Life cycle management : supports users to specify the life cycle when creating tables regularly detects data update time automatically clears expired data releases storage space and reduces storage pressure and costs. Data kinship : It can automatically parse synchronization tasks and SQL codes to generate tablelevel and fieldlevel kinship relationships. Users can query the ins and outs of each indicator which facilitates developers to quickly locate and troubleshoot problems and business personnel to deeply understand the indicators.
Data quality Data quality is the usable baseline for the entire data cons Job Seekers Phone Numbers List truction and governance work. For a data system that cannot guarantee data quality no matter how cool the data application is it will be a castle in the air that is neither trustworthy nor usable. Therefore data quality management is an integral part of data management. How do we measure whether data quality is up to par?accuracy uniqueness and timeliness. How should we carry out data quality management? The following steps are for reference: Step Plan: Refer to data standards Define data quality rule base Build data quality evaluation index system Develop data quality management strategies and plans.
Step Execution: Rely on tools to manage internal and external requirements rule bases and evaluation index systems determine business project and data scope and carry out quality audits and differentiated management. Step InspectionAnalysis: Record the audit results analyze the cause of the problem identify the person responsible and issue a report and rectification suggestions. Step Improvement: Establish a data quality management knowledge base improve management processes improve management efficiency and optimize management strategies. When it comes to data quality management we should follow two major principles: source governance and closedloop management.
發表於 2024-3-11 11:49:37
