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 B

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

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

According tPhilips, 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:

Driver:

Growth in the adoption of data-driven decision-making

Data is being used by organizations tmake important decisions. Prior tthe 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 trealize that these methods improve profitability and can be used tmake better strategic decisions. Several businesses are adopting BI; for instance, data-driven decision-making among US manufacturers increased threefold between 2005 and 2010, according tU.S. Central Bureau Surveys.

Restraint:

Lack of 0rganizational capabilities and cultural barriers

The main hindrances tbig data exploitation are organizational capabilities and culture. The use of data monetization tools is predicted tbe 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 tthe 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.

Opportunity:

Rising adoption of AI for data processing

Organizations have been forced tadopt new technologies like AI, IoT, machine learning, and deep learning due tthe production of enormous amounts of data and the requirement tevaluate 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 tanalyze their products, services, and customer behavior patterns using a wealth of data. These tools are alsused tanalyze big data sets and derive analytical insights that can be used tmarket opportunities and develop company strategies.

Threat:

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 tprecise data. Data quality may be lowered as a result of industry-specific data sharing and the integration of data products intexisting systems. False facts and inconsistencies could be the result of poor data quality. The ability of companies tmake 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 tuse data monetization solutions, which will restrict the development of data monetization vendors.

Covid-19 Impact:

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

The tools segment is expected tbe the largest during the forecast period

During the forecast period, the tools segment is anticipated thold the largest market share as business applications employ data monetization techniques timprove their functionality and extract insights from the business data, allowing businesses tmake 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 tgrow their market shares and make more money by improving their capacity tmeet the unique requirements of their customers.

The customer data segment is expected thave 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 ttailor their products and services for their clients, businesses alsbenefit 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 toutside companies sthat they may display tailored advertisements.

Region with largest share:

Due tthe enormous populations of nations like Japan, China, and Australia, the Asia-Pacific region is predicted thave the largest share during the projected period. Hence, in order tmanage a vast volume of data, enterprises in these nations are required timplement 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 tthe 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 tthe increased usage of cutting-edge technologies like IoT and cloud computing, Asia Pacific is anticipated texperience 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, CiscSystems, 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 tdata exploration, machine learning, and model deployment analytics. The tool is available in many languages and includes an in-app learning center tsupport immediate onboarding. With SAS Viya on Microsoft Azure, users would alshave access tthe 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 tsee 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, tmonitor 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 inta multi-year strategic partnership. The partnership was aimed at aiding telecom operators and service providers tbetter adopt digital transformation.

In August 2021, Adastra and PaymentComponents announced a partnership through which they plan toffer 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 tmarket 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
US
Canada
Mexico
• Europe
Germany
UK
Italy
France
Spain
Rest of Europe
• Asia Pacific
Japan
China
India
Australia
New Zealand
South Korea
Rest of Asia Pacific
• South America
Argentina
Brazil
Chile
Rest of South America
• Middle East & Africa
Saudi Arabia
UAE
Qatar
South Africa
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

Benchmarking of key players based on product portfolio, geographical presence, and strategic alliances