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Determine the Current and Desired Quality Levels

After gaining a functional understanding of the data objectives and identifying the key necessities, the next step is to define the level of data quality. This should be done for the data in its current state as well as how you would like it to be.

Picture of twins - got duplicates??Measurable quality elements:

The following are examples of standards you could set for your data. This is where you get to be the boss; you get to define your standards of cleanliness for your project. Quality can be defined in terms of:

Accuracy

  • Data values present are one of a predetermined value for the specific field
  • Numeric values are within a predetermined specified range
  • Date values fall within a predetermined time period
  • Data outliers are non-existent or have identified and corrected/explained
  • Derived data fields have been performed correctly
  • Calculated data fields have been calculated correctly

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Consistency

  • Symbolic data values are consistent throughout the given dataset
  • Data values are consistent across related datasets

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Completeness

  • There are no missing values were completeness is required
  • The number of records present is the appropriate amount of data
  • All necessary fields are present
  • Primary keys are present, unique and in good format
  • All foreign key fields are present and in good format

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Duplicates

  • Duplicate records are not present
  • Redundant fields are not present
  • Duplicate records across distinct datasets are not present

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Rules

  • All rules have been identified and are accurate
  • Data has been tested and follows data rules
  • All field data is formatted correctly for the representative data type

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Usability

  • Metadata is available
  • Data is easy to interpret
  • Data is representative of intended objectives

Next StepDiscover the Scope of the Problem >>

 

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rev. 29-Aug-2016

 

 

 

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