Gientech Data Governance Consulting empowers enterprises through data strategy planning, governance assurance mechanisms, data standardization, data quality management, and metadata organization.
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Conduct in-depth interviews and surveys to assess the client’s current data management practices. Using the Data Management Capability Maturity Model (DCMM), we evaluate ten key domains of data governance to identify gaps between the client and industry best practices. Root cause analysis and our hands-on experience inform actionable improvement recommendations, culminating in a comprehensive diagnostic report.Conduct in-depth interviews and surveys to assess the client’s current data management practices. Using the Data Management Capability Maturity Model (DCMM), we evaluate ten key domains of data governance to identify gaps between the client and industry best practices. Root cause analysis and our hands-on experience inform actionable improvement recommendations, culminating in a comprehensive diagnostic report.
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Design a tailored data governance framework aligned with the client’s organizational structure, clearly defining roles and responsibilities across all levels.
Develop enterprise-wide data standards, including a data standards dictionary, to enable consistent and efficient data sharing.Design a tailored data governance framework aligned with the client’s organizational structure, clearly defining roles and responsibilities across all levels.
Develop enterprise-wide data standards, including a data standards dictionary, to enable consistent and efficient data sharing. -
Develop enterprise-wide data standards, including a data standards dictionary, to enable consistent and efficient data sharing.Develop enterprise-wide data standards, including a data standards dictionary, to enable consistent and efficient data sharing.
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Establish a data quality management framework. Based on our experience and in collaboration with business and technical teams, we design a comprehensive rule library for data quality checks, conduct assessments, and implement a closed-loop quality control process—resulting in a detailed Data Quality Analysis Report.Establish a data quality management framework. Based on our experience and in collaboration with business and technical teams, we design a comprehensive rule library for data quality checks, conduct assessments, and implement a closed-loop quality control process—resulting in a detailed Data Quality Analysis Report.
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Define a robust system of governance policies to ensure effective and sustainable operations. This includes management policies for data standards, data quality, metadata, and data security, serving as safeguards for long-term governance.Define a robust system of governance policies to ensure effective and sustainable operations. This includes management policies for data standards, data quality, metadata, and data security, serving as safeguards for long-term governance.
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Promote a data governance culture within the organization through targeted communication and awareness programs, fostering deep understanding and alignment among employees.Promote a data governance culture within the organization through targeted communication and awareness programs, fostering deep understanding and alignment among employees.
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Provide support for building a data asset management platform to operationalize data governance efforts, ensuring the delivery and long-term retention of governance outcomes.Provide support for building a data asset management platform to operationalize data governance efforts, ensuring the delivery and long-term retention of governance outcomes.
As a core member of China’s Xinchuang initiative, GienTech’s Big Data team is one of the largest and most capable consulting teams in the domestic data management field.
With over 20 years of deep expertise in the financial data domain, we offer end-to-end services encompassing consulting, product solutions, and implementation. Our data governance solutions are built upon comprehensive assessments of enterprise data architecture and current data conditions, benchmarked against industry best practices and aligned with DCMM standards. These solutions are designed to support the enterprise’s long-term data strategy.
