Frost & Sullivan in collaboration with LeadLeo releases the '2024 China Data Management Solutions Market Report'

Under the wave of the in-depth development of the digital economy and the rapid advancement of artificial intelligence, the focus of data management solution market development will shift towards the coordination between data management technology and artificial intelligence technology, as well as promoting the application and development of data elements. Specifically:

• Collaboration between data management and AI technology: Data+AI or digital intelligence integration has become the core direction for product iteration and optimization in data management solutions. Both data providers and owners are increasingly emphasizing 'AI-ready' data capabilities to ensure that data meets the needs of AI applications in terms of quality, format, annotation, etc. At the same time, both parties are actively exploring how to use AI technology to optimize data management processes, improve the efficiency and accuracy of data processing, and reduce the cost and errors of manual intervention. For example, by automating data classification, cleaning, and annotation through machine learning, the availability and analytical value of data can be enhanced.

   

• Promote the application and development of data elements: The country is actively promoting the development of data elements, covering explorations in areas such as data assets and data transactions. By introducing policies, encouraging enterprises to practice, and promoting the implementation of the Data Management Maturity Model (DCMM), measures are taken to enhance enterprises' emphasis on data management capabilities. The actual value of data is gradually being materialized, including its practical application value for businesses and individuals. For example, which data is 'useful', and which data has commercial potential to directly generate business benefits for enterprises.

Against this backdrop, the requirements for data management capabilities are no longer limited to data storage, computation, and application. There is also a need to pay further attention to the real-time processing capability of data, responding promptly to external changes. At the same time, enterprises also need to focus on the technical foundation that can enable cross-domain circulation in the future, including data sharing between different departments and systems within the enterprise, as well as efficient data circulation with external partners or markets.

The core goal of enterprises constructing data management solutions is to fully tap into the value of data through efficient storage, processing, analysis of internal historical and newly generated data, as well as external acquired data. This is aimed at optimizing internal management and business efficiency, and enhancing the profitability of enterprises. To clarify the shortcomings and optimization directions of internal data management solutions within an enterprise, it is essential to clearly recognize the stage at which the current built capabilities have reached in terms of unlocking data value, thereby improving resource investment efficiency and iterating towards capability evolution.

The release of data value can be divided into three stages: business insights with a progressive relationship, decision optimization, and circulation empowerment. In the first two stages, enterprises should gradually build a complete foundation in data management and governance technology, optimize processes, and cultivate a data culture. At the same time, by continuously accumulating data volume and enhancing understanding of data, a solid foundation is laid for the circulation empowerment stage. The circulation empowerment stage further improves the capabilities of decision optimization and business insights by aggregating more valuable data.

In the era of big data, development is rapid, but many enterprises that have lagged behind in digital transformation have not kept up with the trend of the times. They are still at the business insight stage and believe that this is all there is to data value at the cognitive level. Their investment in iterative data management capabilities is unclear, making it difficult for them to progress to later stages.

Entering an era that emphasizes intelligence and artificial intelligence, the attributes of data such as quality and type receive greater attention. Enterprises with better attribute datasets will be able to better create differentiation and maintain competitive advantages in the market. This has begun to prompt enterprises to rethink what data they possess, how to improve data quality, and what unknown uses data can have. In the exploration of these questions, the direction for building enterprise data management capabilities will become clearer and clearer. Frost & Sullivan believes that enterprises should build the ability to sustainably unlock the value of data around the following three aspects:

• Data precipitation: By building a complete data management infrastructure, including data warehouses, data lakes, and lakehouse integrations, the data of various business departments is connected. Gradually, a data governance framework or data governance tools are established to enable the discovery and accumulation of data, as well as its understanding and application by business personnel.

   

• Digital Intelligence Integration: 1) Data4AI Capability Building: Focus on enhancing the support capabilities for massive multi-source heterogeneous data storage, query, and processing within data management infrastructure, further optimizing data governance capabilities, thereby improving data quality and maintaining data order management. It is also necessary to build capabilities that support AI computing paradigms, such as vector data processing engines. 2) AI4Data Capability Building: Gradually introduce AI capabilities throughout the entire data management process (integration, processing and storage, analysis, governance), such as intelligent tuning, intelligent data cleaning, and annotation, to reduce the manual operation and maintenance pressure and limitations of data management solutions and improve the efficiency and effectiveness of data processing.

   

• Data Governance: Strengthen investment and attention to data governance, including data quality management, metadata management, data security and compliance management, data sharing and exchange mechanisms, etc., to lay the foundation for data assetization and productization. Data assetization and productization are important ways to realize the materialization of data value.

When small and medium-sized enterprises (SMEs) choose data management solution technologies, they consider whether the amount of data is not large enough to justify investing in building a data management system. Considering costs, they often only focus on building data infrastructure to ensure digital transformation can be carried out, ensuring that data can be stored, i.e., staying at the business insight stage.

"Big data" is a relative concept, as the business scale, industry attributes, and development strategies of different enterprises vary, leading to differences in the amount and type of data required. Large enterprises possess more resources to acquire and utilize data (due to their broader business types), making "big data" a vast and complex dataset for them. This massive dataset and resource allocation method are difficult to replicate in small and medium-sized enterprises, but this does not mean that there is no "big data."

Frost & Sullivan believes that the enterprise's internal 'big data' should be able to fully support the construction and optimization of various data application scenarios, providing high-quality, demand-aligned, and comprehensive data to support enterprises in three major stages: business insight, decision optimization, and circulation empowerment.To activate the value of these data, enterprises not only need to strengthen the construction of data infrastructure but also pay attention to the quality of data management, including improving data governance, establishing management processes and standards, enhancing the data literacy of enterprise personnel, etc., so as to maximize the utility of data in various application scenarios.

The establishment of the National Data Administration and the introduction of the 'Twenty Regulations on Data' have provided strong support for Chinese enterprises in constructing and improving their data management systems. At the same time, they have laid the foundation for accumulating experience in the practice of data in different business scenarios and exploring data assetization.

For enterprises, data assets and data products help to materialize the value of data, enabling them and their stakeholders to clearly identify the value of internal and external data, as well as the contribution of data to business development. Effective data governance is key to this.

Enterprises need to establish a data quality management system and architecture to ensure the accuracy, completeness, and consistency of data, and to enable the smooth aggregation and circulation of data between different departments and systems. At the same time, enterprises also need to strengthen their attention to metadata management to ensure that data is discoverable, manageable, and interpretable. Through data security and compliance management, they should prevent data leakage and meet compliance requirements.

This report will evaluate the comprehensive performance of competing entities from two dimensions: customer value and technology leadership.

The evaluation system is based on the average score of the comprehensive performance of Chinese and global manufacturers (including both domestic and overseas ones), and dynamically stratifies competing entities into tiers. These benchmarks will change dynamically with market and manufacturer development, reflecting the actual competitive landscape. Without pre-setting score thresholds, they can more accurately identify industry leaders.

Based on the comprehensive scores of 'Customer Value' and 'Technology Leadership', Tencent Cloud, Amazon Web Services (AWS), Alibaba Cloud, Huawei Cloud, and Inspur Cloud are positioned in the leading tier of the Chinese data management solution market. The following are some selected evaluations of the comprehensive performance of these vendors:

Tencent Cloud:

• customer valueIn terms of value creation, Tencent Cloud attaches great importance to the construction of data governance capabilities. Tencent Cloud continuously integrates AI capabilities into solutions and continuously improves the depth and breadth of WeData functions, providing users with features such as data quality management, asset inventory, and data exploration. This helps various users build a complete data governance system and effectively carry out governance work regardless of their data governance level. In addition, Tencent Cloud provides strong security technical support for the security and compliance of data with its profound security technology strength. In terms of value delivery, Tencent Cloud offers a series of tools to facilitate the implementation and deployment of solutions. Among them,FinOpsThe layout is at the industry-leading level, capable of efficiently assisting users in resource and cost management.

   

• Technology-ledTencent Cloud places great emphasis on technological innovation and layout. It leads in China in terms of research topics, achievements in data management solutions, and the number of patents applied for. At the same time, Tencent Cloud has made forward-looking arrangements in areas such as cloud-native, data governance, artificial intelligence, lakehouse integration, and real-time analysis, forming a profound technical strength. To meet the needs of data circulation and value discovery in the Chinese market, Tencent Cloud continues to strengthen data governance, optimize WeData capabilities, enhance user convenience, and launch the Metadata Lake Management System, unifying different data sources and providing key technical foundations to unlock data value. The enterprise-level lakehouse integration solution built through Tencent Cloud TCHouse and Data Lake Computing DLC enables users to enjoy both the flexibility of a data lake and the advantages of low-cost integrated storage + the high-performance query advantages provided by cloud TCHouse.

Amazon Web Services:

• customer valueAmazon Web Services (AWS) places significant emphasis on customer experience and business value through its technology investments. All its data analysis services are now Serverless, and it actively invests in Zero-ETL development to simplify the process of building data management capabilities for enterprise users, reduce operational burdens, and significantly enhance user experience. AWS also has a deep layout in data governance. For example, Amazon DataZone can help enterprise users catalog, discover, analyze, share, and manage data across organizational boundaries. In addition, by integrating generative AI capabilities, Amazon DataZone can also provide explanations for tables and metadata, making it easier for users to understand their data.

   

• Technology-ledAmazon Web Services (AWS) is at the forefront of the industry in cloud-native, artificial intelligence, data governance, and real-time data processing, and has implemented solutions in multiple industries, demonstrating mature technical capabilities. Especially in the Data+AI domain, AWS places great emphasis on matching technology with actual customer needs, promoting the practical application and efficient implementation of technology. For example, Amazon QuickSight Q allows users to ask questions in natural language and automatically generate visual reports; Amazon Glue Studio Notebooks integrate Amazon CodeWhisperer, helping data engineers, analysts, and developers write Glue jobs with AI code, significantly improving work efficiency.

Alibaba Cloud:

• customer valueIn terms of value creation, Alibaba Cloud continuously optimizes system capabilities among products and develops a series of solutions to help users quickly build data management systems. For example, Alibaba Cloud's OpenLake solution. At the same time, Alibaba Cloud continues to make efforts in data governance and compliance with data security regulations. Its DataWorks Data Governance Center can automatically identify issues in dimensions such as data storage, task computing, code development, data quality, and security, and quantitatively evaluate them through health scores. It provides governance results from multiple perspectives including global, workspace, and individual perspectives to help enterprises efficiently achieve governance goals.

   

• Technology-ledIn the field of AI4Data, Alibaba Cloud has launched DataWorks Copilot and provides solutions integrating MaxCompute and PAI to help enterprises improve efficiency in data processing needs across multiple scenarios. In terms of Data4AI, Alibaba Cloud has paid special attention to the critical impact of data quality on AI. During the data preparation phase, Alibaba Cloud provides solutions for identifying and cleaning harmful data, filtering abnormal features, and de-anonymizing private data to ensure that AI training is conducted on secure and compliant datasets.

Huawei Cloud:

• customer valueIn terms of value creation, Huawei Cloud has a profound accumulation in data governance and data security compliance. Combining years of practical experience, it can provide users with quick-to-implement solutions that meet industry standards. Huawei Cloud not only focuses on providing efficient data management for customers but also pays special attention to helping them ensure data security and privacy protection, thereby maximizing the value of data assets.

   

• Technology-ledIn terms of technology foresight, Huawei Cloud focuses on the implementation of data governance capabilities in specific industry application scenarios. DataArts Studio provides data governance frameworks and templates, offering a quick start for enterprise users to establish their data governance capabilities. With the upgrade of DataArts, Huawei Cloud integrates AI and big data fusion engines, data development governance, knowledge services, and intelligent application empowerment services, significantly improving resource utilization efficiency and data supply efficiency.

Inspur Cloud:

• customer valueIn terms of value communication, Inspur Cloud has established a good mechanism, laying a solid foundation for market education, demand understanding, and user relationship building. This is conducive to fostering trust and loyalty among enterprise users. To fully identify and understand customer needs, Inspur Cloud has built an integrated RAG information sharing platform and reward and punishment mechanism within the organization, promoting efficient information transmission and feedback. At the same time, Inspur Cloud has also established a complete mechanism for collecting and storing external customer needs to ensure that market demands can be captured and responded to in a timely manner.

   

• Technology-ledInspur Cloud has a well-structured ecosystem for promoting innovative development. It has made outstanding investments in technology achievements and standardization, actively participated in the compilation of many Chinese standards related to data management solutions, and attaches great importance to patents and technological innovation, achieving remarkable results. In terms of technological foresight, Inspur Cloud performs well in the forward-looking layout of artificial intelligence and data governance. Among them, the capabilities of artificial intelligence have been integrated into specific functions of data management and data governance, which helps to reduce user operation and maintenance pressure and improve the efficiency of releasing data value.