Driven by MAD to realize scenario-based data control, AISWare DataGo conducts inventory and assessment according to the current status of data, establishes control methods, releases the data value, promotes knowledge sharing, and makes data more accessible through good governance.
Driven by meta-model, AI/NLP and other technologies are to reduce manual workload and improve metadata quality
The form of explorer realizes the integrated invocation of data capabilities and the global unified entrance, and quickly builds an enterprise-level data directory, which makes data easier to understand and use, and continuously releases the value of data
Global and panoramic data search is performed based on meta-model objects, supporting the use of business language for data retrieval and query
Data resource governance and data asset operation are structured to solve data problems and develop data value
From the effective governance of data resources to the operation of data assets, a combination of control and use is adopted to advance the step-by-step data governance
Scenarios-based data management are realized through the best practices of industry-level data asset management, which establishes effective control methods, and effectively improves data operation efficiency
Driven by meta-model, supporting unstructured data governance and seamless integration of multi-source heterogeneous platforms. Based on data factory, meeting the needs of different roles within the company. Unified multi-tenant management realizes the effective management of data privacy
Reusing the accumulated best practices to quickly improve work efficiency and quality. Presetting excellent models and typical characteristic data accumulated by various industries, and providing stable and efficient AI algorithms and tools to quickly build high-quality data services, and save labor and capital costs
Able to be integrated with third-party platform capabilities. With the IT construction process as the core, API service capabilities (such as audit tools, standard assessment, AI inventory, master data services, etc.) can be integrated in different project stages, and capabilities can be turned into microservices and embedded in other systems to promote control with services
Rich visual components support graphical display and drag-and-drop operation. Data assets are displayed and manipulated in the form of catalogs, atlases, maps, etc., crowdfunding and rapid migration are applied for data
From the “separated” governance route to the “consistent” one, the three full (full process, full life cycle, and full view) governance model, integrated management (integrating data definition, specification, development, operations, assessment, openness, and running)
The deployment method is flexible, which is like building blocks. Providing multiple model output methods such as SDK and API, which can be quickly and seamlessly integrated with existing platforms and systems of customers
Meta-model driven management
Innovative data asset management
Professional, innovative and intelligent control
Data assetization and monetization
By constructing the DataGo metadata management platform, the distribution of enterprise data has been clarified, the contextualized data relationships realized, and the ability to quickly locate data problems and causes achieved, which lays the foundation for realizing data quality management and improvement in the industry and global data traceability.
The master data of the group and its subsidiaries have been managed, the unified data collection, data management, and visual presentation of data completed, and the standard system for the master data of group products, materials, and customers unified, which realizes the unified management of master data, and helps the company reduce costs, increase efficiency and improve quality.
An enterprise-level data resource directory has been quickly built based on data inventory technology, and high-value data are open to provincial companies for browsing, application, and downloading, so as to achieve basic goal of “full visibility” of the company’s data resource directory and the “sharing and available” data resource content. The full data resources of the first-level deployment system have been centralized comprehensively to meet the needs of provincial companies for “easy access” to data resources within the scope of their own authorities, and support the innovative applications and innovative development of businesses.