PUBLISHER: Stratistics Market Research Consulting | PRODUCT CODE: 1250707
PUBLISHER: Stratistics Market Research Consulting | PRODUCT CODE: 1250707
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.
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.
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.
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.
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.
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Benchmarking of key players based on product portfolio, geographical presence, and strategic alliances
Note: Tables for North America, Europe, APAC, South America, and Middle East & Africa Regions are also represented in the same manner as above.