세계의 데이터 수익화 시장 규모 : 데이터 유형별, 수익화 방법별, 업계별, 지역별, 범위 및 예측

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