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Built-in

Built-In Attribute fields can either be left empty or filled with data that may or may not represent actual values for the attribute. The non-actual values are forms of default values, place holders, or empty proxies. We summarize the various types of attribute fields below. Empty field Nothing is specified. It can be null or empty string Populated field Some value is specified. Such value could either be an actual value explicitly assigned to the attribute or some non-actual value that is used merely to not leave the field empty. More breakdown details below.
Actual value where a value is explicitly assigned to the attribute to represent its true value. The value is expected to comply with the attribute type and constraints. More details on how to validate that is in the Attribute Data Types section below
Default value where a predefined value is used as a proxy for unspecified actual value. Examples: 0 or -1 may be used as a default value for Salary, ‘Medium’ for Shirt_Size, ‘Clear’ for Color, ‘US’ for Country, ‘23%’ for Fed_TAX, ... etc. Note that default values might get intermixed with actual values and, thus, a desire to have them identified will depend on the specific use case
Placeholder where a dummy value is used as a proxy for unknown actual value. The placeholder can be thought of as a temporary value until the actual value becomes known, or just as a mere empty field filler. Examples: “*******” may be used as a placeholder for Title, “(415) 111-1111” for Phone, 99999 for Zip_Code, … etc.
Empty proxy where a string indicating a non existence of value is used. Such proxies come in all sorts of shapes and sizes. Examples: “ ”, “[ ]”, “none'', “null'', “n/a'', “nothing'', “not provided'', “unknown'', … etc
Note that default values may be considered as separate or intermixed with either actual values or placeholders, depending on the use case.
Telmai can offer
Immediate identification, and isolation, of empty fields
Comprehensive out of the box lists of empty proxies
Automated identification of potential placeholders
Advanced ML or other methods for automated facilitation to easily customize lists of non-actual values for attributes. This includes empty proxies and placeholders
Capability to have customized lists be defined for either the entire dataset or for a segmented subset of it (e.g., in the context of default values)
Measures of completeness which to be used for data quality evaluation and trends monitoring
Customized completeness measures that take into consideration any combination of the following: empty fields, empty proxies, placeholders, and default values