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市場調査レポート
商品コード
1677945
データラングリングの市場規模、シェア、成長分析:コンポーネント別、展開モデル別、業界別、組織規模別、業務機能別、地域別 - 産業予測 2025~2032年Data Wrangling Market Size, Share, and Growth Analysis, By Component (Tools, Services), By Deployment Model (On-Premises, Cloud), By Industry, By Organization Size, By Business Function, By Region - Industry Forecast 2025-2032 |
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データラングリングの市場規模、シェア、成長分析:コンポーネント別、展開モデル別、業界別、組織規模別、業務機能別、地域別 - 産業予測 2025~2032年 |
出版日: 2025年03月06日
発行: SkyQuest
ページ情報: 英文 196 Pages
納期: 3~5営業日
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データラングリング市場規模は2023年に38億米ドル、予測期間(2025-2032年)のCAGRは13.8%で、2024年の43億2,000万米ドルから2032年には121億6,000万米ドルに成長すると予測されています。
2023年10月現在、データラングリング業界は、データ損失インシデントの減少、BYOD動向、ワークプレイスモビリティに牽引され、大きな成長を遂げようとしています。しかし、機械学習や人工知能(AI)の進歩とともに、データの急速な増大と複雑化から課題も生じています。ITリーダーは、データの関連性やツールの効果的な使用といった重要な運用上の問題に直面しています。AIをうまく活用するには、情報に意味があり、コンテキストが豊富であることを保証する強固なデータ管理が必要です。組織は強力なデータ関係を構築し、ITアーキテクチャを包括的に可視化するソリューションを活用してアナリティクスを効率化しなければならないです。AIがより重要な意思決定の役割を担うようになる中、効果的なデータ管理に注力することは、AIのメリットを享受しながら企業目標を達成するために不可欠となると思われます。
Data Wrangling Market size was valued at USD 3.8 billion in 2023 and is poised to grow from USD 4.32 billion in 2024 to USD 12.16 billion by 2032, growing at a CAGR of 13.8% during the forecast period (2025-2032).
As of October 2023, the data wrangling industry is poised for significant growth driven by the decline in data loss incidents, BYOD trends, and workplace mobility. However, challenges arise from the rapid growth and complexity of data alongside advancements in machine learning and artificial intelligence (AI). IT leaders face crucial operations issues such as data relevance and the effective use of tools. Successfully harnessing AI necessitates robust data management that ensures information is meaningful and context-rich. Organizations must foster strong data relationships and leverage solutions that provide comprehensive visibility into IT architecture to streamline analytics. As AI takes on more critical decision-making roles, focusing on effective data management will be integral to achieving corporate objectives while reaping AI benefits.
Top-down and bottom-up approaches were used to estimate and validate the size of the Data Wrangling market and to estimate the size of various other dependent submarkets. The research methodology used to estimate the market size includes the following details: The key players in the market were identified through secondary research, and their market shares in the respective regions were determined through primary and secondary research. This entire procedure includes the study of the annual and financial reports of the top market players and extensive interviews for key insights from industry leaders such as CEOs, VPs, directors, and marketing executives. All percentage shares split, and breakdowns were determined using secondary sources and verified through Primary sources. All possible parameters that affect the markets covered in this research study have been accounted for, viewed in extensive detail, verified through primary research, and analyzed to get the final quantitative and qualitative data.
Data Wrangling Market Segments Analysis
Global Data Wrangling Market is segmented by Component, Deployment Model, Industry, Organization Size, Business Function and region. Based on Component, the market is segmented into Tools and Services. Based on Deployment Model, the market is segmented into On-Premises and Cloud. Based on Industry, the market is segmented into Banking, Financial Services, and Insurance, Government & Public Sector, Healthcare & Life Sciences, Retail & Ecommerce, Travel & Hospitality, Telecom & IT, Energy & Utilities and Others. Based on Organization Size, the market is segmented into Small and Medium-Sized Enterprises and Large Enterprises. Based on Business Function, the market is segmented into Marketing and Sales, Finance, Operations, HR and Legal. Based on region, the market is segmented into North America, Europe, Asia Pacific, Latin America and Middle East & Africa.
Driver of the Data Wrangling Market
The Data Wrangling market is driven by the increasing complexity of managing vast amounts of unstructured data sourced from diverse origins. This complexity necessitates advanced data wrangling tools that streamline the transformation and organization of data, enabling businesses to derive valuable insights. As organizations face the challenge of efficiently handling intricate datasets, the demand for robust data wrangling solutions continues to rise. This heightened need reflects the broader trend in data management, where effective wrangling processes are essential for optimizing operational efficiency and informed decision-making. Consequently, the market for data wrangling tools is experiencing significant growth as enterprises seek to harness the power of their data.
Restraints in the Data Wrangling Market
The Data Wrangling market faces several constraints, primarily stemming from the challenges organizations encounter when managing substantial volumes of sensitive information. Ensuring compliance with stringent data privacy regulations, such as the General Data Protection Regulation (GDPR), poses a significant hurdle. Companies may hesitate to adopt data wrangling solutions due to these compliance concerns, as they prioritize safeguarding data security. The need to maintain privacy while effectively processing and transforming extensive datasets can create additional obstacles, ultimately stifling the growth of data wrangling services. As a result, organizations must navigate these complexities to implement effective data management practices while adhering to legal requirements.
Market Trends of the Data Wrangling Market
As of October 2023, the data wrangling market is experiencing a notable trend characterized by the increasing integration of AI and machine learning technologies. These advancements facilitate the automation of data cleaning, transformation, and analysis, markedly enhancing the accuracy and efficiency of data processes. Consequently, organizations can derive actionable insights rapidly, which in turn empowers informed decision-making across diverse sectors. This technological convergence not only streamlines workflows but also fosters innovation, prompting a surge in demand for intuitive, AI-driven data wrangling solutions. As businesses aim to leverage big data for strategic advantage, the market is poised for substantial growth and transformation.