合成资料：人工智慧和新生态系统的未来

改变企业利用资料并产生有意义的见解的方式

合成资料是根据从现实世界中发生的事件收集的资料人工产生的资料。人工生成的文字、表格、图像、影片等形式的资料。合成资料产生解决了低效资料集和隐私问题的挑战。

它使用演算法生成，允许组织测试营运资料并有效训练人工智慧 (AI)/机器学习 (ML) 模型。它对于检验数学模型和训练深度学习模型也很有用。鑑于全球范围内采用人工智慧/机器学习模型来改善运营，这项技术很可能在未来五年内成为主流。我们不断研究、开发和增强以标准化格式建立合成资料。

在这项研究中，Frost & Sullivan 透过人工产生的资料来评估资料驱动的转型。

本次调查对象

• 产生合成资料的模型与技术
• 现有和新的生态系统
• 科技相关发展与全球趋势
• 成长机会
• 战略见解和观点

目录

战略衝动

• 为什么成长如此困难？策略要务 8 (TM)：阻碍成长的因素
• The Strategic Imperative 8（TM）
• 战略要务对合成资料产业的影响
• 成长机会推动Growth Pipeline Engine（TM）
• 调查方法

成长机会分析

• 分析范围
• 分割
• 生长促进因子
• 成长阻碍因素

技术吸引力仪表板

• 技术吸引力仪表板

综合资料影响评估

• 合成资料及其故事生成框架
• 合成资料的类型以及如何创建它们
• 人工智慧与合成资料之间的关係
• 合成资料的应用与影响评估
• 生态系：科技颠覆多个产业
• 改变合成资料使用的顶尖研究
• 将合成资料模型收益
• 确保公平使用虚假资料的法规环境
• 合成资料技术的专利形势
• 资金筹措及投资场景
• 策略伙伴关係：B2B 对接会
• 区域趋势和见解
• 为什么公司需要合成资料？

成长机会宇宙

• 成长机会一：跨产业合作的开放原始码倡议
• 成长机会2：多模态综合资料
• 成长机会2：多模态综合资料
• 成长机会3：制定标准化协议

蓝图和策略见解

• 策略洞察力

附录

• 技术完备等级(TRL)：说明

下一步

Transforms the Way Businesses Use Data and Generate Meaningful Insights

Synthetic data is data generated artificially based on data collected from real-world occurrences. It is artificially generated data in the form of text, tables, images, and videos, among others. Synthetic data generation will address the challenge of inefficient datasets and privacy concerns.

Generated using algorithms, it enables organizations to test operational data and train artificial intelligence (AI)/machine learning (ML) models efficiently. It also helps validate mathematical models and train deep learning models. The technology will go mainstream in the next 5 years, considering the global adoption of AI/ML models to elevate operations. There is constant R&D and reinforcement for building synthetic data in a standardized format.

In this study, Frost & Sullivan will assess the transformation due to data usage caused by artificially generated data.

This research covers the following:

• Models and techniques to generate synthetic data
• Existing and emerging ecosystems
• Technology-related developments and global trends
• Growth opportunities
• Strategic insights and viewpoints

Table of Contents

Strategic Imperatives

• Why Is It Increasingly Difficult to Grow?The Strategic Imperative 8™: Factors Creating Pressure on Growth
• The Strategic Imperative 8™
• The Impact of the Top 3 Strategic Imperatives on the Synthetic Data Industry
• Growth Opportunities Fuel the Growth Pipeline Engine™
• Research Methodology

Growth Opportunity Analysis

• Scope of Analysis
• Segmentation
• Growth Drivers
• Growth Restraints

Technology Attractiveness Dashboard

• Technology Attractiveness Dashboard

Synthetic Data: Impact Assessment

• Synthetic Data and Its Story-Generation Frameworks
• Types of Synthetic Data and Their Creation
• Relationship Between AI and Synthetic Data
• Synthetic Data Applications and Impact Assessment
• Ecosystem-Technologies That Disrupt Multiple Industries
• Top Research Transforming the Use of Synthetic Data
• Monetization of Synthetic Data Models
• Regulatory Environment to Ensure Fair Usage of Fake Data
• Patent Landscape for Synthetic Data Technologies
• Funding and Investment Scenario
• Strategic Partnerships-B2B Matchmaking
• Regional Trends and Insights
• Why Do Businesses Need Synthetic Data?

Growth Opportunity Universe

• Growth Opportunity 1: Open-source Initiatives for Cross-industry Collaboration
• Growth Opportunity 2: Multi-modal Synthetic Data
• Growth Opportunity 2: Multi-modal Synthetic Data
• Growth Opportunity 3: Setting Up Standardization Protocols

Roadmap and Strategic Insights

• Strategic Insights

Appendix

• Technology Readiness Levels (TRL): Explanation

Next Steps

• Your Next Steps
• Why Frost, Why Now?
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