The big data market will show three major development trends in the future.

The big data industry chain is generally complete, but the degree of competition varies significantly in certain segments. In the data acquisition segment, the comprehensive big data source market is in the structured integration stage, while the vertical big data source market is in the layout stage. The data storage and data mining segment has a stable market structure, dominated by international giants, forming an oligopoly that domestic companies will find difficult to surpass in the short term. The data application segment presents an opportunity for domestic companies, but the technology is still immature.

With the rapid development of mobile internet, the Internet of Things, and other technologies, new data sources are constantly emerging. The continuous growth of China's total data volume makes big data an important resource, conducive to driving business innovation in various fields such as retail, tourism, healthcare, finance, telecommunications, and government public services.

IT giants and vendors with single big data businesses in the offline big data market (the market for big data applications and big data platform businesses of IT companies) are beginning to take action, optimizing their product and service roadmaps. The online big data market (the internet user data market and the online financial market, mainly internet finance) is gradually maturing, with online big data applications centered on finance and retail maturing, further expanding the market size. Enterprises are focusing on cultivating data assets and actively exploring data monetization, with industry big data tending to be more concentrated rather than integrated. The big data industry chain is generally well-structured, but the level of competition varies significantly across different segments. In the data acquisition segment, the comprehensive big data source market is in a phase of structural integration, while the vertical big data source market is in the deployment phase. The data storage and data mining segments have a stable market structure, dominated by international giants, forming an oligopoly that domestic companies will find difficult to surpass in the short term. The data application segment presents an opportunity for domestic companies, but the technology remains immature.

These segments of the industry chain are influencing the transformation of business models for companies.

Model 1: Utilizing storage capacity for operation to meet the massive information storage needs of enterprises and individuals;

Model 2: Predicting the behavior of relevant entities after data mining and analysis to conduct business;

Model 3: Directly renting or selling information or providing an information rental platform;

Model 4: IT service providers offer big data space rental models, starting with simple file storage and gradually

expanding to data aggregation platforms by renting out virtual space;

Model 5: Providing solutions for specific operational processes or business problems tailored to enterprise needs, implementing single-point technologies, such as providing big data analytics technology to retailers to obtain marketing ideas;

Model 6: Providing overall solutions for enterprise system needs;

Model 7: BDaaS (Big Data as a Service), a data application as a service model, providing online big data technology or solutions through cloud services.

According to Analysys's 2014 China Big Data Market Industry Investment Structure Data, finance, telecommunications, and retail were the top three industries, accounting for 16.0%, 15.6%, and 13.9% of investment, respectively. Government, healthcare, and tourism investments accounted for 12.7%, 9.0%, and 4.1% respectively. These six major industries collectively accounted for 71.3% of the total. Other industries, including education, manufacturing, energy, media, and the internet, accounted for 28.7% cumulatively. A big data industry cluster is gradually forming, meaning that for enterprises, this cluster relies on cloud-based big data aggregation and industry cloud services as a platform to share core competitiveness.

The big data market will exhibit the following development trends in the future:

Firstly, the data ecosystem will become increasingly complex. The world of big data is not just a single, massive computer network, but an ecosystem composed of numerous active components and diverse participants. This includes terminal equipment providers, infrastructure providers, network service providers, network access service providers, data service enablers, data service providers, touchpoint services, data service retailers, and many other participants. Currently, the basic framework of such a data ecosystem has been formed. Future development will tend towards the segmentation of roles within the system (market segmentation); adjustments to system mechanisms (business model innovation); and adjustments to system structure (adjustments to the competitive environment), thereby gradually increasing the complexity of the data ecosystem.

Secondly, data management has become a core competency, directly impacting financial performance. Once the concept that "data assets are core corporate assets" takes root, companies have a clearer definition of data management, making it a core competency for continuous development. Strategic planning and utilization of data assets has become central to corporate data management. Data asset management efficiency is significantly positively correlated with the growth rate of main business revenue and sales revenue. Furthermore, for companies with an internet-centric mindset, data asset competitiveness accounts for 36.8%, and the effectiveness of data asset management directly affects the company's financial performance.

Thirdly, those who control core industry elements dominate the data ecosystem. The core links in the data ecosystem are the core elements of the industry, such as payment, logistics, and information (credit) in e-commerce. Companies that control these core industry elements, seizing the opportunities of the big data era, can further unleash their core competitive advantages, utilize internet thinking, and control the main ecological chain of the data ecosystem through big data on core industry elements, ultimately achieving another leap forward in the digital economy era.

Further, by leveraging data to drive operations and marketing, retail enterprises will optimize management with members at the core. Through data-driven, people-centric approaches, they can achieve optimized decision-making and precise marketing. The industry will explore increasingly more new big data marketing models, and various retail enterprises will actively try new opportunities, such as micro-stores, to identify new trends in consumer preferences. Continuously enriching external data sources, while improving their own offline data collection capabilities, will rapidly enhance marketing accuracy through cooperation with richer external data sources, including authoritative market research institutions and leading internet giants.

The application of tourism big data aims to quickly establish unified data standards, unified data exchange standards, and integrated regional and national tourism data. This will achieve unified big data across three screens (tourism monitoring screens, scenic area announcement screens, and tourist mobile screens). By utilizing EHRs data from healthcare services, hospital and medical insurance settlement and expense data, academic, social, and governmental data from medical research, pharmaceutical, medical device, and clinical trial data from medical manufacturers, resident behavior and health management data, government population and public health data, and network-generated data from public socio-economic life, precise data support is provided for drug development, disease treatment, public health management, resident health management, and health risk factor analysis in the medical industry.

Under traditional financial operation models, financial institutions lack the ability to assess consumers' creditworthiness, spending power, and willingness to consume, leading to excessively high pricing of products and services in some financial sectors and leaving some areas as surplus markets, which still falls short of actual financial requirements. Big data will help promote data aggregation in the financial and banking industries, mine value based on overall industry data, drive industry development, and promote business model innovation. The main value of big data applications in the financial industry currently lies in financial risk management, consumer intelligence, and intelligent operations.

Telecommunications companies are transitioning from the traditional data era to the big data era. Due to the needs of telecommunications companies' production and operations, their own production management systems already possess massive, customer-centric, unified view data resources. Big data can improve network service quality and enhance pipeline intelligence for the telecommunications industry; provide more accurate insights into customer needs and enhance market competitiveness; upgrade industry IT solutions and increase customer value; and provide data security services, establishing a differentiated competitive advantage in the big data market.

Big data is not merely a massive amount of data and its corresponding processing technologies, but also a way of thinking, a crucial infrastructure, and a fundamental social system influencing the operation of the entire nation and society. It is a powerful tool for addressing "urban ills" such as traffic congestion, smog, difficulty accessing healthcare, and food safety. Furthermore, it will open a policy window for governments to understand public opinion, creating a platform-based government, a service-oriented government, and an open government—a smart government. Its application value includes: strengthening overall planning and optimizing the big data formation mechanism; enhancing data collection and information perception to improve the perception level of smart cities; and promoting big data applications to improve the level of economic and social intelligence.