数据散修：修复散乱数据的策略与方法

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Understanding “Data Casual Repair”

The term "data scattering" vividly describes a common situation in data governance: data is scattered across various systems, databases, files and even in different formats, just like practitioners scattered everywhere, and we need to gather them together and manage them in a unified manner.

Why is the data scattered?

• Historical reasons:  As the company develops, different departments and systems operate independently, resulting in serious data silos.
• Diverse data sources:  Data may come from various channels such as internal systems, external interfaces, and manual entry.
• Inconsistent data formats:  Different systems and departments  may use different data formats and encodings.
  

How to "fix" messy data?

• Data inventory and sorting
  • Data source identification:  Find all systems and files that contain target data.
  • Data format analysis:  Understand the structure and characteristics of each data format.
  • Data quality assessment:  Evaluate the completeness, accuracy, and consistency of data.
    
• Data Integration
  • ETL process:  Use ETL tools to extract, transform, and load data from different sources into a unified data warehouse or data lake.
  • Data cleaning:  dealing with data quality issues such as missing values, outliers, and duplicate values.
  • Data standardization:  unify data format, coding, units, etc.
    
• Data Modeling
  • Conceptual model:  Establish a business concept model to clarify the relationship between data.
  • Logical model:  Convert the conceptual model into a logical model and design the database table structure.
  • Physical model:  maps the logical model to a specific database system.
    
• Data Governance
  • Data standard formulation:  Establish unified data standards, including naming conventions, coding rules, etc.
  • Data permission management:  Control the access rights of different users to data.
  • Data quality monitoring:  Regularly monitor data quality to identify and correct problems in a timely manner.
    
  
  

Common tools and techniques

• ETL tools:  Informatica, Talend, Kettle
• Database:  Oracle, SQL Server, MySQL, PostgreSQL
• Data warehouse tools:  Kimball, Inmon
• Big data platforms:  Hadoop, Spark
• Data visualization tools:  Tableau, Power BI
  

The value of data repair

• Improve data quality:  Unified data standards and quality monitoring to ensure data accuracy and consistency.
• Improve data utilization:  break through data silos, realize data sharing, and provide strong support for business analysis.
• Support data-driven decision-making:  Based on a unified data platform, conduct in-depth data analysis to provide a basis for decision-making.
• Reduce data maintenance costs:  Reduce repetitive data maintenance work through data integration and standardization.
  

Summarize

Data repair is a complex and systematic project that requires the comprehensive use of multiple technologies and tools. Only through careful planning and execution can scattered data be transformed into valuable assets and provide impetus for enterprise development.

What aspect do you want to learn more about?  For example:

• Specific ETL tools used
• Methods for data quality assessment
• Best Practices for Data Modeling
• Application of big data platform in data integration