Data Insight

Data insight is defined as a graphical representation of the health of data.  In MDO, the data insight framework provides an easy understanding of the data quality. It helps the user to quickly identify records in error with their specified categorization in business rules 

The Data insight framework provides a graphical representation in three ways:     

  • Overall data quality representation 
  • By Categories 
  • By Business rules 

The data metrics can be either defined as record counts or percentage of total records. The metrics are shown across a timeline to identify data quality trends. The three graphical representation can be shown as below: 

Overall data quality representation:

This section gives the user a graphical representation of the overall health of their data. Using this report the user can find out the number/percentage of records in error at any specific date and time.  The report can also be filtered based on date ranges. This helps users to keep a track of the health of the data as well as monitoring the effectiveness of the data governance solution. 

With this representation, a display of the total number of records in error with graphical representation can be produced. With the Y-axis, representing the number of records in error and the X-axis representing the time when the schema is being run, every time the schema is run, it will be visible on the graph. As the number of times is increased, the X-axis of the graph changes from the time to the day when the schema was run. In order to receive the updated representation, it is necessary to run the schema in the data quality workbench prior to checking it on the data insight framework. Schemas can be viewed based on the date range of the X-axis. 

By Categories: 

This section gives the user a graphical representation number/percentage of inconsistent records in a category. Using this report, the user can find out the number or percentage of records in error at any specific date and time. The reports can help users to keep a track of data quality as well as monitor the effectiveness of their data governance solution. 

Using the categories representation defines the number of error records. This is based on the categories defined in the schema such as completeness, accuracy or validity. With the Y-axis, representing the number of records in error and the X-axis representing the time when the schema is being run, every time the schema is run, it will be visible on the graph. As the number of times is increased, the X-axis of the graph changes from the time to the day when the schema was run. In order to receive the updated representation, it is necessary to run the schema in the data quality workbench prior to checking it on the data insight framework. Schemas can be viewed based on the date range of the X-axis. 

By Business Rules: 

Using business rules, the result is represented based on each business rule defined in the schema. With the Y-axis representing the number of records in error and the X-axis representing the time when the schema is being run, every time the schema is run, it will be visible on the graph. As the number of times is increased, the X-axis of the graph changes from the time to the day when the schema was run. In order to receive the updated representation, it is necessary to run the schema in the data quality workbench prior to checking it on the data insight framework.  

After selecting the schema, the inconsistent records can be viewed. The inconsistency across business rules are depicted with red borders across identified fields.