Traditional systems are built in a "stovepipe" manner, and users have inconsistent permissions for various applications and data. This forces them to switch between systems frequently during work, resulting in low work efficiency.
There is a lack of a unified service portal, and matters are not categorized. For business operations and pending tasks across decentralized systems, users need to access each individual system to handle them.
Facing multi-level and multi-dimensional subordinate organizations, it is impossible to assign maintenance permissions, and thus unable to promptly respond to the business needs of group-based operations and hierarchical authority distribution.
Traditional OA systems have an overly outdated interface, fail to comply with UI specifications, lack personalized features such as theme switching and font adjustment, and have low interface interactivity.
How to govern data has become a major challenge for enterprise managers. Enterprises need to address a series of issues, such as incomplete data, scattered and inconsistent data, low data quality, and high integration costs. Based on this, Shanghai DeSmart has carried out long-term, continuous and effective data governance, and is committed to establishing a practical data governance system. Taking solutions for master data, data governance and data middle platform as the foundation, the company manages data standards, data governance, data security, data models, data architecture, data lifecycle, master data, metadata, etc. Through these efforts, it controls data from the source, unifies data standards, conducts data mining and analysis, and coordinates external data. This helps improve data quality, enhance data sharing, simplify structures, establish a comprehensive and unified enterprise-wide management system, solve enterprises’ problems in data "storage", "connection" and "application", enable data to empower enterprises, and enhance their competitiveness.
Relying on low-code data technology, enterprises can reduce development costs, unify data standards, lower communication costs for business personnel, facilitate data search and access methods, reduce the time cost of data acquisition, and ultimately achieve a reduction in overall enterprise operating costs.
Relying on data governance, processes can be traced, data can be tracked, barriers between data and business are broken down, and the processes of data assetization, asset valuation, and value servitization are accelerated, truly achieving cost reduction and efficiency improvement.
After integrating enterprise-wide data, data transitions from a single-department or single-business-system perspective to an enterprise-wide, all-domain perspective. This enables more comprehensive and scientific data analysis, providing data support for enterprise leaders' decision-making.
Through data governance, business systems are connected, the problem of data silos is solved, and a unified data service is built based on data standards. This enables integrated analysis of data and business, contributing to the digital transformation of enterprises.
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