按资料类型、货币化方法、产业、地区、范围和预测划分的全球资料货币化市场规模

资料货币化市场规模及预测

2023 年数据货币化市场规模为 35 亿美元，预计在 2024 年至 2030 年预测期内将以 20.3% 的复合年增长率增长，并在 2030 年达到 85 亿美元。数据货币化市场是指将原始数据转换为可出售以产生收入的有价值的见解、产品和服务的过程。这个市场包括公司用来提取、分析和商业化资料资产的各种策略和技术。该市场包括数据聚合、分析和视觉化等技术，以得出可透过各种管道货币化的可行见解。

资料货币化的全球市场推动因素

数据货币化市场的市场推动因素受到多种因素的影响。

资料量增加：

随着数位科技变得越来越普遍，组织、人员和网路设备产生的资料量呈指数级增长。数据量的成长为组织提供了将其数据资产货币化的机会。

先进的分析与资料技术：

机器学习和人工智慧等分析技术的发展使组织能够从数据中获得有意义的见解。这些见解可以透过多种方式货币化，包括提供数据驱动的产品和服务以及客製化广告。

越来越重视数据货币化策略：

公司越来越认识到其数据资产的价值，并积极寻求将其货币化的方法。这包括规划如何向第三方行销、打包和销售数据，以及如何透过简化决策程序来创造价值。

监理环境：

CCPA 和 GDPR 等监管框架提高了人们对资料保护和安全的认识，并迫使组织考虑采用合规的方式来将其资料资产货币化。参与数据货币化营运的公司必须考虑遵守这些要求。

数据市场提供了购买、销售和交换数据资产的场所，并且变得越来越受欢迎。透过促进用户和数据生产者之间的交易，这些市场增加了数据货币化生态系统的可近性和流动性。

产业融合与合作关係：

该行业正在日益加强合作并建立合作伙伴关係，以利用彼此的数据资产实现互惠互利。跨产业协作可协助公司创造新的收入来源并开发创造性的数据驱动解决方案。

对个人化体验的需求：

在个人化体验方面，客户对各行业公司的期望越来越高。数据货币化使企业能够利用消费者资讯来创建客製化产品、服务和广告活动，从而提高客户的满意度和忠诚度。

全球资料货币化市场的阻碍因素

有几个因素可能会成为数据货币化市场的限制和挑战。其中包括：

资料隐私问题：

资料隐私问题：由于对资料安全和隐私的担忧日益增加，寻求将资料货币化的组织面临重大障碍。 CCPA 和 GDPR 等法规对资料控制和权限设定了严格限制，这使得企业保持合规性和保护客户隐私至关重要。

缺乏资料品质与治理：

不良的资料治理和品质可能会阻碍资料货币化工作的成功。不准确、不完整或过时的数据可能会产生不可靠的见解和决策，从而对数据货币化工作的价值主张产生负面影响。必须建立强而有力的治理和品质框架，以确保资料资产的有效性和可靠性。

资料孤岛与碎片：

在组织内部，资料孤岛和碎片化使资料货币化工作变得复杂。多样化的系统和资料来源阻碍了资料整合和互通性，使得难以获得有价值的见解并最大化资料资产的价值。为了最大限度地提高资料货币化专案的价值，您需要打破组织界限并培养资料共享和协作的文化。

缺乏知识与经验：

许多公司没有意识到其数据资产的潜在价值，并且缺乏成功地将其数据资产货币化所需的知识和经验。为了克服这一障碍，利害关係人必须瞭解数据货币化的好处，并获得支援和培训以培养数据分析技能。

获利策略复杂性：

制定和实施可获利的数据货币化计划需要大量的努力和资源。公司必须管理许多问题，例如目标市场选择、定价策略、分销路线和有价值的数据资产。在製定和实施货币化策略方面缺乏清晰度和经验可能会阻碍数据货币化市场的成功。

竞争格局：

在竞争激烈的数据货币化产业，许多公司都在争夺市场占有率。新创公司、数据经纪人和成熟的科技公司都在争取数据货币化机会。在这样的竞争环境下，企业可能很难在竞争中脱颖而出并获得市场占有率。

道德与社会问题：

数据的适当使用及其对人类和社会的潜在影响提出了数据货币化带来的道德和社会问题。如果资料货币化过程不以道德和透明的方式进行，则可能会出现偏见、歧视和资料利用等问题。解决这些问题并维护道德标准对于建立信誉并培养对数据货币化行业的信任是必要的。

目录

第1章简介

• 市场定义
• 市场区隔
• 调查方法

第 2 章执行摘要

• 主要发现
• 市场概览
• 市场亮点

第3章市场概述

• 市场规模与成长潜力
• 市场趋势
• 市场推动因素
• 市场阻碍因素
• 市场机会
• 波特五力分析

第 4 章资料货币化市场：依资料类型

• 结构化数据
• 非结构化数据
• 半结构化数据
• 防护装备

第5章资料变现市场：依变现方式

• 直接获利
• 间接获利
• 基于订阅的获利方式
• 按使用付费获利

第 6 章资料货币化市场：依产业划分

• 银行、金融服务和保险 (BFSI)
• 医疗保健
• 零售/电子商务
• 製造业
• 通讯/媒体
• 交通/物流

第7章区域分析

• 北美
• 美国
• 加拿大
• 墨西哥
• 欧洲
• 英国
• 德国
• 法国
• 义大利
• 亚太地区
• 中国
• 日本
• 印度
• 澳大利亚
• 拉丁美洲
• 巴西
• 阿根廷
• 智利
• 中东/非洲
• 南非
• 沙乌地阿拉伯
• 阿拉伯联合大公国

第 8 章市场动态

• 市场推动因素
• 市场阻碍因素
• 市场机会
• 新冠肺炎 (COVID-19) 对市场的影响

第9章竞争态势

• 主要公司
• 市占率分析

第10章公司简介

• IBM Corporation
• Oracle Corporation
• Salesforce.com, Inc.
• SAP SE
• SAS Institute Inc.
• Teradata Corporation
• Accenture plc
• Infosys Limited
• Capgemini SE
• Adobe Inc.
• Google LLC

第 11 章市场前景与机会

• 新兴技术
• 未来市场趋势
• 投资机会

第12章附录

• 缩写列表
• 来源与参考文献

Data Monetization Market Size And Forecast

Data Monetization Market size was valued at USD 3.5 Billion in 2023 and is projected to reach USD 8.5 Billion by 2030, growing at a CAGR of 20.3 % during the forecast period 2024-2030. The Data Monetization Market refers to the process of converting raw data into valuable insights, products, or services that can be sold to generate revenue. This market encompasses various strategies and technologies used by organizations to extract, analyze, and commercialize their data assets. It includes techniques such as data aggregation, analytics, and visualization to derive actionable insights that can be monetized through various channels.

Global Data Monetization Market Drivers

The market drivers for the Data Monetization Market can be influenced by various factors. These may include:

Increasing Data Volume:

As digital technologies have spread widely, the amount of data produced by organizations, people, and networked devices has increased exponentially. Organizations have the opportunity to monetize their data assets due to the volume of data.

Advanced Analytics and Data Technologies:

Organisations may now extract meaningful insights from their data thanks to developments in analytics techniques like machine learning and artificial intelligence. These insights can be made profitable in a number of ways, such by providing data-driven goods and services or specialized advertising.

A Greater Attention to Data Monetization Strategies:

Companies are aggressively looking for ways to monetize their data assets as they become more and more aware of their worth. This entails creating plans for how to market, package, and sell data to third parties or how to create value by streamlining decision-making procedures.

Regulatory Environment:

Organisations are being prompted to investigate compliant methods of monetizing their data assets by regulatory frameworks like the CCPA and GDPR, which have raised awareness regarding data protection and security. Businesses who are involved in data monetization operations must take compliance with these requirements into account.

Data marketplaces are becoming more and more popular, offering venues for the purchase, sale, and exchange of data assets. By facilitating trades between users and data producers, these markets increase accessibility and liquidity within the ecosystem of data monetization.

Industry Convergence and Partnerships:

In order to take advantage of one another's data assets for mutual gain, industries are working together more and more and establishing partnerships. Collaborations across industries help businesses generate new revenue streams and develop creative data-driven solutions.

Demand for Personalised Experiences:

Customers are coming to expect more and more from companies in a variety of industries when it comes to personalized experiences. Through data monetization, businesses can use consumer information to create customized goods, services, and advertising campaigns that increase client happiness and loyalty.

Global Data Monetization Market Restraints

Several factors can act as restraints or challenges for the Data Monetization Market. These may include:

Data Privacy Issues:

Organisations trying to monetize their data face major obstacles due to increased concerns about data security and privacy. Strict limits on data management and permission are enforced by regulatory regulations like the CCPA and GDPR, thus it is crucial for businesses to maintain compliance and safeguard customer privacy.

Absence of Data Quality and Governance:

Inadequate data governance and quality might make data monetization efforts less successful. The value proposition for initiatives to monetize data can be negatively impacted by inaccurate, incomplete, or out-of-date data since it can produce untrustworthy insights and judgments. To ensure the validity and dependability of data assets, strong governance, and quality frameworks must be established.

Data Silos and Fragmentation:

Within organizations, data silos and fragmentation can present difficulties for data monetization initiatives. Diverse systems and data sources impede data integration and interoperability, which makes it challenging to extract valuable insights and realize the full value of data assets. Maximizing the value of data monetization projects requires breaking down organizational boundaries and promoting a culture of data sharing and collaboration.

Lack of Knowledge and Experience:

A lot of businesses are unaware of the potential value of their data assets, and they can also lack the knowledge or experience necessary to successfully monetize them. Overcoming this obstacle requires educating stakeholders about the advantages of data monetization and offering assistance and training to develop data analytics skills.

Complexity of Monetization Strategy:

Creating and putting into practice a profitable data monetization plan may need a lot of work and resources. Businesses have to manage a number of issues, including selecting target markets, pricing strategies, distribution routes, and precious data assets. Success in the data monetization market might be hampered by a lack of clarity or experience in developing and implementing monetization strategies.

Competitive Landscape:

There are many companies fighting for market share in the data monetization industry, which is growing more and more competitive. Startups, data brokers, and well-established tech firms are all vying for the opportunity to profit from data monetization. In this highly competitive environment, organizations could find it difficult to stand out from the competition and gain market share.

Ethical and Social Issues:

The appropriate use of data and its possible effects on people and society present ethical and social issues that are brought up by data monetization. If processes for data monetization are not carried out in an ethical and transparent manner, problems like bias, discrimination, and data exploitation may occur. Establishing credibility and fostering confidence in the data monetization industry requires addressing these issues and upholding moral standards.

Global Data Monetization Market Segmentation Analysis

The Global Data Monetization Market is Segmented on the basis of Data Type, Monetization Method, Industry Vertical, and Geography.

Data Monetization Market, By Data Type

• Structured Data:
• Data that is predetermined and arranged in a specific way, as found in databases, spreadsheets, and tables, is referred to as structured data.
• Unstructured Data:
• Unstructured data, which includes text-heavy files like emails, social media posts, and multimedia material, lacks a predetermined format.
• Semi-structured Data:
• Semi-structured data refers to information like XML files and JSON documents that have some structure but do not neatly fit into a relational database.
• Protective Gear:
• Items made to keep players safe during games, such as padding, headgear, and mouthguards.

Data Monetization Market, By Monetization Method

• Direct Monetization:
• Charging third parties directly for the sale of raw or processed data.
• Indirect Monetization:
• Using data to improve already-existing goods or services, draw in clients, or boost productivity, all of which tangentially result in income production.
• Subscription-based Monetization:
• Offering data access or insights through subscription-based models, where clients pay a regular charge for access to data products or services, is known as subscription-based monetization.
• Pay-per-Use Monetization:
• Cost-per-use charging clients according to how much they use data services or goods is known as monetization; this is frequently accomplished through usage-based pricing schemes or metered billing.

Data Monetization Market, By Industry Vertical

• Banking, Financial Services, and Insurance (BFSI):
• Making money off of fraud detection tools, risk analytics, customer behavior insights, and financial transaction data.
• Healthcare:
• Using clinical data, real-world evidence, and patient health records to advance medical research, personalized therapy, and healthcare analytics.
• Retail and E-commerce:
• Supply chain optimization, tailored marketing, and personalized suggestions can be achieved by monetizing consumer purchase history, browsing habits, and demographic information.
• Telecommunications and Media:
• Using subscriber data, usage trends, and network utilization insights to generate revenue for network optimization, content recommendations, and targeted advertising.
• Manufacturing:
• Using supply chain data, production metrics, and machine sensor data to generate revenue for process optimization, quality assurance, and predictive maintenance.
• Transportation and Logistics:
• Making the most of route optimization insights, fleet tracking data, and transportation analytics to enhance customer service, fuel efficiency, and logistics management.

Data Monetization Market, By Geography

• North America
• Europe
• Asia Pacific
• Latin America
• Middle East and Africa

Key Players

• The major players in the Data Monetization Market are:
• IBM Corporation
• Oracle Corporation
• com, Inc.
• SAP SE
• SAS Institute Inc.
• Teradata Corporation
• Accenture plc
• Infosys Limited
• Capgemini SE
• Adobe Inc.
• Google LLC

TABLE OF CONTENTS

1. Introduction

• Market Definition
• Market Segmentation
• Research Methodology

2. Executive Summary

• Key Findings
• Market Overview
• Market Highlights

3. Market Overview

• Market Size and Growth Potential
• Market Trends
• Market Drivers
• Market Restraints
• Market Opportunities
• Porter's Five Forces Analysis

4. Data Monetization Market, By Data Type

• Structured Data
• Unstructured Data
• Semi-structured Data
• Protective Gear

5. Data Monetization Market, By Monetization Method

• Direct Monetization
• Indirect Monetization
• Subscription-based Monetization
• Pay-per-Use Monetization

6. Data Monetization Market, By Industry Vertical

• Banking, Financial Services, and Insurance (BFSI)
• Healthcare
• Retail and E-commerce
• Manufacturing
• Telecommunications and Media
• Transportation and Logistics

7. Regional Analysis

• North America
• United States
• Canada
• Mexico
• Europe
• United Kingdom
• Germany
• France
• Italy
• Asia-Pacific
• China
• Japan
• India
• Australia
• Latin America
• Brazil
• Argentina
• Chile
• Middle East and Africa
• South Africa
• Saudi Arabia
• UAE

8. Market Dynamics

• Market Drivers
• Market Restraints
• Market Opportunities
• Impact of COVID-19 on the Market

9. Competitive Landscape

• Key Players
• Market Share Analysis

10. Company Profiles

• IBM Corporation
• Oracle Corporation
• Salesforce.com, Inc.
• SAP SE
• SAS Institute Inc.
• Teradata Corporation
• Accenture plc
• Infosys Limited
• Capgemini SE
• Adobe Inc.
• Google LLC

11. Market Outlook and Opportunities

• Emerging Technologies
• Future Market Trends
• Investment Opportunities

12. Appendix

• List of Abbreviations
• Sources and References