Impact of AI and Generative AI on the Market
    • 3.11.1 AI Driven Disruption of Existing Business Models
    • 3.11.2 GenAI Use Cases and Adoption Roadmap by Segment
    • 3.11.3 Risks Limitations and Regulatory Considerations
  • 3.12 Forecast assumptions & scenario analysis (Driven by Primary Research)
    • 3.12.1 Base Case- Key Macro & Industry Variables Driving CAGR
    • 3.12.2 Optimistic Scenarios- Favorable macro and industry tailwinds
    • 3.12.3 Pessimistic Scenario - Macroeconomic slowdown or industry headwinds

Chapter 4 Competitive Landscape, 2025

  • 4.1 Introduction
  • 4.2 Company market share analysis
    • 4.2.1 North America
    • 4.2.2 Europe
    • 4.2.3 Asia Pacific
    • 4.2.4 LATAM
    • 4.2.5 MEA
  • 4.3 Competitive analysis of major market players
  • 4.4 Competitive positioning matrix
  • 4.5 Key developments
    • 4.5.1 Mergers & acquisitions
    • 4.5.2 Partnerships & collaborations
    • 4.5.3 New Product Launches
    • 4.5.4 Expansion Plans and funding
  • 4.6 Company tier benchmarking
    • 4.6.1 Tier classification criteria & qualifying thresholds
    • 4.6.2 Tier positioning matrix by revenue, geography & innovation

Chapter 5 Market Estimates & Forecast, By Component, 2022 - 2035 (USD Bn)

  • 5.1 Key trends
  • 5.2 Solution
    • 5.2.1 Data Management & Integration Tools
    • 5.2.2 Analytics Platforms
    • 5.2.3 Reporting & Dashboard Solutions
    • 5.2.4 BIM-Integrated Analytics Software
    • 5.2.5 ETL (Extract, Transform, Load) Tools
  • 5.3 Services
    • 5.3.1 Professional Services
    • 5.3.2 Managed Services

Chapter 6 Market Estimates & Forecast, By Deployment Model, 2022 - 2035 (USD Bn)

  • 6.1 Key trends
  • 6.2 Cloud-Based
  • 6.3 On-Premises
  • 6.4 Hybrid

Chapter 7 Market Estimates & Forecast, By Technology, 2022 - 2035 (USD Bn)

  • 7.1 Key trends
  • 7.2 Predictive Analytics
  • 7.3 Machine Learning & AI
  • 7.4 Data Visualization
  • 7.5 IoT Integration Analytics
  • 7.6 Others

Chapter 8 Market Estimates & Forecast, By Construction Type, 2022 - 2035 (USD Bn)

  • 8.1 Key trends
  • 8.2 Residential Construction
  • 8.3 Commercial Construction
  • 8.4 Infrastructure & Civil Projects
  • 8.5 Industrial Construction

Chapter 9 Market Estimates & Forecast, By Application, 2022 - 2035 (USD Bn)

  • 9.1 Key trends
  • 9.2 Project Management
  • 9.3 Design & Planning
  • 9.4 Construction Management
  • 9.5 Risk Management
  • 9.6 Cost Optimization
  • 9.7 Quality Control & Safety
  • 9.8 Supply Chain Management
  • 9.9 Operations & Maintenance

Chapter 10 Market Estimates & Forecast, By End User, 2022 - 2035 (USD Bn)

  • 10.1 Key trends
  • 10.2 General Contractors
  • 10.3 Subcontractors
  • 10.4 Project Owners & Developers
  • 10.5 Architects & Engineers
  • 10.6 Government Agencies
  • 10.7 Others

Chapter 11 Market Estimates & Forecast, By Region, 2022 - 2035 (USD Bn)

  • 11.1 Key trends
  • 11.2 North America
    • 11.2.1 US
    • 11.2.2 Canada
  • 11.3 Europe
    • 11.3.1 Germany
    • 11.3.2 UK
    • 11.3.3 France
    • 11.3.4 Italy
    • 11.3.5 Spain
    • 11.3.6 Russia
    • 11.3.7 Norway
    • 11.3.8 Netherlands
    • 11.3.9 Sweden
  • 11.4 Asia Pacific
    • 11.4.1 China
    • 11.4.2 India
    • 11.4.3 Japan
    • 11.4.4 Australia
    • 11.4.5 South Korea
    • 11.4.6 Singapore
    • 11.4.7 Thailand
    • 11.4.8 Indonesia
    • 11.4.9 Vietnam
  • 11.5 Latin America
    • 11.5.1 Brazil
    • 11.5.2 Mexico
    • 11.5.3 Argentina
  • 11.6 MEA
    • 11.6.1 South Africa
    • 11.6.2 Saudi Arabia
    • 11.6.3 UAE
    • 11.6.4 Turkey

Chapter 12 Company Profiles

  • 12.1 Global Players
    • 12.1.1 Oracle
    • 12.1.2 Autodesk
    • 12.1.3 Procore Technologies
    • 12.1.4 Trimble
    • 12.1.5 Bentley Systems
    • 12.1.6 Hexagon
    • 12.1.7 Nemetschek
    • 12.1.8 Dassault Systemes
    • 12.1.9 SAP
    • 12.1.10 RIB Software
  • 12.2 Regional Players
    • 12.2.1 OpenSpace
    • 12.2.2 CMiC
    • 12.2.3 InEight
    • 12.2.4 Buildots
    • 12.2.5 Kahua
    • 12.2.6 PlanRadar
    • 12.2.7 Trackunit
    • 12.2.8 Doxel
    • 12.2.9 Alice Technologies
    • 12.2.10 AVEVA