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資料收益化

市場調查報告書

商品編碼

1620208

按資料類型、貨幣化方法、產業、地區、範圍和預測劃分的全球資料貨幣化市場規模

Global Data Monetization Market Size By Data Type, By Monetization Method, By Industry Vertical, By Geographic Scope And Forecast

出版日期: 2024年07月09日 | 出版商:

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

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簡介目錄

資料貨幣化市場規模及預測

2023 年數據貨幣化市場規模為 35 億美元，預計在 2024 年至 2030 年預測期內將以 20.3% 的複合年增長率增長，並在 2030 年達到 85 億美元。數據貨幣化市場是指將原始數據轉換為可出售以產生收入的有價值的見解、產品和服務的過程。這個市場包括公司用來提取、分析和商業化資料資產的各種策略和技術。該市場包括數據聚合、分析和視覺化等技術，以得出可透過各種管道貨幣化的可行見解。

資料貨幣化的全球市場推動因素

數據貨幣化市場的市場推動因素受到多種因素的影響。

資料量增加：

隨著數位科技變得越來越普遍，組織、人員和網路設備產生的資料量呈指數級增長。數據量的成長為組織提供了將其數據資產貨幣化的機會。

先進的分析與資料技術：

機器學習和人工智慧等分析技術的發展使組織能夠從數據中獲得有意義的見解。這些見解可以透過多種方式貨幣化，包括提供數據驅動的產品和服務以及客製化廣告。

越來越重視數據貨幣化策略：

公司越來越認識到其數據資產的價值，並積極尋求將其貨幣化的方法。這包括規劃如何向第三方行銷、打包和銷售數據，以及如何透過簡化決策程序來創造價值。

監理環境：

CCPA 和 GDPR 等監管框架提高了人們對資料保護和安全的認識，並迫使組織考慮採用合規的方式來將其資料資產貨幣化。參與數據貨幣化營運的公司必須考慮遵守這些要求。

數據市場提供了購買、銷售和交換數據資產的場所，並且變得越來越受歡迎。透過促進用戶和數據生產者之間的交易，這些市場增加了數據貨幣化生態系統的可近性和流動性。

產業融合與合作關係：

該行業正在日益加強合作並建立合作夥伴關係，以利用彼此的數據資產實現互惠互利。跨產業協作可協助公司創造新的收入來源並開發創造性的數據驅動解決方案。

對個人化體驗的需求：

在個人化體驗方面，客戶對各行業公司的期望越來越高。數據貨幣化使企業能夠利用消費者資訊來創建客製化產品、服務和廣告活動，從而提高客戶的滿意度和忠誠度。

全球資料貨幣化市場的阻礙因素

有幾個因素可能會成為數據貨幣化市場的限制和挑戰。其中包括：

資料隱私問題：

資料隱私問題：由於對資料安全和隱私的擔憂日益增加，尋求將資料貨幣化的組織面臨重大障礙。 CCPA 和 GDPR 等法規對資料控制和權限設定了嚴格限制，這使得企業保持合規性和保護客戶隱私至關重要。

缺乏資料品質與治理：

不良的資料治理和品質可能會阻礙資料貨幣化工作的成功。不準確、不完整或過時的數據可能會產生不可靠的見解和決策，從而對數據貨幣化工作的價值主張產生負面影響。必須建立強而有力的治理和品質框架，以確保資料資產的有效性和可靠性。

資料孤島與碎片：

在組織內部，資料孤島和碎片化使資料貨幣化工作變得複雜。多樣化的系統和資料來源阻礙了資料整合和互通性，使得難以獲得有價值的見解並最大化資料資產的價值。為了最大限度地提高資料貨幣化專案的價值，您需要打破組織界限並培養資料共享和協作的文化。

缺乏知識與經驗：

許多公司沒有意識到其數據資產的潛在價值，並且缺乏成功地將其數據資產貨幣化所需的知識和經驗。為了克服這一障礙，利害關係人必須瞭解數據貨幣化的好處，並獲得支援和培訓以培養數據分析技能。

獲利策略複雜性：

制定和實施可獲利的數據貨幣化計劃需要大量的努力和資源。公司必須管理許多問題，例如目標市場選擇、定價策略、分銷路線和有價值的數據資產。在製定和實施貨幣化策略方面缺乏清晰度和經驗可能會阻礙數據貨幣化市場的成功。

競爭格局：

在競爭激烈的數據貨幣化產業，許多公司都在爭奪市場佔有率。新創公司、數據經紀人和成熟的科技公司都在爭取數據貨幣化機會。在這樣的競爭環境下，企業可能很難在競爭中脫穎而出並獲得市場佔有率。

道德與社會問題：

數據的適當使用及其對人類和社會的潛在影響提出了數據貨幣化帶來的道德和社會問題。如果資料貨幣化過程不以道德和透明的方式進行，則可能會出現偏見、歧視和資料利用等問題。解決這些問題並維護道德標準對於建立信譽並培養對數據貨幣化行業的信任是必要的。

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.