Data Quality

Data Quality

Data Quality

Validity: How closely the data meets defined business rules or constraints. Some common constraints include:
  >Mandatory constraints: Certain columns cannot be empty
  >Data-type constraints: Values in a column must be of a certain data type
  >Range constraints: Minimum and maximum values for numbers or dates
  >Foreign-key constraints: A set of values in a column are defined in the column of another table containing unique   values  
  >Unique constraints: A field or fields must be unique in a dataset
   Regular expression patterns: Text fields will have to be validated this way. 
  >Cross-field validation: Certain conditions that utilize multiple fields must hold
  >Set-membership constraint: This one is the subcategory of foreign-key constraints. Values for a column come from a  set of discrete values or codes.

Accuracy: How closely data conforms to a standard or a true value.

Completeness: How thorough or comprehensive the data and related measures are known

Consistency: The equivalency of measures across systems and subjects

Uniformity: Ensuring that the same units of measure are used in all systems

Traceability: Being able to find (and access) the source of the data

Timeliness: How quickly and recently the data has been updated