Curtailment (SPEN_009) Data Quality Checks

Attachments

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Dataset schema

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Name
Title of the data table.

No description available for this field.

Name (identifier)
name
Type
text
Sample
                       
Field
Name of column that data check has been applied to.

No description available for this field.

Name (identifier)
field
Type
text
Sample
                       
Description
Details of data check applied.

No description available for this field.

Name (identifier)
description
Type
text
Sample
                       
Score
Percentage of rows that adhere to data quality check.

No description available for this field.

Name (identifier)
score
Type
decimal
Sample
                       
Failed Rows
Number of rows that did not adhere to the data quality check.

No description available for this field.

Name (identifier)
failed_rows
Type
decimal
Sample
                       
Dimension
VALIDITY measures whether the values in a dataset are within the correct range or format. This dimension ensures that the data adheres to predefined criteria set by the data owner, such as acceptable value ranges, formats, and types.

COMPLETENESS checks whether the cells in a dataset are filled or empty. The score is based on a simple 'Yes/No' - if the cell is filled, it counts as complete. This check does not consider if the value in the cell is correct/valid.

UNIQUENESS measures how many values in a dataset are unique. Any duplicate values will lower this score. This measure is important for data that must be unique to be correct, such as Customer ID or Project Reference ID.

No description available for this field.

Name (identifier)
dimension
Type
text
Sample
                       

JSON Schema

The following JSON object is a standardized description of your dataset's schema. More about JSON schema.

{
  • "title":"spen_data_quality_curtailment",
  • "type":"object",
  • "oneOf":
    [
    • {
      • "$ref":"#/definitions/spen_data_quality_curtailment"
      }
    ]
    ,
  • "definitions":
    {
    • "spen_data_quality_curtailment":
      {
      • "properties":
        {
        • "records":
          {
          • "type":"array",
          • "items":
            {
            • "$ref":"#/definitions/spen_data_quality_curtailment_records"
            }
          }
        }
      }
      ,
    • "spen_data_quality_curtailment_records":
      {
      • "properties":
        {
        • "fields":
          {
          • "type":"object",
          • "properties":
            {
            • "name":
              {
              • "type":"string",
              • "title":"Name",
              • "description":"Title of the data table."
              }
              ,
            • "field":
              {
              • "type":"string",
              • "title":"Field",
              • "description":"Name of column that data check has been applied to."
              }
              ,
            • "description":
              {
              • "type":"string",
              • "title":"Description",
              • "description":"Details of data check applied."
              }
              ,
            • "score":
              {
              • "type":"number",
              • "title":"Score",
              • "description":"Percentage of rows that adhere to data quality check."
              }
              ,
            • "failed_rows":
              {
              • "type":"number",
              • "title":"Failed Rows",
              • "description":"Number of rows that did not adhere to the data quality check."
              }
              ,
            • "dimension":
              {
              • "type":"string",
              • "title":"Dimension",
              • "description":"VALIDITY measures whether the values in a dataset are within the correct range or format. This dimension ensures that the data adheres to predefined criteria set by the data owner, such as acceptable value ranges, formats, and types. COMPLETENESS checks whether the cells in a dataset are filled or empty. The score is based on a simple 'Yes/No' - if the cell is filled, it counts as complete. This check does not consider if the value in the cell is correct/valid. UNIQUENESS measures how many values in a dataset are unique. Any duplicate values will lower this score. This measure is important for data that must be unique to be correct, such as Customer ID or Project Reference ID."
              }
            }
          }
        }
      }
    }
}

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