Data Monetization Market

Data Monetization Market

Data Monetization Market Forecasts to 2028 - Global Analysis By Type (Direct Data Monetization and Indirect Data Monetization), By Component (Services, Tools and Software), By Data Type (Supplier Data, Product Data, Customer Data and Financial Data), By Business Function, By Deployment Type, By Organization Size, By Method, By End User and By Geography

4.4 (78 reviews)
4.4 (78 reviews)
Published: 2023 ID: SMRC22584

This report covers the impact of COVID-19 on this global market

Years Covered


Estimated Year Value (2022)

US $2.90 BN

Projected Year Value (2028)

US $9.33 BN

CAGR (2022 - 2028)


Regions Covered

North America, Europe, Asia Pacific, South America, and Middle East & Africa

Countries Covered

US, Canada, Mexico, Germany, UK, Italy, France, Spain, Japan, China, India, Australia, New Zealand, South Korea, Rest of Asia Pacific, South America, Argentina, Brazil, Chile, Middle East & Africa, Saudi Arabia, UAE, Qatar, and South Africa

Largest Market

Asia Pacific

Highest Growing Market

Asia Pacific

According to Stratistics MRC, the Global Data Monetization Market is accounted for $2.90 billion in 2022 and is expected to reach $9.33 billion by 2028 growing at a CAGR of 21.5% during the forecast period. Data monetization is the method used to transform a vast volume of unstructured, unused company data into insightful knowledge that can then be exchanged for money or services. By investing in analytics platforms that transform unstructured data into useful insights based on requirements, businesses can lower the cost of their business processes and enhance income streams.

According to Philips, as of February 2022, 92% of healthcare leaders surveyed in Singapore declared they had already implemented or had been in the process of adopting predictive analytics in their healthcare organizations.

Market Dynamics:


Growth in the adoption of data-driven decision-making

Data is being used by organizations to make important decisions. Prior to the use of Business Intelligence (BI) software and tools, data analysis decisions were based on conventional methods like intuitions, hunches, or opinions. However, organizations have begun to realize that these methods improve profitability and can be used to make better strategic decisions. Several businesses are adopting BI; for instance, data-driven decision-making among US manufacturers increased threefold between 2005 and 2010, according to U.S. Central Bureau Surveys.


Lack of 0rganizational capabilities and cultural barriers

The main hindrances to big data exploitation are organizational capabilities and culture. The use of data monetization tools is predicted to be hampered by obstacles such as a lack of adequate roles and responsibilities, ineffective organizational processes, a lack of management focus and support, and a lack of procedures and quality measurements. Data monetization necessitates a certain set of procedures, tools, and abilities, but most significantly, it needs a culture that is conducive to the development of novel products. As data monetization is all about developing a new line of business, having a clear business strategy, an effective business unit leader, and a capable staff are crucial.


Rising adoption of AI for data processing

Organizations have been forced to adopt new technologies like AI, IoT, machine learning, and deep learning due to the production of enormous amounts of data and the requirement to evaluate this data in real-time. Since BI technologies are extremely helpful in gathering and analyzing enormous volumes of data, organizations are concentrating on adopting them. Solutions for data monetization can process huge quantities of data and assist in gaining useful insights from the information at hand. For instance, many companies utilize BI tools to analyze their products, services, and customer behavior patterns using a wealth of data. These tools are also used to analyze big data sets and derive analytical insights that can be used to market opportunities and develop company strategies.


Increase in complexities in data structures

Data quality is one of the key considerations for monetizing data, which is becoming more widespread across industries and offering new business opportunities. Organizations can determine this correctly owing to precise data. Data quality may be lowered as a result of industry-specific data sharing and the integration of data products into existing systems. False facts and inconsistencies could be the result of poor data quality. The ability of companies to make wise decisions is therefore directly impacted by adequate data quality. Without quality, information is inefficient and can have unexpected consequences. As a result, it is anticipated that the quality of the data obtained by organizations will make it difficult to use data monetization solutions, which will restrict the development of data monetization vendors.

Covid-19 Impact:

Owing to the COVID-19 epidemic, new solutions have evolved that provide their customers with predictive and prescriptive analysis, allowing them to make decisions about cost reduction by simplifying their business processes. Customers receive the most value from this method of data monetization, which also enables product teams to create and deploy actionable analytics apps that can be seamlessly integrated with other software. Enterprises can extract secret information that can add value to the company's data with the use of technologies and services for data monetization. By comprehending customers' purchasing behaviors and patterns, these tools and services also meet the consumers' inherent demands, improving the entire customer experience.

The tools segment is expected to be the largest during the forecast period

During the forecast period, the tools segment is anticipated to hold the largest market share as business applications employ data monetization techniques to improve their functionality and extract insights from the business data, allowing businesses to make wise business decisions. The integration of structured and unstructured data across technologies is made possible by the established features of the data monetization platform. Moreover, the data monetization solution gives data monetization providers the ability to grow their market shares and make more money by improving their capacity to meet the unique requirements of their customers.

The customer data segment is expected to have the highest CAGR during the forecast period

Over the predicted period, the customer data segment commanded the highest growth rate, as crucial consumer data assists businesses in developing their company strategy. With the aid of customer relationship management (CRM) systems, businesses gather client data from advertisements, surveys, social media, and websites. Because of client data, businesses can reinvent themselves and create new revenue streams for their core business. In order to tailor their products and services for their clients, businesses also benefit from understanding the buying patterns of their target market and analyzing their judgments about product design and pricing. For instance, Facebook analyzes user data and sells it to outside companies so that they may display tailored advertisements.

Region with largest share:

Due to the enormous populations of nations like Japan, China, and Australia, the Asia-Pacific region is predicted to have the largest share during the projected period. Hence, in order to manage a vast volume of data, enterprises in these nations are required to implement data monetization at a rapid rate. Moreover, the major factors driving market growth are the expanding usage of digital services like IoT, mobility, AI, cloud, and over-the-top services, as well as the rising investments in technological advancements in the region. However, China generates the most revenue in the region, which is due to the existence of several MSMEs and large companies, the ongoing digitalization of business operations, and the rise in the amount of data generated.

Region with highest CAGR:

Due to the increased usage of cutting-edge technologies like IoT and cloud computing, Asia Pacific is anticipated to experience significant growth opportunities throughout the forecast period. The number of businesses is rising in the Asia-Pacific region. For instance, Singapore is dedicated for more than 200,000 businesses. This is one of the primary causes behind Asia-Pacific's highest growth rate. However, the adoption of data monetization tools in the BFSI, retail, healthcare, and life sciences industry verticals would be accelerated by significant investments in big data and business analytics solutions that would enhance business performance, expose fraud, and maintain a competitive edge in the global economy.

Key players in the market

Some of the key players in Data Monetization market include Accenture plc, ALC, Monetize Solutions, Inc., Adastra Corporation, Optiva, Inc. (Redknee Solutions Inc.), Reltio, Cisco Systems, Inc., SAP SE, Mahindra ComViva , SAS Institute Inc., VIAVI Solutions Inc., Emu Analytics Ltd., Thales Group, Google LLC (Alphabet Inc.), IBM Corporation, Infosys Limited, Ness Technologies Inc, NetScout Systems Inc., Openwave Mobility Inc. (ENEA) and Dawex Systems SAS.

Key Developments:

In September 2022, SAS announced that its Viya analytics platform is available in the Microsoft Azure Marketplace. All features of SAS Viya on Microsoft Azure would equip customers globally with access to data exploration, machine learning, and model deployment analytics. The tool is available in many languages and includes an in-app learning center to support immediate onboarding. With SAS Viya on Microsoft Azure, users would also have access to the complete Viya package, including SAS Visual Analytics, SAS Visual Statistics, SAS Visual Data Mining, Machine Learning, and SAS Model Manager.

In July 2022, Google launched new dimensions and metrics, enabling customers to see bounce rate, additional UTM parameter values, and conversion rate across various surfaces, including explorations, segments, audiences, reports, and the Google Analytics Data API.

In June 2022, The UK Civil Aviation Authority (CAA) aligned Emu Analytics’ digital twin solution, Flo. w, to monitor how UK airspace is utilized and make informed, data-led decisions on its future, accounting for safety, efficiency, and all airspace users.

In January 2022, Optiva, Inc. and Google Cloud entered into a multi-year strategic partnership. The partnership was aimed at aiding telecom operators and service providers to better adopt digital transformation.

In August 2021, Adastra and PaymentComponents announced a partnership through which they plan to offer advanced open banking and payment solutions in the US and Canada. The combined strengths of Adastra and PaymentComponents can offer their customers exclusive solutions that they can take to market effectively and boost the latter’s position as a comprehensive fintech solutions provider in the region.

Types Covered:
• Direct Data Monetization
• Indirect Data Monetization

Components Covered:
• Services
• Tools
• Software

Data Types Covered:
• Supplier Data
• Product Data
• Customer Data
• Financial Data

Business Functions Covered:
• Finance
• Supply Chain Management
• Sales and Marketing
• Operations
• Other Business Functions

Deployment Types Covered:
• On Premises
• Cloud

Organization Sizes Covered:
• Small and Medium Sized Enterprises
• Large Enterprises

Methods Covered:
• Insight as a Service (IaaS)
• Embedded Analytics
• Data as a Service (DaaS)
• Analytics Enabled Platform as a Service (AaaS)

End Users Covered:
• Telecommunication
• Banking, Financial Services and Insurance
• Information Technology
• Consumer Goods and Retail
• Manufacturing
• Healthcare
• Transportation and Logistics
• Media and Entertainment
• Energy and Utilities
• Other End Users

Regions Covered:
• North America
o US
o Canada
o Mexico
• Europe
o Germany
o UK
o Italy
o France
o Spain
o Rest of Europe
• Asia Pacific
o Japan
o China
o India
o Australia
o New Zealand
o South Korea
o Rest of Asia Pacific
• South America
o Argentina
o Brazil
o Chile
o Rest of South America
• Middle East & Africa
o Saudi Arabia
o Qatar
o South Africa
o Rest of Middle East & Africa

What our report offers:
- Market share assessments for the regional and country-level segments
- Strategic recommendations for the new entrants
- Covers Market data for the years 2020, 2021, 2022, 2025, and 2028
- Market Trends (Drivers, Constraints, Opportunities, Threats, Challenges, Investment Opportunities, and recommendations)
- Strategic recommendations in key business segments based on the market estimations
- Competitive landscaping mapping the key common trends
- Company profiling with detailed strategies, financials, and recent developments
- Supply chain trends mapping the latest technological advancements

Free Customization Offerings:
All the customers of this report will be entitled to receive one of the following free customization options:
• Company Profiling
o Comprehensive profiling of additional market players (up to 3)
o SWOT Analysis of key players (up to 3)
• Regional Segmentation
o Market estimations, Forecasts and CAGR of any prominent country as per the client's interest (Note: Depends on feasibility check)
• Competitive Benchmarking
Benchmarking of key players based on product portfolio, geographical presence, and strategic alliances

Table of Contents

1 Executive Summary

2 Preface
2.1 Abstract
2.2 Stake Holders
2.3 Research Scope
2.4 Research Methodology
2.4.1 Data Mining
2.4.2 Data Analysis
2.4.3 Data Validation
2.4.4 Research Approach
2.5 Research Sources
2.5.1 Primary Research Sources
2.5.2 Secondary Research Sources
2.5.3 Assumptions

3 Market Trend Analysis
3.1 Introduction
3.2 Drivers
3.3 Restraints
3.4 Opportunities
3.5 Threats
3.6 End User Analysis
3.7 Emerging Markets
3.8 Impact of Covid-19

4 Porters Five Force Analysis
4.1 Bargaining power of suppliers
4.2 Bargaining power of buyers
4.3 Threat of substitutes
4.4 Threat of new entrants
4.5 Competitive rivalry

5 Global Data Monetization Market, By Type
5.1 Introduction
5.2 Direct Data Monetization
5.3 Indirect Data Monetization

6 Global Data Monetization Market, By Component
6.1 Introduction
6.2 Services
6.2.1 Consulting
6.2.2 Implementation and Integration
6.2.3 Support and Maintenance
6.3 Tools
6.4 Software

7 Global Data Monetization Market, By Data Type
7.1 Introduction
7.2 Supplier Data
7.3 Product Data
7.4 Customer Data
7.5 Financial Data

8 Global Data Monetization Market, By Business Function
8.1 Introduction
8.2 Finance
8.3 Supply Chain Management
8.4 Sales and Marketing
8.5 Operations
8.6 Other Business Functions

9 Global Data Monetization Market, By Deployment Type
9.1 Introduction
9.2 On Premises
9.3 Cloud

10 Global Data Monetization Market, By Organization Size
10.1 Introduction
10.2 Small and Medium Sized Enterprises
10.3 Large Enterprises

11 Global Data Monetization Market, By Method
11.1 Introduction
11.2 Insight as a Service (IaaS)
11.3 Embedded Analytics
11.4 Data as a Service (DaaS)
11.5 Analytics Enabled Platform as a Service (AaaS)

12 Global Data Monetization Market, By End User
12.1 Introduction
12.2 Telecommunication
12.3 Banking, Financial Services and Insurance
12.4 Information Technology
12.5 Consumer Goods and Retail
12.6 Manufacturing
12.7 Healthcare
12.8 Transportation and Logistics
12.9 Media and Entertainment
12.10 Enegy and Utilities
12.11 Other End Users

13 Global Data Monetization Market, By Geography
13.1 Introduction
13.2 North America
13.2.1 US
13.2.2 Canada
13.2.3 Mexico
13.3 Europe
13.3.1 Germany
13.3.2 UK
13.3.3 Italy
13.3.4 France
13.3.5 Spain
13.3.6 Rest of Europe
13.4 Asia Pacific
13.4.1 Japan
13.4.2 China
13.4.3 India
13.4.4 Australia
13.4.5 New Zealand
13.4.6 South Korea
13.4.7 Rest of Asia Pacific
13.5 South America
13.5.1 Argentina
13.5.2 Brazil
13.5.3 Chile
13.5.4 Rest of South America
13.6 Middle East & Africa
13.6.1 Saudi Arabia
13.6.2 UAE
13.6.3 Qatar
13.6.4 South Africa
13.6.5 Rest of Middle East & Africa

14 Key Developments
14.1 Agreements, Partnerships, Collaborations and Joint Ventures
14.2 Acquisitions & Mergers
14.3 New Product Launch
14.4 Expansions
14.5 Other Key Strategies

15 Company Profiling
15.1 Accenture plc
15.2 ALC
15.3 Monetize Solutions, Inc.
15.4 Adastra Corporation
15.5 Optiva, Inc. (Redknee Solutions Inc.)
15.6 Reltio
15.7 Cisco Systems, Inc.
15.8 SAP SE
15.9 Mahindra ComViva
15.10 SAS Institute Inc.
15.11 VIAVI Solutions Inc.
15.12 Emu Analytics Ltd.
15.13 Thales Group
15.14 Google LLC (Alphabet Inc.)
15.15 IBM Corporation
15.16 Infosys Limited
15.17 Ness Technologies Inc
15.18 NetScout Systems Inc.
15.19 Openwave Mobility Inc. (ENEA)
15.20 Dawex Systems SAS

List of Tables
1 Global Data Monetization Market Outlook, By Region (2020-2028) ($MN)
2 Global Data Monetization Market Outlook, By Type (2020-2028) ($MN)
3 Global Data Monetization Market Outlook, By Direct Data Monetization (2020-2028) ($MN)
4 Global Data Monetization Market Outlook, By Indirect Data Monetization (2020-2028) ($MN)
5 Global Data Monetization Market Outlook, By Component (2020-2028) ($MN)
6 Global Data Monetization Market Outlook, By Services (2020-2028) ($MN)
7 Global Data Monetization Market Outlook, By Consulting (2020-2028) ($MN)
8 Global Data Monetization Market Outlook, By Implementation and Integration (2020-2028) ($MN)
9 Global Data Monetization Market Outlook, By Support and Maintenance (2020-2028) ($MN)
10 Global Data Monetization Market Outlook, By Tools (2020-2028) ($MN)
11 Global Data Monetization Market Outlook, By Software (2020-2028) ($MN)
12 Global Data Monetization Market Outlook, By Data Type (2020-2028) ($MN)
13 Global Data Monetization Market Outlook, By Supplier Data (2020-2028) ($MN)
14 Global Data Monetization Market Outlook, By Product Data (2020-2028) ($MN)
15 Global Data Monetization Market Outlook, By Customer Data (2020-2028) ($MN)
16 Global Data Monetization Market Outlook, By Financial Data (2020-2028) ($MN)
17 Global Data Monetization Market Outlook, By Business Function (2020-2028) ($MN)
18 Global Data Monetization Market Outlook, By Finance (2020-2028) ($MN)
19 Global Data Monetization Market Outlook, By Supply Chain Management (2020-2028) ($MN)
20 Global Data Monetization Market Outlook, By Sales and Marketing (2020-2028) ($MN)
21 Global Data Monetization Market Outlook, By Operations (2020-2028) ($MN)
22 Global Data Monetization Market Outlook, By Other Business Functions (2020-2028) ($MN)
23 Global Data Monetization Market Outlook, By Deployment Type (2020-2028) ($MN)
24 Global Data Monetization Market Outlook, By On Premises (2020-2028) ($MN)
25 Global Data Monetization Market Outlook, By Cloud (2020-2028) ($MN)
26 Global Data Monetization Market Outlook, By Organization Size (2020-2028) ($MN)
27 Global Data Monetization Market Outlook, By Small and Medium Sized Enterprises (2020-2028) ($MN)
28 Global Data Monetization Market Outlook, By Large Enterprises (2020-2028) ($MN)
29 Global Data Monetization Market Outlook, By Method (2020-2028) ($MN)
30 Global Data Monetization Market Outlook, By Insight as a Service (IaaS) (2020-2028) ($MN)
31 Global Data Monetization Market Outlook, By Embedded Analytics (2020-2028) ($MN)
32 Global Data Monetization Market Outlook, By Data as a Service (DaaS) (2020-2028) ($MN)
33 Global Data Monetization Market Outlook, By Analytics Enabled Platform as a Service (AaaS) (2020-2028) ($MN)
34 Global Data Monetization Market Outlook, By End User (2020-2028) ($MN)
35 Global Data Monetization Market Outlook, By Telecommunication (2020-2028) ($MN)
36 Global Data Monetization Market Outlook, By Banking, Financial Services and Insurance (2020-2028) ($MN)
37 Global Data Monetization Market Outlook, By Information Technology (2020-2028) ($MN)
38 Global Data Monetization Market Outlook, By Consumer Goods and Retail (2020-2028) ($MN)
39 Global Data Monetization Market Outlook, By Manufacturing (2020-2028) ($MN)
40 Global Data Monetization Market Outlook, By Healthcare (2020-2028) ($MN)
41 Global Data Monetization Market Outlook, By Transportation and Logistics (2020-2028) ($MN)
42 Global Data Monetization Market Outlook, By Media and Entertainment (2020-2028) ($MN)
43 Global Data Monetization Market Outlook, By Enegy and Utilities (2020-2028) ($MN)
44 Global Data Monetization Market Outlook, By Other End Users (2020-2028) ($MN)

Note: Tables for North America, Europe, APAC, South America, and Middle East & Africa Regions are also represented in the same manner as above.

List of Figures


Research Methodology

We at Stratistics opt for an extensive research approach which involves data mining, data validation, and data analysis. The various research sources include in-house repository, secondary research, competitor’s sources, social media research, client internal data, and primary research.

Our team of analysts prefers the most reliable and authenticated data sources in order to perform the comprehensive literature search. With access to most of the authenticated data bases our team highly considers the best mix of information through various sources to obtain extensive and accurate analysis.

Each report takes an average time of a month and a team of 4 industry analysts. The time may vary depending on the scope and data availability of the desired market report. The various parameters used in the market assessment are standardized in order to enhance the data accuracy.

Data Mining

The data is collected from several authenticated, reliable, paid and unpaid sources and is filtered depending on the scope & objective of the research. Our reports repository acts as an added advantage in this procedure. Data gathering from the raw material suppliers, distributors and the manufacturers is performed on a regular basis, this helps in the comprehensive understanding of the products value chain. Apart from the above mentioned sources the data is also collected from the industry consultants to ensure the objective of the study is in the right direction.

Market trends such as technological advancements, regulatory affairs, market dynamics (Drivers, Restraints, Opportunities and Challenges) are obtained from scientific journals, market related national & international associations and organizations.

Data Analysis

From the data that is collected depending on the scope & objective of the research the data is subjected for the analysis. The critical steps that we follow for the data analysis include:

  • Product Lifecycle Analysis
  • Competitor analysis
  • Risk analysis
  • Porters Analysis
  • PESTEL Analysis
  • SWOT Analysis

The data engineering is performed by the core industry experts considering both the Marketing Mix Modeling and the Demand Forecasting. The marketing mix modeling makes use of multiple-regression techniques to predict the optimal mix of marketing variables. Regression factor is based on a number of variables and how they relate to an outcome such as sales or profits.

Data Validation

The data validation is performed by the exhaustive primary research from the expert interviews. This includes telephonic interviews, focus groups, face to face interviews, and questionnaires to validate our research from all aspects. The industry experts we approach come from the leading firms, involved in the supply chain ranging from the suppliers, distributors to the manufacturers and consumers so as to ensure an unbiased analysis.

We are in touch with more than 15,000 industry experts with the right mix of consultants, CEO's, presidents, vice presidents, managers, experts from both supply side and demand side, executives and so on.

The data validation involves the primary research from the industry experts belonging to:

  • Leading Companies
  • Suppliers & Distributors
  • Manufacturers
  • Consumers
  • Industry/Strategic Consultants

Apart from the data validation the primary research also helps in performing the fill gap research, i.e. providing solutions for the unmet needs of the research which helps in enhancing the reports quality.

For more details about research methodology, kindly write to us at

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