资料发现市场规模：依组织规模、组件、部署模型、垂直、区域、范围和预测

资料发现市场规模及预测

数据发现市场规模预计将在 2024 年达到 107.7 亿美元，在 2031 年达到 341.2 亿美元，2024 年至 2031 年的复合年增长率为 15.50%。数据发现是寻找、理解和视觉化组织内不同来源的相关数据的过程。这就像在浩瀚的资讯海洋中航行并发现隐藏的宝藏——无价的见解可以帮助我们做出更好的决策、优化营运并发掘新的机会。与专注于从大型资料集中提取模式的资料探勘不同，资料发现允许使用者迭代地探索和分析资料、提出问题并优化搜寻。数据发现主要有两种方法：手动和自动。手动资料发现涉及资料管理员和分析师仔细识别、分类和记录资料资产。这种传统方法需要深厚的技术知识，并且对于大型资料集来说非常耗时。现代解决方案利用机器学习驱动的自动资料发现工具。这些工具扫瞄各种资料储存库，将资讯分类，建立资料目录，并为使用者提供可搜寻的资料资源索引。

数据发现不仅仅是找到正确的数据，还包括以一种让用户产生共鸣的方式呈现数据。可视化是这里的关键。资料发现工具提供各种图表、图形和仪表板，可将复杂的资料集转换为易于理解的格式。趋势、模式和异常一目瞭然，帮助使用者瞭解数据所讲述的故事。互动式仪表板让使用者深入瞭解具体的细节，从而促进更深入的调查和分析。

传统上，数据分析一直是资料科学家和分析师的领域。但自助资料发现 (SSDD) 工具的兴起正在改变这一局面。 SSDD 平台专为具有最低技术专长的商业用户设计。这些用户友好的介面使用户能够独立探索数据、建立报告和回答业务问题。这不仅释放了 IT 资源，而且还培养了一种数据驱动的文化，让每个人都可以为明智的决策做出贡献。

资料发现市场动态

主要市场推动因素：

资料驱动决策的重要性日益增加：

公司正意识到直觉和本能的限制。以数据发现的洞察力为推动力的数据驱动决策可带来更明智的策略和更好的结果。

资料量呈指数成长：

社群媒体、物联网设备、感测器网路和其他因素正在导致组织产生的资料量激增。数据发现工具对于探索这个浩瀚的数据海洋和提取有价值的见解至关重要。

自助资料发现 (SSDD) 的兴起：

传统上，数据分析一直是 IT 专业人员的领域。 SSDD 工具使业务用户能够自行探索数据，从而培养数据驱动的文化并使整个组织能够更快地做出决策。

业务效率改善请求：

资料发现有助于识别流程效率低下和瓶颈。透过分析营运数据，企业可以优化工作流程、降低成本、简化营运以提高整体绩效。

提高对客户的理解：

客户资料包含有关行为、偏好和购买模式的丰富知识。数据发现工具有助于解锁这些见解，使企业能够个人化行销活动，改善客户服务，并开发出更能引起目标受众共鸣的产品和服务。

法规遵循与资料治理：

随着 GDPR 和 CCPA 等资料隐私法规的不断增加，确保资料安全和合规性至关重要。先进的资料发现工具透过维护资料品质、实施存取控制和促进合规工作来帮助资料治理。

大数据科技的演进：

云端运算、人工智慧和机器学习等技术的进步正在推动数据发现市场向前发展。这些进步使得资料发现解决方案的资料处理速度更快、分析能力更强大、洞察力产生更自动化。

主要问题

资料孤岛与缺乏标准化：

资料通常是孤立的，分散在组织内的不同来源。格式和结构的多样性使得发现和整合资料以进行全面分析变得困难。

资料品质问题：

资料的准确性和完整性直接影响您从资料发现中获得的洞察的品质。不一致的数据、缺失的值和重复会导致误导性的结果。

使用者技能差距与采用情况：

自助式资料发现赋予使用者权力，但技能差距阻碍了其采用。为了弥合这一差距并使用户能够有效地利用数据发现工具的潜力，提供培训计划和培育数据驱动的文化非常重要。

大数据管理的复杂性：

资料量和速度的不断增长对资料发现工具提出了课题。整合大数据技术并确保可扩展的资料处理能力对于有效管理大量资料集的复杂性至关重要。

主要趋势

自然语言处理 (NLP) 革命：

随着 NLP 的整合，数据发现变得更加用户友好。用户可以使用自然语言查询与资料交互，从而使探索变得直观，甚至非技术用户也可以存取。这使得更广泛的员工能够在决策中利用数据洞察。

增强分析以获得更深入的见解：

人工智慧 (AI) 正在透过增强分析改变数据发现。人工智慧可以自动执行数据分析任务，例如识别模式、产生见解和提供建议。这使得用户能够更好地瞭解他们的数据并做出更明智的决策。

协作资料探索：

资料探索的未来是协作的。先进的工具可以实现无缝的基于团队的发现项目，促进知识共享和明智的决策。具有不同技能的团队成员可以一起工作并结合他们的专业知识来从您的数据中获得最大价值。

关注可解释人工智慧（XAI）：

随着人工智慧在数据发现中发挥越来越大的作用，确保其产生的见解的可解释性将至关重要。 XAI技术使AI决策过程透明化，让使用者瞭解建议背后的原因，建立对AI驱动的资料探索的信任。

透过设计确保安全性和隐私性：

随着资料隐私法规的不断加强，资料安全和隐私成为首要关注的问题。我们的资料发现解决方案在设计原则上整合了安全性和隐私性。这可确保在整个数据发现过程中保护数据，从而降低风险并建立对数据驱动决策的信任。

按地区划分的资料发现市场分析

北美：

北美目前在数据发现领域占有最大的市场占有率，预计在市场估计和预测期内仍将保持其主导地位。

北美公司在采用数据分析解决方案方面处于领先地位，并已拥有成熟的市场和知名的参与者。早期采用是其主导的原因之一。

北美公司在 IT 基础设施和软体上投入了大量资金，包括资料发现工具。高额的 IT 支出正在推动数据发现的需求。

HIPAA 和 CCPA 等加强的资料隐私法规正在推动采用确保合规性的资料发现工具。

亚太地区 (APAC)：

据 VMR 分析师称，亚太地区预计将出现数据发现市场最快的成长。

亚太地区各经济体的快速经济成长正在推动数位转型计划，包括采用数据分析，从而导致数据发现市场快速成长。

亚太地区的许多政府正在推行数据驱动的治理，并投资大数据基础设施，为数据发现工具创造了肥沃的土壤。这些政府措施是推动亚太地区数据发现市场快速成长的因素之一。

亚太地区科技人口的不断增长将推动复杂资料发现解决方案的采用和实施。

亚太地区智慧型手机用户的增加产生了大量数据，因此需要分析和利用这些资讯的工具。

欧洲：

欧洲在资料发现领域占有很大的市场占有率。

欧洲 GDPR 要求强而有力的资料治理，资料发现工具可以促进这一点。严格的监管环境是欧洲数据发现市场成长的主要原因之一。

欧洲公司以注重技术创新而闻名，并率先采用了先进的数据发现解决方案。

SAP 和 Qlik 等欧洲公司为资料发现市场做出了重大贡献。

目录

第 1 章 全球资料发现市场：简介

市场概况
• 研究范围
• 先决条件

第 2 章执行摘要

第 3 章 已验证市场研究研究方法

• 资料探勘
• 验证
• 主要来源
• 资料来源列表

第 4 章 全球资料发现市场展望

• 概述
• 市场动态
  • 驱动程式
  • 阻碍因素
  • 机会
• 波特五力模型
• 价值链分析

第 5 章。

• 概述
• 大型企业
• 中小型企业

6. 全球资料发现市场（按组件划分）

• 概述
• 软体
• 服务

7. 全球资料发现市场（依部署模式）

• 概述
• 基于云端
• 本地

第 8 章。

• 概述
• 医疗保健
• 政府与国防
• IT 和电信
• 英国金融服务业协会
• 其他

第 9 章。

• 概述
• 北美洲
  • 美国
  • 加拿大
  • 墨西哥
• 欧洲
  • 德国
  • 英国
  • 法国
  • 其他欧洲国家
  亚太地区
  • 中国
  • 日本
  • 印度
  • 其他亚太地区
• 世界其他地区
  • 拉丁美洲
  • 中东和非洲

第 10 章。

• 概述
• 各公司的市场排名
• 主要发展策略

第 11 章 公司简介

• Oracle Corporation
• SAP
• Micro strategy, Inc.
• Qlik Technologies, Inc.
• Tibco Software Inc.
• Platfora
• Datameer Inc.
• Cloud era, Inc.
• Data Watch Corporation
• Clearstory Data

第 12 章 重大进展

• 产品发布/开发
• 合併和收购
• 业务扩展
• 伙伴关係和合作关係

第 13 章附录

• 相关研究

Data Discovery Market Size And Forecast

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.

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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.

TABLE OF CONTENTS

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

12 KEY DEVELOPMENTS

• 12.1 Product Launches/Developments
• 12.2 Mergers and Acquisitions
• 12.3 Business Expansions
• 12.4 Partnerships and Collaborations

13 Appendix

• 13.1 Related Research