Frost & Sullivan, in collaboration with LeadLeo Research Institute, released the '2023 China Data Management Solutions Market Report'

With the continuous optimization of data management technology in recent years, coupled with national policies that have increasingly strengthened their focus on the development of the data field, Chinese enterprises have strengthened the implementation of data management technology practices, and the level of data management is gradually improving. However, enterprises face the problem of not fully realizing the value of their data after implementing the technology. This mainly stems from the need for enterprises to iterate with their existing IT and organizational structures during the digital transformation process. Blind pursuit of new technologies without considering the inherent attributes of the enterprise can lead to the inability to connect the value of data with the company's business development. For example, technical complexity can exacerbate data silos, and technical complexity can increase communication costs between data teams and business teams.

Against this backdrop, we have observed thatThe supplier's technology trends revolve around two main themes: efficiency improvement and cost reduction, as well as data security. By integrating technologies and implementing methodologies, we enhance the performance effectiveness of solutions and their implementation in specific scenarios while reducing costs. We also enrich our data security features to facilitate data circulation.These layouts will enhance the alignment between data management solutions and the sustainable development of an enterprise's data management capabilities, improving the implementation effectiveness of the solutions.

The early implementation of automation and intelligence mainly relied on preset rules and models, which could alleviate the pressure on data management teams to a certain extent. However, the achievable functions and scope of impact were still limited. With the advancement of AI technology, intelligent functions that can be implemented have been expanded, such as predictive functions, adaptive functions, natural language understanding-related functions, etc. These functions not only enable enterprises to further reduce team pressure, better conduct data insights, lower data management and operation costs, but also lower the threshold for data use and expand the range of data users. Thus, they help enterprises solve both technical implementation and organizational collaboration issues simultaneously, making data usable, easy to use, and empowering enterprise business development through data.

Technologies such as data lakes, lakehouse integration, and HTAP can solve problems brought about by business development. Therefore, they have been implemented and adopted in recent years. The potential value of these technologies is significant, but they have not yet been fully realized. The main resistance comes from the need to build new systems with existing IT architecture and organizational structures. Common issues include: the collaboration between new technologies and existing IT architectures requires a certain learning cost, and after technology implementation, operational pressure does not decrease but increases; technology implementation increases system complexity, but data usage processes are not updated or are difficult to update, making it difficult to achieve technology implementation.

To address these issues, suppliers offer cloud-native solutions combined with methodologies, providing impetus for the release of technical value. For instance, cloud-native capabilities can make solutions lighter and achieve higher performance ceilings, reducing operational pressure while improving performance; methodologies provide guidance from a holistic perspective for building new systems, thereby promoting optimization of data management efficiency and organizational collaboration efficiency. Based on these choices, enterprises will gradually eliminate resistance and fully utilize the effectiveness of technology.

In recent years, China has successively introduced bills to promote the marketization of data elements, that is, to facilitate the external flow of data within enterprises. Enterprises are also gradually paying more attention to data-driven business development. How data can flow efficiently and securely within enterprises has become a key issue that enterprises need to consider when building their data management systems. At the same time, the flow of data needs to comply with laws and regulations such as the Data Security Law and the Personal Information Protection Law. Especially when dealing with personal sensitive data and industry-critical data, safety and compliance risks need to be considered. If relevant data are leaked, stolen, maliciously tampered with, etc., during the flow process, it will cause losses to personal and national interests. Driven by compliance, enterprises' awareness of data security has been improved. However, due to insufficient technical reserves or choosing to avoid risks, enterprises may fail to ensure the secure flow of data.

In response to industry pain points, suppliers have begun building platform-based data management services that integrate data security technology, lowering the technical threshold. Trusted and privacy-computing technologies provide an environment where 'data is available but not visible' and 'data is controllable and measurable', making it possible for data consumers and producers to conduct secure and compliant data circulation. However, the construction of this technology has high thresholds and significant investment, and not all enterprises have the capability to build it. Therefore, data management solution providers are starting to build secure platform-based data management services to reduce the burden on enterprises, such as Google Cloud Dataplex, Amazon DataZone, DataWorks, and the StarRing Technology data circulation operation PaaS platform. As technology becomes more accessible, secure and compliant, efficient data circulation will gradually be realized, and the true value of data will be released.

Data management solutions are evolving towards intelligence and easier flexible combination and deployment to meet the needs of enterprise scenarios, achieve efficiency improvements and cost reductions. This cannot be achieved without the support of cloud service resources provided by cloud service providers. Recently, leading Chinese cloud vendors have initiated a wave of price cuts in cloud services, which can play a role in strengthening the cooperation enthusiasm of ecosystem partners and increasing enterprises' willingness to build cloud services. This will enhance the promotion of cloud-based technologies and facilitate the expansion of the market scale for data management solutions. The price cut in cloud services can directly reduce enterprises' cost investment in cloud services, thereby lowering the resource threshold for using new technologies and enabling the popularization and promotion of new cloud-based technologies such as AI and privacy computing. At the same time, the price cut in cloud services can reduce the purchasing costs of partners, enhancing their enthusiasm to promote cloud services in various industries and further expanding the market scale for technology implementation.

At the same time, solution providers have also strengthened the matching between solutions and scenarios by accumulating industry scenario understanding. They aim to solve users' real pain points through the following main methods: based on the accumulation of industry practice experience, they build product matrices that can be efficiently linked, reducing performance loss and complexity in multi-product collaboration. Customers perceive them as integrated platforms, which can not only meet flexible industry needs through product combinations but also provide high-performance solutions for efficient implementation of industry scenario solutions; based on a deep understanding of industry scenarios, they form industry theme libraries or industry-specific solutions that can be directly implemented in specific scenarios, allowing for direct matching with enterprise needs and implementation, thereby reducing the burden of digital transformation for enterprises.

This report measures the competitive strength of outstanding manufacturers in the industry through two main dimensions: market growth index and innovation index. The growth index assesses the competitiveness of competitors in the dimension of data management solution growth. The higher up the position, the stronger the market growth capabilities and levels in terms of performance and compatibility, security assurance, service support, industrial chain ecosystem, and commercial implementation maturity of data management solutions; the innovation index measures the competitiveness of competitors in the dimension of data management solution innovation. The rightmost position indicates stronger capabilities and levels in areas such as data warehouse and data lake technology improvements, cloud-native innovation, lake-cum-hive integration innovation, and methodological innovation.

Frost & Sullivan, in collaboration with LeadLeo, conducts a multi-factor hierarchical assessment of the competitiveness of China's data management solution market based on two major evaluation dimensions: growth index and innovation index. This assessment involves nine major indicators including improvements in data warehouse and data foundation technologies, cloud-native innovation, lakehouse integration innovation, methodology innovation, performance and compatibility, security assurance capabilities, service support levels, ecological construction capabilities, and business implementation maturity.Based on the comprehensive scores of the 'Innovation Index' and 'Growth Index', Amazon Web Services, Alibaba Cloud, Tencent Cloud, Huawei Cloud, and Inspur Cloud are positioned at the leading tier in the Chinese data management solution market.

Amazon Web Services:Amazon Web Services (AWS) is focusing on its cloud-native data strategy, with all data analysis services now Serverless. This helps enterprises achieve efficiency and cost reduction through the integration of Ops-free services, while also enabling them to adapt more quickly to changes in business scale, logic, and requirements. In addition, AWS has proposed the vision of building Zero ETL. By enhancing the performance of product interconnections and combining intelligent services provided by automation and machine learning, the solution is optimized to improve the user experience and effectiveness at the endpoint.

Alibaba Cloud:Alibaba Cloud DataWorks is deeply integrated with computing engines such as Maxcompute and Hologres, providing a unified full-link big data development and governance platform for solutions such as data warehouses/data lakes/holographic databases. Based on the practical products of data governance experience accumulated by Alibaba Group over the years, DataWorks combines intelligent and visual functions to reduce the burden of enterprise data development and operation and maintenance. At the same time, it provides capabilities for data security and governance, helping enterprises optimize their data management capabilities and accelerate digital transformation.

Tencent Cloud:Tencent Cloud actively embraces open-source technologies and persists in implementing technological innovation strategies. For instance, its self-developed massive data integration framework Inlong has become a top-level project of Apache. Based on this project, DataInLong was incubated, supporting data collection, aggregation, storage, and sorting processing through its trillion-level data access and processing capabilities relying on Inlong. Tencent Cloud is also continuously strengthening cloud-native capabilities, such as developing the "Fengluan" system to optimize the cloud-native scheduling platform's rate. Currently, Tencent Cloud has formed scenario-based capabilities in multiple industry fields.

Huawei Cloud:Huawei Cloud DataArts Studio, combined with DAYU data governance methodology, helps customers continuously improve their data management systems. Currently, DataArts Studio has formed thematic libraries for multiple industries and provides full-process data management guidance to enterprises by integrating with the intelligent data lake FusionInsight service, promoting efficient implementation of data management solutions on the enterprise side. In addition, Huawei Cloud actively embraces cloud-native technologies, launching a Serverless cloud-native architecture version of GaussDB (DWS), connecting data warehouses with AI production lines, and improving performance in aspects such as query and metadata management for lake-hive integration.

Inspur Cloud:In the face of a market environment centered on data elements, Inspur Cloud has launched the Data Cloud, constructing a distributed data cloud system centered around data and supported by 'cloud-network-edge-end'. It has built a trusted data space and released a business strategy for the Data Cloud to promote the implementation and coverage of Inspur Data Cloud in 100 cities. At the same time, Inspur Cloud is also actively strengthening its digital intelligence collaboration capabilities, integrating AI capabilities into various data management scenarios such as data standard intelligent recommendation and cross-domain data association and mutual recognition.