首頁 > 市場調查報告書 > 通訊

市場調查報告書

商品編碼

1624497

資料發現市場規模：依組織規模、組件、部署模型、垂直、區域、範圍和預測

Data Discovery Market Size By Organization Size, By Component, By Deployment Model, By Vertical, By Geographic Scope And Forecast

出版日期: 2024年10月10日 | 出版商:

Verified Market Research | 英文 202 Pages | 商品交期: 2-3個工作天內

價格

簡介目錄

資料發現市場規模及預測

數據發現市場規模預計將在 2024 年達到 107.7 億美元，在 2031 年達到 341.2 億美元，2024 年至 2031 年的複合年增長率為 15.50%。數據發現是尋找、理解和視覺化組織內不同來源的相關數據的過程。這就像在浩瀚的資訊海洋中航行並發現隱藏的寶藏——無價的見解可以幫助我們做出更好的決策、優化營運並發掘新的機會。與專注於從大型資料集中提取模式的資料探勘不同，資料發現允許使用者迭代地探索和分析資料、提出問題並優化搜尋。數據發現主要有兩種方法：手動和自動。手動資料發現涉及資料管理員和分析師仔細識別、分類和記錄資料資產。這種傳統方法需要深厚的技術知識，並且對於大型資料集來說非常耗時。現代解決方案利用機器學習驅動的自動資料發現工具。這些工具掃瞄各種資料儲存庫，將資訊分類，建立資料目錄，並為使用者提供可搜尋的資料資源索引。

數據發現不僅僅是找到正確的數據，還包括以一種讓用戶產生共鳴的方式呈現數據。可視化是這裡的關鍵。資料發現工具提供各種圖表、圖形和儀表板，可將複雜的資料集轉換為易於理解的格式。趨勢、模式和異常一目瞭然，幫助使用者瞭解數據所講述的故事。互動式儀表板讓使用者深入瞭解具體的細節，從而促進更深入的調查和分析。

傳統上，數據分析一直是資料科學家和分析師的領域。但自助資料發現 (SSDD) 工具的興起正在改變這一局面。 SSDD 平台專為具有最低技術專長的商業用戶設計。這些用戶友好的介面使用戶能夠獨立探索數據、建立報告和回答業務問題。這不僅釋放了 IT 資源，而且還培養了一種數據驅動的文化，讓每個人都可以為明智的決策做出貢獻。

資料發現市場動態

主要市場推動因素：

資料驅動決策的重要性日益增加：

公司正意識到直覺和本能的限制。以數據發現的洞察力為推動力的數據驅動決策可帶來更明智的策略和更好的結果。

資料量呈指數成長：

社群媒體、物聯網設備、感測器網路和其他因素正在導致組織產生的資料量激增。數據發現工具對於探索這個浩瀚的數據海洋和提取有價值的見解至關重要。

自助資料發現 (SSDD) 的興起：

傳統上，數據分析一直是 IT 專業人員的領域。 SSDD 工具使業務用戶能夠自行探索數據，從而培養數據驅動的文化並使整個組織能夠更快地做出決策。

業務效率改善請求：

資料發現有助於識別流程效率低下和瓶頸。透過分析營運數據，企業可以優化工作流程、降低成本、簡化營運以提高整體績效。

提高對客戶的理解：

客戶資料包含有關行為、偏好和購買模式的豐富知識。數據發現工具有助於解鎖這些見解，使企業能夠個人化行銷活動，改善客戶服務，並開發出更能引起目標受眾共鳴的產品和服務。

法規遵循與資料治理：

隨著 GDPR 和 CCPA 等資料隱私法規的不斷增加，確保資料安全和合規性至關重要。先進的資料發現工具透過維護資料品質、實施存取控制和促進合規工作來幫助資料治理。

大數據科技的演進：

雲端運算、人工智慧和機器學習等技術的進步正在推動數據發現市場向前發展。這些進步使得資料發現解決方案的資料處理速度更快、分析能力更強大、洞察力產生更自動化。

主要問題

資料孤島與缺乏標準化：

資料通常是孤立的，分散在組織內的不同來源。格式和結構的多樣性使得發現和整合資料以進行全面分析變得困難。

資料品質問題：

資料的準確性和完整性直接影響您從資料發現中獲得的洞察的品質。不一致的數據、缺失的值和重複會導致誤導性的結果。

使用者技能差距與採用情況：

自助式資料發現賦予使用者權力，但技能差距阻礙了其採用。為了彌合這一差距並使用戶能夠有效地利用數據發現工具的潛力，提供培訓計劃和培育數據驅動的文化非常重要。

大數據管理的複雜性：

資料量和速度的不斷增長對資料發現工具提出了課題。整合大數據技術並確保可擴展的資料處理能力對於有效管理大量資料集的複雜性至關重要。

主要趨勢

自然語言處理 (NLP) 革命：

隨著 NLP 的整合，數據發現變得更加用戶友好。用戶可以使用自然語言查詢與資料交互，從而使探索變得直觀，甚至非技術用戶也可以存取。這使得更廣泛的員工能夠在決策中利用數據洞察。

增強分析以獲得更深入的見解：

人工智慧 (AI) 正在透過增強分析改變數據發現。人工智慧可以自動執行數據分析任務，例如識別模式、產生見解和提供建議。這使得用戶能夠更好地瞭解他們的數據並做出更明智的決策。

協作資料探索：

資料探索的未來是協作的。先進的工具可以實現無縫的基於團隊的發現項目，促進知識共享和明智的決策。具有不同技能的團隊成員可以一起工作並結合他們的專業知識來從您的數據中獲得最大價值。

關注可解釋人工智慧（XAI）：

隨著人工智慧在數據發現中發揮越來越大的作用，確保其產生的見解的可解釋性將至關重要。 XAI技術使AI決策過程透明化，讓使用者瞭解建議背後的原因，建立對AI驅動的資料探索的信任。

透過設計確保安全性和隱私性：

隨著資料隱私法規的不斷加強，資料安全和隱私成為首要關注的問題。我們的資料發現解決方案在設計原則上整合了安全性和隱私性。這可確保在整個數據發現過程中保護數據，從而降低風險並建立對數據驅動決策的信任。

按地區劃分的資料發現市場分析

北美：

北美目前在數據發現領域佔有最大的市場佔有率，預計在市場估計和預測期內仍將保持其主導地位。

北美公司在採用數據分析解決方案方面處於領先地位，並已擁有成熟的市場和知名的參與者。早期採用是其主導的原因之一。

北美公司在 IT 基礎設施和軟體上投入了大量資金，包括資料發現工具。高額的 IT 支出正在推動數據發現的需求。

HIPAA 和 CCPA 等加強的資料隱私法規正在推動採用確保合規性的資料發現工具。

亞太地區 (APAC)：

據 VMR 分析師稱，亞太地區預計將出現數據發現市場最快的成長。

亞太地區各經濟體的快速經濟成長正在推動數位轉型計劃，包括採用數據分析，從而導致數據發現市場快速成長。

亞太地區的許多政府正在推行數據驅動的治理，並投資大數據基礎設施，為數據發現工具創造了肥沃的土壤。這些政府措施是推動亞太地區數據發現市場快速成長的因素之一。

亞太地區科技人口的不斷增長將推動複雜資料發現解決方案的採用和實施。

亞太地區智慧型手機用戶的增加產生了大量數據，因此需要分析和利用這些資訊的工具。

歐洲：

歐洲在資料發現領域佔有很大的市場佔有率。

歐洲 GDPR 要求強而有力的資料治理，資料發現工具可以促進這一點。嚴格的監管環境是歐洲數據發現市場成長的主要原因之一。

歐洲公司以注重技術創新而聞名，並率先採用了先進的數據發現解決方案。

SAP 和 Qlik 等歐洲公司為資料發現市場做出了重大貢獻。

Data Discovery Market size was valued at 10.77 USD Billion in 2024 and is projected to reach 34.12 USD Billion by 2031 , growing at a CAGR of 15.50% from 2024 to 2031. Data discovery is the process of finding, understanding, and visualizing relevant data from various sources within an organization. It's akin to navigating a vast ocean of information and uncovering hidden treasures - valuable insights that can inform better decision-making, optimize operations, and unlock new opportunities. Unlike data mining, which focuses on extracting patterns from large datasets, data discovery empowers users to explore and analyze data iteratively, asking questions and refining their search as they go. There are two primary approaches to data discovery: manual and automated. Manual data discovery involves data stewards and analysts meticulously identifying, classifying, and documenting data assets. This traditional approach requires deep technical knowledge and can be time-consuming for vast datasets. Modern solutions leverage automated data discovery tools powered by machine learning. These tools scan various data repositories, categorize information, and build data catalogs, providing users with a searchable index of their data resources.

Data discovery isn't just about finding the right data; it's about presenting it in a way that resonates with users. Visualizations are the key here. Data discovery tools offer a wide range of charts, graphs, and dashboards that transform complex data sets into easily digestible formats. Trends, patterns, and anomalies become readily apparent, enabling users to grasp the story the data is telling. Interactive dashboards allow users to drill down into specific details, fostering deeper exploration and analysis.

Traditionally, data analysis was the domain of data scientists and analysts. However, the rise of self-service data discovery (SSDD) tools is changing the game. SSDD platforms are designed for business users with minimal technical expertise. These user-friendly interfaces enable them to independently explore data, generate reports, and answer their business questions. This not only frees up IT resources but also fosters a data-driven culture where everyone can contribute to informed decision-making.

Data Discovery Market Dynamics

The key market dynamics that are shaping the data discovery market include:

Key Market Drivers:

Growing Importance of Data-Driven Decisions:

Businesses are increasingly recognizing the limitations of intuition and gut feeling. Data-driven decision-making, fueled by insights from data discovery, leads to more informed strategies and improved outcomes.

Exponential Growth of Data Volume:

The amount of data organizations generate is exploding, driven by factors like social media, IoT devices, and sensor networks. Data discovery tools are essential for navigating this vast data ocean and extracting valuable insights.

Rise of Self-Service Data Discovery (SSDD):

Traditionally, data analysis was the domain of IT experts. SSDD tools empower business users to explore data independently, fostering a data-driven culture and enabling faster decision-making across the organization.

Demand for Improved Operational Efficiency:

Data discovery helps identify inefficiencies and bottlenecks in processes. By analyzing operational data, businesses can optimize workflows, reduce costs, and streamline operations for overall performance improvement.

Enhancing Customer Understanding:

Customer data holds a wealth of knowledge about behavior, preferences, and buying patterns. Data discovery tools unlock these insights, allowing businesses to personalize marketing campaigns, improve customer service, and develop products and services that resonate better with their target audience.

Regulatory Compliance and Data Governance:

With stricter data privacy regulations like GDPR and CCPA, ensuring data security and compliance is crucial. Advanced data discovery tools assist with data governance by maintaining data quality, enforcing access controls, and facilitating compliance efforts.

Advancement in Big Data Technologies:

The evolution of technologies like cloud computing, artificial intelligence, and machine learning is propelling the data discovery market forward. These advancements enable faster data processing, more robust analytics capabilities, and automated insights generation within data discovery solutions.

Key Challenges:

Data Silos and Lack of Standardization:

Data is often scattered across various sources within an organization, creating silos. These disparate formats and structures make it difficult to discover and integrate data for comprehensive analysis.

Data Quality Issues:

The accuracy and completeness of data directly impact the quality of insights derived through data discovery. Inconsistent data, missing values, and duplicates lead to misleading results.

User Skill Gap and Adoption:

While self-service data discovery empowers users, a skills gap can hinder adoption. Providing training programs and fostering a data-driven culture are crucial to bridge this gap and encourage users to leverage the potential of data discovery tools effectively.

Complexity of Big Data Management:

The ever-increasing volume and velocity of data pose challenges for data discovery tools. Integrating big data technologies and ensuring scalable data processing capabilities are essential to handle the complexities of managing massive datasets effectively.

Key Trends:

Natural Language Processing (NLP) Revolution:

Data discovery is becoming more user-friendly with the integration of NLP. Users can interact with data using natural language queries, making exploration intuitive and accessible even for non-technical users. This empowers a wider range of employees to leverage data insights in their decision-making.

Augmented Analytics for Deeper Insights:

Artificial intelligence (AI) is transforming data discovery with augmented analytics. AI automates data analysis tasks like identifying patterns, generating insights, and providing recommendations. This empowers users to gain a deeper understanding of their data and make more informed decisions.

Collaborative Data Exploration:

The future of data discovery lies in fostering collaboration. Advanced tools will enable seamless team-based exploration projects, facilitating knowledge sharing and informed decision-making. Team members with different skill sets can work together, combining their expertise to extract maximum value from the data.

Focus on Explainable AI (XAI):

As AI plays a bigger role in data discovery, ensuring the explainability of AI-generated insights is crucial. XAI techniques will make AI's decision-making processes transparent, allowing users to understand the reasoning behind recommendations and fostering trust in AI-driven data exploration.

Security and Privacy by Design:

With data privacy regulations becoming stricter, data security and privacy are paramount concerns. Data discovery solutions are incorporating security and privacy by design principles. This ensures data is protected throughout the discovery process, mitigating risks and fostering trust in data-driven decision-making.

What's inside a VMR industry report?

Our reports include actionable data and forward-looking analysis that help you craft pitches, create business plans, build presentations and write proposals.

Data Discovery Market Regional Analysis

Here is a more detailed regional analysis of the data discovery market:

North America:

North America currently holds the largest market share in data discovery and is estimated to hold the dominant position for the forecasting period.

North American companies have been at the forefront of adopting data analytics solutions, fostering a mature market with established players. This early adoption is one of the reasons for their dominant position.

North American organizations allocate significant budgets for IT infrastructure and software, including data discovery tools. High IT spending is propelling the demand for data discovery.

Stricter data privacy regulations like HIPAA and CCPA drive the adoption of data discovery tools that ensure compliance.

Asia Pacific (APAC):

According to VMR analysts, the APAC region is expected to witness the fastest growth in the data discovery market.

Rapid economic growth across APAC economies is driving digital transformation initiatives, including data analytics adoption leading to rapid growth in the data discovery market.

Many APAC governments are promoting data-driven governance and investing in big data infrastructure, creating a fertile ground for data discovery tools. These government initiatives are one of the key drivers to the rapid growth of the data discovery market in the Asia Pacific region

The expanding tech talent pool in APAC facilitates the adoption and implementation of complex data discovery solutions.

The rising smartphone user base in APAC generates vast amounts of data, creating a demand for tools to analyze and utilize this information.

Europe:

Europe holds a significant market share in data discovery.

GDPR in Europe necessitates robust data governance, which data discovery tools can facilitate. This strong regulatory landscape is one of the major reasons for Europe's growth in the data discovery market.

European companies are known for their focus on innovation, leading to the early adoption of advanced data discovery solutions.

Several European firms like SAP and Qlik contribute significantly to the data discovery market landscape.

Data Discovery Market Segmentation Analysis

The Data Discovery Market is segmented based on Organization Size, Component, Deployment Model, Vertical, and Geography.

Data Discovery Market, By Organization Size

Large Enterprises
Small and Medium Enterprises

Based on Organization size, the market is bifurcated into Large Enterprises and Small and Medium Enterprises. Large Enterprises are currently the dominant force in the data discovery market, Small and Medium Enterprises (SMEs) are expected to close the gap significantly by 2031. Large Enterprises possess vast data volumes and complex data needs, necessitating robust data discovery solutions. However, their existing IT infrastructure and budget allocations might limit the growth rate.

The market for data discovery solutions specifically designed for SMEs is experiencing a boom. Cloud-based, subscription-model data discovery tools are becoming more affordable for SMEs, making them a viable option. Self-service data discovery tools are designed for user-friendliness, empowering non-technical users within SMEs to leverage data insights. SMEs are increasingly recognizing the value of data for making informed decisions, driving their adoption of data discovery tools. This shift towards self-service analytics and affordable solutions is expected to propel the SME segment's growth in the coming years. While Large Enterprises will likely maintain a larger market share in absolute terms, SMEs are poised to become a significant driving force in the data discovery market by 2031.

Data Discovery Market, By Component

Software
Services

Based on Components, the market is bifurcated into Software and Services. Software is expected to retain the dominant position throughout the forecast period, driven by its core functionality. Data discovery software provides the essential tools for data exploration, visualization, and analysis, forming the foundation for any data discovery initiative. However, Services will experience significant growth due to the increasing complexity of data environments and the rise of self-service data discovery. As organizations adopt self-service tools, they'll require implementation, training, and ongoing support services to ensure successful adoption and maximize the value derived from data discovery solutions. This creates a symbiotic relationship - the growth of software fuels the demand for services, and robust services empower users to leverage the full potential of the software, solidifying its dominance.

Data Discovery Market, By Deployment Model

Cloud-based
On-premises

Based on the Deployment model, the market is bifurcated into Cloud-based and On-premises. Cloud-based data discovery solutions are poised to significantly outpace on-premises deployments in the forecast period. This dominance can be attributed to several factors: scalability and cost-effectiveness. Cloud-based solutions offer on-demand scalability, allowing organizations to easily adjust their data discovery capabilities based on evolving needs. Additionally, cloud platforms eliminate the need for upfront hardware and software investments, making them a more attractive option for budget-conscious organizations. While on-premises deployments might still be preferred by some due to security concerns or regulatory compliance requirements, the overall market is shifting towards the flexibility, agility, and cost benefits offered by cloud-based data discovery solutions.

Data Discovery Market, By Vertical

Healthcare
Government
Defence

Based on Vertical, the market is bifurcated into Healthcare, Government, and Defence. Healthcare Government & Defense are expected to exhibit significant growth. Healthcare is leveraging data discovery for tasks like improving patient outcomes, drug discovery, and fraud detection. Government agencies are utilizing it for citizen service optimization, national security, and resource allocation. However, the sheer volume of data generated in the Government & Defense sectors, coupled with increasing investments in big data initiatives for national security and intelligence gathering, might lead them to hold a larger market share in the coming years. Healthcare, however, will continue to be a major driver due to the ever-growing need for data-driven personalized medicine and improved healthcare delivery systems.

Data Discovery Market, By Geography

North America
Europe
Asia Pacific
Rest of the World

Based on regional analysis, the Data Discovery Market is classified into North America, Europe, Asia Pacific, and the Rest of the World. On the market for data discovery, North America holds the current lead due to established market players and high IT spending, APAC is expected to experience explosive growth. This surge in APAC is fueled by factors like rapid economic expansion, government initiatives promoting big data adoption, and a growing tech talent pool. Both regions will be major players, with North America capitalizing on its strong foundation and APAC leveraging its growth potential. The future market landscape will likely be multipolar, with other regions like Europe, and Middle East & Africa playing increasingly significant roles.

Key Players

The "Data Discovery Market" study report will provide valuable insight with an emphasis on the global market. The major players in the market are
IBM, Microsoft, Oracle, Salesforce, SAS Institute, Google, Amazon Web Services, Micro Focus, Alteryx, Qlik, ThoughtSpot, Looker, Tableau, Domo, and Yellowfin.

Our market analysis also entails a section solely dedicated to such major players wherein our analysts provide an insight into the financial statements of all the major players, along with product benchmarking and SWOT analysis. The competitive landscape section also includes key development strategies, market share, and market ranking analysis of the above-mentioned players globally.

Data Discovery Market Recent Developments
In May 2024, Microsoft announced enhancements to its Power BI platform, integrating new AI-powered features for data storytelling. This includes a "Storytelling Assistant" that suggests visuals and insights, and a "Live Q&A" feature allowing users to interact with data using natural language queries.
In April 2024, Google Cloud launched BigQuery Data Mesh, a new solution aimed at simplifying data management in complex cloud environments. This offering promotes a decentralized approach, allowing business users to manage their data assets more independently while ensuring consistency and governance.
In March 2024, Amazon Web Services (AWS) announced tighter integration between its data discovery service, Amazon QuickSight, and its data warehousing solution, Amazon Redshift. This integration streamlines the process of querying and analyzing data stored in Redshift directly from the QuickSight interface.
In February 2024, Looker, the data discovery and business intelligence platform acquired by Google, unveiled "Data Actions" - a new feature allowing users to trigger actions within external applications directly from Looker dashboards. This streamlines workflows and empowers users to take immediate action based on data insights.
In January 2024, Salesforce bolstered its Einstein Analytics platform with new features focused on customer data analysis. These features include improved customer segmentation capabilities and AI-powered customer journey mapping, allowing businesses to gain a deeper understanding of their customer base.

1 INTRODUCTION OF GLOBAL DATA DISCOVERY MARKET

1.1 Overview of the Market
1.2 Scope of Report
1.3 Assumptions

2 EXECUTIVE SUMMARY

3 RESEARCH METHODOLOGY OF VERIFIED MARKET RESEARCH

3.1 Data Mining
3.2 Validation
3.3 Primary Interviews
3.4 List of Data Sources

4 GLOBAL DATA DISCOVERY MARKET OUTLOOK

4.1 Overview
4.2 Market Dynamics
- 4.2.1 Drivers
- 4.2.2 Restraints
- 4.2.3 Opportunities
4.3 Porters Five Force Model
4.4 Value Chain Analysis

5 GLOBAL DATA DISCOVERY MARKET, BY ORGANIZATION SIZE

5.1 Overview
5.2 Large Enterprises
5.3 Small and Medium Enterprises

6 GLOBAL DATA DISCOVERY MARKET, BY COMPONENT

6.1 Overview
6.2 Software
6.3 Services

7 GLOBAL DATA DISCOVERY MARKET, BY DEPLOYMENT MODEL

7.1 Overview
7.2 Cloud-based
7.3 On-premise

8 GLOBAL DATA DISCOVERY MARKET, BY VERTICAL

8.1 Overview
8.2 Healthcare
8.3 Government and Defense
8.4 It and Telecom
8.5 BFSI
8.6 Others

9 GLOBAL DATA DISCOVERY MARKET, BY GEOGRAPHY

9.1 Overview
9.2 North America
- 9.2.1 U.S.
- 9.2.2 Canada
- 9.2.3 Mexico
9.3 Europe
- 9.3.1 Germany
- 9.3.2 U.K.
- 9.3.3 France
- 9.3.4 Rest of Europe
9.4 Asia Pacific
- 9.4.1 China
- 9.4.2 Japan
- 9.4.3 India
- 9.4.4 Rest of Asia Pacific
9.5 Rest of the World
- 9.5.1 Latin America
- 9.5.2 Middle East and Africa

10 GLOBAL DATA DISCOVERY MARKET COMPETITIVE LANDSCAPE

10.1 Overview
10.2 Company Market Ranking
10.3 Key Development Strategies

11 COMPANY PROFILES

11.1 Oracle Corporation
- 11.1.1 Overview
- 11.1.2 Financial Performance
- 11.1.3 Product Outlook
- 11.1.4 Key Developments
11.2 SAP
- 11.2.1 Overview
- 11.2.2 Financial Performance
- 11.2.3 Product Outlook
- 11.2.4 Key Developments
11.3 Micro strategy, Inc.
- 11.3.1 Overview
- 11.3.2 Financial Performance
- 11.3.3 Product Outlook
- 11.3.4 Key Developments
11.4 Qlik Technologies, Inc.
- 11.4.1 Overview
- 11.4.2 Financial Performance
- 11.4.3 Product Outlook
- 11.4.4 Key Developments
11.5 Tibco Software Inc.
- 11.5.1 Overview
- 11.5.2 Financial Performance
- 11.5.3 Product Outlook
- 11.5.4 Key Developments
11.6 Platfora
- 11.6.1 Overview
- 11.6.2 Financial Performance
- 11.6.3 Product Outlook
- 11.6.4 Key Developments
11.7 Datameer Inc.
- 11.7.1 Overview
- 11.7.2 Financial Performance
- 11.7.3 Product Outlook
- 11.7.4 Key Developments
11.8 Cloud era, Inc.
- 11.8.1 Overview
- 11.8.2 Financial Performance
- 11.8.3 Product Outlook
- 11.8.4 Key Developments
11.9 Data Watch Corporation
- 11.9.1 Overview
- 11.9.2 Financial Performance
- 11.9.3 Product Outlook
- 11.9.4 Key Developments
11.10 Clearstory Data
- 11.10.1 Overview
- 11.10.2 Financial Performance
- 11.10.3 Product Outlook
- 11.10.4 Key Developments