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巨量資料工程服務：市場佔有率分析、產業趨勢與統計、成長預測（2024-2029）

Big Data Engineering Services - Market Share Analysis, Industry Trends & Statistics, Growth Forecasts (2024 - 2029)
出版日期: | 出版商: Mordor Intelligence | 英文 120 Pages | 商品交期: 2-3個工作天內

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簡介目錄

巨量資料工程服務市場規模預計到 2024 年為 793.4 億美元，預計到 2029 年將達到 1622.2 億美元，在預測期內（2024-2029 年）複合年成長率為 15.38%。

巨量資料工程服務-市場

資料整合和工程需要應用程式介面。資料工程師使用專用的工具、程序和設備來準備和分析資料以供以後分析。

主要亮點

  • 巨量資料工程服務市場的成長受到幾個關鍵因素的推動。首先，在數位技術激增的推動下，各行業資料生成呈指數級成長，對先進資料管理和處理解決方案的需求正在迅速成長。隨著公司尋求利用資料的潛力，他們擴大轉向巨量資料工程服務來最佳化儲存、處理和分析能力。
  • 金融業正在迅速變化並提供新的消費產品和服務。銀行業預計將對資料工程市場產生重大影響。例如，澳洲國民銀行和亞馬遜網路服務之間的合作關係正在不斷發展。該銀行表示，其 70% 的程式現已轉移到雲端，並且剛剛成為第一家轉換其線上商業銀行平台的澳洲大型銀行。
  • 醫療保健中使用的資料量正在迅速增加。電子健康記錄是醫療保健產業最普遍且最重要的資料來源。以前，這些資訊儲存在手寫檔案中。借助電子病歷創建的大量資料和機器學習等強大的分析技術，醫學研究人員現在能夠創建預測模型。
  • 此外，機器學習和人工智慧的進步為從大型資料集提取有價值的見解開闢了新的可能性，促使組織投資資料工程服務以支援其人工智慧舉措。此外，資料隱私和安全的監管要求迫使公司採用更強大的資料管理實踐，增加了對巨量資料工程專業知識的需求。
  • 對於資料工程計劃來說，不了解特定使用者群體的需求是很困難的。處理無窮無盡的資料湧入和價值失調很快就會變得不堪重負。透過資料管治計劃建立全面的資料管理策略是應對此資料工程挑戰的潛在應對措施。

巨量資料工程服務市場趨勢

銀行業巨量資料分析預計將大幅成長

  • 銀行業正在見證巨量資料分析和工程的採用迅速增加。其主要原因是巨量資料分析在提高業務效率、客戶體驗和風險管理方面提供的巨大價值。摩根大通和富國銀行等公司正在大力投資巨量資料計劃，以利用業務中產生的大量資料。
  • 此外，巨量資料工程有助於處理、儲存和分析大量資料集，使銀行能夠有效地處理資料的速度、種類和數量。 Hadoop 和 Spark 等技術使銀行能夠大規模儲存和處理資料，從而加快決策速度並改善客戶服務。
  • 此外，數位銀行的成長和線上交易的盛行進一步增加了銀行業對巨量資料解決方案的需求。透過利用先進的分析和機器學習演算法，銀行可以提出個人化提案、簡化業務並有效降低風險。
  • 2023 年 12 月，印度聯合銀行與Accenture合作建構可擴展且安全的企業資料湖平台。該措施旨在提高業務效率，提供以客戶為中心的銀行服務並改善風險管理。該平台利用預測分析、機器學習和人工智慧從結構化和資料中產生有價值的見解。此外，它還為員工提供跨各種職能的強大資料視覺化和報告功能。

亞太地區佔主要市場佔有率

  • 近年來，由於數位技術的採用不斷增加，對資料主導決策的需求不斷成長，包括中國、新加坡、印度和馬來西亞在內的亞太地區的巨量資料工程市場出現了顯著成長 以及網際網路連接設備的激增 以及網際網路連接設備的激增。這些地區的企業正在認知到利用大量資料來獲取洞察並在全球市場上保持競爭力的價值。
  • 阿里巴巴、騰訊等主要企業處於巨量資料創新的前沿，利用其廣泛的用戶基礎和先進的分析能力，提供個人化服務並提高業務效率。例如，阿里巴巴的雲端運算部門阿里雲提供各種巨量資料解決方案，包括資料倉儲、分析和人工智慧服務，為各行業的公司提供服務。
  • 此外，東南亞領先的超級應用程式 Grab 等公司嚴重依賴巨量資料工程來最佳化其叫車、食品配送和金融服務平台。 Grab 利用資料分析來改善使用者體驗、最佳化司機分配並開發適合客戶偏好的新產品和服務，從而為公司的快速擴張和市場主導地位做出貢獻。
  • 同時，在馬來西亞，亞航等公司正在利用巨量資料工程來改變航空業，提供個人化的旅行體驗，並透過預測分析和機器學習演算法最佳化營運。亞航的資料主導方法使我們能夠簡化流程、降低成本並在高度動態的航空市場中保持競爭力。
  • 總體而言，亞太地區巨量資料工程市場的成長反映了數位轉型和創新的更廣泛趨勢，隨著競爭的加劇，各行業的公司都利用資料的力量來推動業務成功並釋放新的成長和差異化機會。

巨量資料工程服務業概況

憑藉差異化和附加價值服務的新機遇，適度分散的巨量資料工程服務市場有可能改變競爭格局。此外，許多行業都在人工智慧方面進行了大量投資，對巨量資料工程技能和能力的需求很高。為了獲得市場佔有率並擴大其在情報領域的服務範圍，埃森哲 (Accenture PLC) 和凱捷 (Capgemini SE) 等知名供應商正在收購和投資新公司和新技術。

  • 2023 年 10 月 Google Cloud 合作夥伴 Onyx 收購資料 ，這是一家以 IP主導的顧問公司，專門從事資料遷移、現代化和 BI/分析。 Datametica 獨特的產品套件可自動將資料倉儲、資料庫、ETL 流程和分析工作負載遷移到 Google Cloud 並實現現代化，為客戶提供快速結果並保證結果。這項策略性舉措增強了 Onix 的資料和人工智慧能力，並將其定位為 IP主導的雲端轉型和資料管理解決方案的領導者。
  • 2023 年 2 月，Alteryx 宣佈在 Alteryx 分析雲端平台中增強自助服務和企業級功能。重新設計的 Alteryx Designer Cloud 介面使現代資料工作者能夠以互動方式和協作方式分析、準備和管道資料。 Alteryx Auto Insights 讓分析師和資料工程師能夠輕鬆建立互動式報告，利用機器學習來顯示描述性值和關鍵促進因素，以做出更好的決策。該平台的可擴展性和安全性使組織能夠做出更快、更明智的決策，同時維護資料管治標準。

其他好處：

  • Excel 格式的市場預測 (ME) 表
  • 3 個月的分析師支持

目錄

第1章簡介

  • 研究假設和市場定義
  • 調查範圍

第2章調查方法

第3章執行摘要

第4章市場動態

  • 市場概況
  • 市場促進因素
    • 由於互連設備和社群媒體的顯著成長，非結構化資料量不斷增加
    • 來自資訊服務公司的具有成本效益的服務和尖端專業知識
  • 市場限制因素
    • 服務提供者無法提供即時見解
  • 波特五力分析
    • 新進入者的威脅
    • 買家/消費者的議價能力
    • 供應商的議價能力
    • 替代品的威脅
    • 競爭公司之間敵對關係的強度
  • 評估宏觀經濟因素對市場的影響

第5章 新科技趨勢

第6章 市場細分

  • 按類型
    • 資料建模
    • 資料整合
    • 資料品質
    • 分析
  • 按業務
    • 行銷和銷售
    • 金融
    • 手術
    • 人力資源
  • 按組織規模
    • 小型企業
    • 主要企業
  • 依部署類型
    • 雲
    • 本地
  • 按最終用戶產業
    • BFSI
    • 政府機構
    • 媒體/通訊
    • 零售業
    • 製造業
    • 衛生保健
    • 其他最終用戶產業
  • 地區
    • 北美洲
    • 歐洲
    • 亞太地區
    • 拉丁美洲
    • 中東/非洲

第7章 競爭格局

  • 公司簡介
    • Accenture PLC
    • Genpact Inc.
    • Cognizant Technology Solutions Corporation
    • Infosys Limited
    • Capgemini SE
    • NTT Data Inc.
    • Mphasis Limited
    • L&T Technology Services
    • Hexaware Technologies Inc.
    • KPMG LLP
    • Ernst & Young LLP
    • Latentview Analytics Corporation

第8章投資分析

第9章 市場機會及未來趨勢

簡介目錄
Product Code: 71352

The Big Data Engineering Services Market size is estimated at USD 79.34 billion in 2024, and is expected to reach USD 162.22 billion by 2029, growing at a CAGR of 15.38% during the forecast period (2024-2029).

Big Data Engineering Services - Market

Application programming interfaces are necessary for data integration and engineering. Data engineers use specialized tools, procedures, and equipment to prepare and analyze data for later analysis.

Key Highlights

  • The growth of the big data engineering services market has been driven by several key factors. Firstly, the exponential increase in data generation across various industries, fueled by the proliferation of digital technologies, has created a pressing need for advanced data management and processing solutions. As organizations seek to harness the potential of their data, they increasingly turn to big data engineering services to optimize storage, processing, and analysis capabilities.
  • The financial industry is quickly changing and providing new consumer products and services. The banking industry is expected to significantly impact the data engineering market. For instance, the partnership between the National Australia Bank and Amazon Web Services has grown. According to the bank, 70% of its programs have now been migrated to the cloud, and it just became the first significant Australian bank to convert its online business banking platform.
  • The amount of data used in healthcare is growing quickly. Electronic health records are the most prevalent significant data source in the healthcare industry. Earlier, this information was stored in handwritten files. Medical researchers can now create prediction models thanks to the enormous data created by EHRs and powerful analytics techniques like machine learning.
  • Furthermore, advancements in machine learning and artificial intelligence have opened up new possibilities for extracting valuable insights from large datasets, prompting organizations to invest in data engineering services to support their AI initiatives. Additionally, regulatory requirements around data privacy and security have compelled companies to adopt more robust data management practices, leading to increased demand for specialized big data engineering expertise.
  • Not comprehending the needs of a specific user group is difficult for a data engineering project. The endless influx of data and dealing with value inconsistencies can quickly become overwhelming. Establishing a thorough data management strategy with a data governance plan is one potential response to this data engineering challenge.

Big Data Engineering Services Market Trends

Big Data Analytics in Banking is Expected to Grow Significantly

  • The banking industry has witnessed a significant surge in the adoption of big data analytics and engineering, primarily due to the immense value they offer in enhancing operational efficiency, customer experience, and risk management. Companies like JPMorgan Chase and Wells Fargo have invested heavily in big data initiatives to harness the vast amounts of data generated within their operations.
  • Furthermore, big data engineering facilitates the processing, storage, and analysis of massive datasets, enabling banks to handle the velocity, variety, and volume of data efficiently. With technologies like Hadoop and Spark, banks can store and process data at scale, enabling faster decision-making and improved customer service.
  • Moreover, the growth of digital banking and the proliferation of online transactions have further fueled the demand for big data solutions in the banking sector. By leveraging advanced analytics and machine learning algorithms, banks can offer personalized recommendations, streamline operations, and mitigate risks effectively.
  • In December 2023, Union Bank of India partnered with Accenture to create a scalable and secure enterprise data lake platform. This initiative aims to enhance operational efficiency, provide customer-centric banking services, and improve risk management. The platform will leverage predictive analytics, machine learning, and artificial intelligence to generate valuable insights from structured and unstructured data. Additionally, it will empower employees with robust data visualization and reporting capabilities across various functions.

Asia-Pacific to Hold Major Market Share

  • The big data engineering market in Asia-Pacific countries like China, Singapore, India, Malaysia, and others has experienced significant growth in recent years, driven by factors such as increasing adoption of digital technologies, rising demand for data-driven decision-making, and the proliferation of internet-connected devices. Companies in these regions are recognizing the value of harnessing vast amounts of data to gain insights and stay competitive in the global market.
  • Key players like Alibaba and Tencent have been at the forefront of big data innovation, leveraging their extensive user bases and advanced analytics capabilities to offer personalized services and improve operational efficiency. For example, Alibaba's cloud computing arm, Alibaba Cloud, provides a range of big data solutions, including data warehousing, analytics, and artificial intelligence services, catering to businesses across various industries.
  • Moreover, companies like Grab, a key super app in Southeast Asia, rely heavily on big data engineering to optimize their ride-hailing, food delivery, and financial services platforms. Grab utilizes data analytics to enhance user experiences, optimize driver allocation, and develop new products and services tailored to customer preferences, contributing to its rapid expansion and market dominance.
  • Meanwhile, in Malaysia, companies like AirAsia are leveraging big data engineering to transform the aviation industry, offering personalized travel experiences and optimizing flight operations through predictive analytics and machine learning algorithms. AirAsia's data-driven approach has enabled it to streamline processes, reduce costs, and maintain a competitive edge in the highly dynamic airline market.
  • Overall, the growth of the big data engineering market in Asia-Pacific countries reflects a broader trend toward digital transformation and innovation, with companies across various sectors harnessing the power of data to drive business success and unlock new opportunities for growth and differentiation in an increasingly competitive landscape.

Big Data Engineering Services Industry Overview

With new opportunities for differentiation and value-added services, the moderately fragmented big data engineering services market has the potential to change the competitive landscape. Moreover, many sectors are investing extensively in artificial intelligence, and there is a high demand for big data engineering technology and capabilities. In order to gain market share in the intelligence sector and expand the scope of their service offerings, well-known vendors, such as Accenture PLC and Capgemini SE, are making acquisitions and investments in new companies and technologies.

  • October 2023: Onix, a Google Cloud partner, acquired Datametica, an IP-driven consulting firm specializing in data migration, modernization, and BI/analytics. Datametica's suite of proprietary products automates the migration and modernization of data warehouses, databases, ETL processes, and analytical workloads to Google Cloud, delivering faster results and guaranteed outcomes for customers. This strategic move enhances Onix's data and AI capabilities, positioning them as a leader in IP-driven solutions for cloud transformation and data management.
  • February 2023: Alteryx introduced enhanced self-service and enterprise-grade features in its Alteryx Analytics Cloud Platform. The reimagined Alteryx Designer Cloud interface empowers modern data workers to profile, prepare, and pipeline their data interactively and collaboratively. Analysts and data engineers can now build interactive reports with ease, and Alteryx Auto Insights leverages machine learning to surface explanatory values and key drivers for better decision-making. The platform's scalability and security ensure that organizations can make faster and more informed decisions while maintaining data governance standards.

Additional Benefits:

  • The market estimate (ME) sheet in Excel format
  • 3 months of analyst support

TABLE OF CONTENTS

1 INTRODUCTION

  • 1.1 Study Assumptions and Market Definition
  • 1.2 Scope of the Study

2 RESEARCH METHODOLOGY

3 EXECUTIVE SUMMARY

4 MARKET DYNAMICS

  • 4.1 Market Overview
  • 4.2 Market Drivers
    • 4.2.1 Increasing Volume of Unstructured Data due to the Phenomenal Growth of Interconnected Devices and Social Media
    • 4.2.2 Cost-effective Services and Cutting-edge Expertise Rendered by Data Servicing Companies
  • 4.3 Market Restraints
    • 4.3.1 Inability of Service Providers to Provide Real-time Insights
  • 4.4 Porter's Five Force Analysis
    • 4.4.1 Threat of New Entrants
    • 4.4.2 Bargaining Power of Buyers/Consumers
    • 4.4.3 Bargaining Power of Suppliers
    • 4.4.4 Threat of Substitute Products
    • 4.4.5 Intensity of Competitive Rivalry
  • 4.5 Assessment of the Impact of Macroeconomic Factors on the Market

5 EMERGING TECHNOLOGY TRENDS

6 MARKET SEGMENTATION

  • 6.1 By Type
    • 6.1.1 Data Modelling
    • 6.1.2 Data Integration
    • 6.1.3 Data Quality
    • 6.1.4 Analytics
  • 6.2 By Business Function
    • 6.2.1 Marketing and Sales
    • 6.2.2 Finance
    • 6.2.3 Operations
    • 6.2.4 Human Resource
  • 6.3 By Organization Size
    • 6.3.1 Small and Medium Enterprizes
    • 6.3.2 Large Enterprises
  • 6.4 By Deployement Type
    • 6.4.1 Cloud
    • 6.4.2 On-premise
  • 6.5 By End-user Industry
    • 6.5.1 BFSI
    • 6.5.2 Government
    • 6.5.3 Media and Telecommunication
    • 6.5.4 Retail
    • 6.5.5 Manufacturing
    • 6.5.6 Healthcare
    • 6.5.7 Other End-user Verticals
  • 6.6 Geography
    • 6.6.1 North America
    • 6.6.2 Europe
    • 6.6.3 Asia-Pacific
    • 6.6.4 Latin America
    • 6.6.5 Middle East & Africa

7 COMPETITIVE LANDSCAPE

  • 7.1 Company Profiles
    • 7.1.1 Accenture PLC
    • 7.1.2 Genpact Inc.
    • 7.1.3 Cognizant Technology Solutions Corporation
    • 7.1.4 Infosys Limited
    • 7.1.5 Capgemini SE
    • 7.1.6 NTT Data Inc.
    • 7.1.7 Mphasis Limited
    • 7.1.8 L&T Technology Services
    • 7.1.9 Hexaware Technologies Inc.
    • 7.1.10 KPMG LLP
    • 7.1.11 Ernst & Young LLP
    • 7.1.12 Latentview Analytics Corporation

8 INVESTMENT ANALYSIS

9 MARKET OPPORTUNITIES AND FUTURE TRENDS