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Create DQ Evaluation Rule

There are multiple ways to create a DQ evaluation rule in ONE:

Rule creation in the rule library. This method gives you access to all the possible rule-creation options, including variables, parameters, and scoring. See Create DQ evaluation rule in the rule library.
Quick rule creation from the attribute. Workflow optimized for quick and easy rule creation from your data. The rule template is prefilled with input and test data from the catalog item, and contains only the most commonly used options from the rule creation workflow. For more advanced options, such as variables, you can modify the rule in the rule library once it’s created. The rule is automatically applied to the attribute it was created from. See Quick DQ rule creation from attribute.
Quick rule creation from the profiling results. You can use mask, pattern, and frequency results available after profiling directly in rule conditions to help make rule implementation quicker and easier. For more information, see Create Rule from Profiling Results.

All rules are saved to the rule library under Data Quality > Rules, where you can access and edit them.

For an example of how to create a DQ rule for a specific use case, see Get Started with Data Quality.

Before you start

Check the following topics:

Data Quality - Learn how data quality works in ONE.
Detection and DQ Evaluation Rules - Read more about the types of rules and their role.

Quick DQ rule creation from attribute

Quick rule creation workflow enables you to quickly create a rule from an input attribute and automatically apply it to that attribute. This is useful in the following scenarios:

While browsing Data Catalog, you see wrong data and know the logic of the rule needed to evaluate the data. You can create and apply such a rule straight away, from the attribute listing on the catalog item Overview and Profile & DQ insights tabs.
When you need to apply a rule to an attribute but do not find a rule you need, you can create a new one from the Add Rule dialog.

Before creating a new rule, check for available rule suggestions.

To create a rule:

Open Create DQ Rule dialog in one of the following ways:
- In the catalog item attribute listing (for example, on the Overview tab), select the three dots menu for the attribute and then Create DQ Rule.
- Select Create Rule from any Add Rule dialog in ONE.
  
  The Add Rule dialog is available throughout ONE in multiple places where you might need to apply a rule.
  
  To open the dialog, select Add (catalog item Data Quality tab, monitoring project Configuration & Results tab) or Add Rule (catalog item attribute Overview, Data Quality, Profile & DQ insights tabs).
  
  The rule creation is not possible from the Add Rule dialog in Business Glossary, where rules are applied to terms instead of attributes.
A Create DQ Rule dialog opens and a draft of the new rule is created, already filled in with the following details:
- Validity as a default rule dimension.
- Input attribute name and data type are filled in based on the attribute from which you started rule creation.
  
  You can change these to suit your needs.
  
  If you are using the quick rule creation workflow for the first time, the rule logic is empty. Select Add condition for the prefilled rule draft to appear.
Specify the rule conditions. For details on building rule conditions, see Create DQ evaluation rule in the rule library, section Rule logic.

You can optionally test the rule by selecting Test Rule and trying different inputs in the test table.

The test table is prefilled with the data from the samples saved in the Data tab of the catalog item. You can also fill in the values manually, or copy and paste them from an Excel or CSV file.

Data samples are visible only to users with View data access to the catalog item (Data Owner and Data Steward by default). This data is not stored and is deleted once you exit the rule creation dialog.

You can edit any value in the table by double-clicking it. To remove a row, select X next to the row, and to add a row, select Add row at the bottom of the test table.

The results are evaluated automatically after any change in the test table.

To renew the test data, or to populate the test table column after adding a new condition, select Generate new data sample.

Select Next step.

In the General Information screen, provide the following information:

Name: The name of the rule.
Description (optional): A description of the rule and how to use it.
Stewardship (recommended): Select the user group which are owners of the asset. After you select a group, the list of users assigned to the governance roles within the selected group is displayed. For more information, see Stewardship.

Share with: Select groups or users with whom you want to share the rule (see the section Share access to data assets in Get Started with Catalog and Glossary).

If needed, select Previous step to return to the previous screen.

You can leave rule creation dialog at any time - select X and decide whether to keep or discard the draft. If you keep the draft, you can find it in the rule library in Data Quality > Rules.

Select Publish and apply to publish the rule and apply it to the attribute.

Depending on your access and whether the review workflow is set, you might see Publish, Request publish, Request publish and apply, or Request review and publish. Select the option to proceed with publishing and applying the rule to attribute.

A message appears confirming that the new rule was created and applied to the attribute.
To use the rule for DQ evaluation, publish the catalog item.

Create DQ evaluation rule in the rule library

To create a rule, do the following:

Navigate to Data Quality > Rules
Click Create.
Provide the following information:
- Name: The name of the rule.
- Description (optional): A description of the rule and how to use it.
- Rule definition source (optional): A descriptive name for the data source associated with the rule.
- Stewardship (recommended): Select the user group which are owners of the asset. After you select a group, the list of users assigned to the governance roles within the selected group is displayed.
  
  For more information, see Stewardship
Click Save to create a draft of the rule.
Before publishing or submitting an approval request, you need to define the rule logic consisting of at least one input attribute and condition. Continue to Rule implementation.

Rule implementation

Once you have created the rule, go to the Implementation tab.

Select rule type

Use the dropdown, and under DQ Evaluation Rules, choose from the following:

Validity: Validity rules verify the usability of the data (for example, regarding data format, data content or attribute relations). The default results are Valid and Invalid.
Uniqueness: Use this dimension to verify that there are no duplicate values and only one instance appears in the dataset. The default results are Unique, Not populated, and Not Unique.
Completeness: Use this dimension to verify that the value field is filled. The default results are Complete and Not complete.
Accuracy: Use this dimension to check whether values are accurate and reflect the true values, for example, based on reference data. The default results are Accurate, No reference available, and Not accurate.
Timeliness: Use this dimension to verify whether data is available at the time it is needed. The default results are Timeliness ok, Minor delay, and Major delay.
Custom: If you have created any custom dimensions, they can also be selected here.

DQ dimensions are fully customizable. Validity, Uniqueness, Accuracy, Completeness, and Timeliness are available by default, but their configuration and results can be changed if required. For more information, see Data Quality Dimensions.

Inputs

Add input attributes by providing names and selecting the data types. Select Add attribute if you require additional fields.

Input attributes are abstract at this point. The number of input attributes defined here and their rule logic become available when you apply rules to catalog items.

The attribute names can be as you choose and do not affect the implementation.

If special characters or reserved words are used in input names, they must be enclosed in square brackets or publishing fails.

(Optional) Add terms to the attribute.

Adding terms to input attributes during implementation results in this rule being suggested by the system in the instance an attribute also has this term.

(Optional) Add Parameters. Parameters are placeholders for values provided by the user. First, use parameters in conditions instead of constant values, and then provide parameter values when applying the rule. This way, you can create reusable rules that can be applied with different parameter values.

For example, you can use parameters to set up a DQ rule that checks whether the value of an age attribute falls within a certain interval, which is not known upfront. This rule will use any age interval specified by the user while applying the rule to the age attribute.

To add parameters:

Select Add parameter.
Provide a meaningful parameter name.

Provide a data type.

There are two additional data types that can be used with parameters:

List of values: Useful for validating whether the value is or is not from a predefined list. The list of values is provided by user when applying the rule.

Used in combination with the Is from parametrized list or Is not from the parametrized list condition.
List of masks: Useful for validating whether masks and patterns come from a predefined list or not. This is useful especially for autogenerated rules where the masks are prefilled from profiling (an option preferred to specifying masks manually).

This datatype is not supported in the Condition Builder yet, use ONE expression matches([Valid masks], Attribute) instead.

To add fields for additional parameters, select Add parameter.

Parameter values are case sensitive.

(Optional) Create Variables from your inputs. Within the Variables section, you can apply a range of transformations to your input attribute. For example, apply the rule conditions only on trimmed versions of string attributes.

To create variables:

Select a name for your variable. You can choose a meaningful name or leave as default. To add fields for additional variables, select Add variable.

Select the attributes on which the transformations are applied. Use the dropdown to select from the list of input attributes - you apply the transformations in the next step.

Alternatively, if you are an advanced user, use the dropdown to select Advanced Expression and define the attribute and the transformations using ONE expression language.

Add transformations using the Add transformation option. The transformations available depend on the data type of the selected attribute: a full list of types and available transformations is provided below.

You can apply multiple transformations to one attribute, but you can only add them in a sensical sequence. For example, if you start with an attribute of data type String and apply the transformation To integer, you can’t then add the Uppercase transformation, as this is only available on string.

For the same reason, you can only remove the last transformation in a sequence at any given time. To remove the transformation, hover over the last transformation and use the x to delete.

Expand to see all available transformations

Datatype Available transformations Description

Datatype	Available transformations	Description
`String`	`To float`	Changes data type from string to float.
`To integer`	Changes data type from string to integer.
`To long`	Changes data type from string to long.
`Uppercase`	Converts string characters to uppercase.
`Lowercase`	Converts string characters to lowercase.
`Trim`	Removes spaces from both ends of the string.
`Squeeze spaces`	Trims string and replaces repeated spaces with a single space.
`Remove non-digits`	Removes non-digit characters from string.
`Remove non-letters`	Removes non-letter characters from string.
`Integer`	`To float`	Changes data type from integer to float.
`To date`	Changes data type from integer to date.
`To long`	Changes data type from integer to long.
`Boolean`	`To String`	Changes data type from Boolean to string.
`Date`	`To datetime`	Changes data type from date to datetime.
`To string`	Changes data type from date to string.
`Datetime`	`To date`	Changes data type from datetime to date.
`To string`	Changes data type from datetime to string.
`Long`	`To float`	Changes data type from long to float.
`To string`	Changes data type from long to string.
`To date`	Changes data type from long to date.
`To datetime`	Changes data type from long to datetime.
`Float`	`To string`	Changes data type from float to string.
`Floor`	Rounds to the nearest integer that is less than or equal to float value.
`Ceiling`	Rounds to the nearest integer that is greater than or equal to float value.
`Round`	Rounds to nearest integer.

String

To float

Changes data type from string to float.

To integer

Changes data type from string to integer.

To long

Changes data type from string to long.

Uppercase

Converts string characters to uppercase.

Lowercase

Converts string characters to lowercase.

Trim

Removes spaces from both ends of the string.

Squeeze spaces

Trims string and replaces repeated spaces with a single space.

Remove non-digits

Removes non-digit characters from string.

Remove non-letters

Removes non-letter characters from string.

Integer

To float

Changes data type from integer to float.

To date

Changes data type from integer to date.

To long

Changes data type from integer to long.

Boolean

To String

Changes data type from Boolean to string.

Date

To datetime

Changes data type from date to datetime.

To string

Changes data type from date to string.

Datetime

To date

Changes data type from datetime to date.

To string

Changes data type from datetime to string.

Long

To float

Changes data type from long to float.

To string

Changes data type from long to string.

To date

Changes data type from long to date.

To datetime

Changes data type from long to datetime.

Float

To string

Changes data type from float to string.

Floor

Rounds to the nearest integer that is less than or equal to float value.

Ceiling

Rounds to the nearest integer that is greater than or equal to float value.

Round

Rounds to nearest integer.

Rule logic

First, select whether you are creating a standard Rule, Aggregation rule, or Component rule.

Component rules need to be created first in ONE Desktop by power users. To add a component rule, select the component from the list of existing components by clicking Select a Component.

The component rule needs to fulfil the following conditions in order to ensure the correct results are returned:

The total number of input records must match the total number of output records in the component.
The order of output records must match the order of input records.

Aggregation rules require additional configuration, described in Aggregation Rules.

Provide the rule conditions and an explanation of the results. There are two ways to do this: through the Condition Builder or via Advanced Expression:

Select Advanced Expression to leverage ONE expressions in your rule condition (see ONE Expressions).
Select Condition Builder to define the rule logic using the predefined options.

If you selected Advanced Expression, enter the expression in the space provided and proceed to Test rule.

If you selected Condition Builder:

Select the input attributes to use in the rule logic.

Select any required modifiers, for example, Trim or Round. Different modifiers are available for string and integer inputs.

Expand to see all available modifiers

Modifier Description

Modifier	Description
`Value`	The given value of an attribute.
`Uppercase`	Uppercase version of string.
`Lowercase`	Lowercase version of string.
`Trim`	Removes any whitespaces from both sides of strings.
`Trim left`	Removes any leading whitespaces from strings.
`Trim right`	Removes any trailing whitespaces from strings.
`Squared`	Squares the value of an integer, float, or long data type attributes.
`Round`	Rounds the value of an integer, float, or long data type attributes to the nearest whole number.
`Average`	Averages the value of integer, float, or long data type attributes (available only when using aggregation rules and grouping).
`Min`	The minimum of string, integer, or long data types attributes (available only when using aggregation rules and grouping).
`Max`	The maximum of string, integer, or long data type attributes (available only when using aggregation rules and grouping).
`Sum`	The sum of of string, integer, or long data type attributes (available only when using aggregation rules and grouping).

Value

The given value of an attribute.

Uppercase

Uppercase version of string.

Lowercase

Lowercase version of string.

Trim

Removes any whitespaces from both sides of strings.

Trim left

Removes any leading whitespaces from strings.

Trim right

Removes any trailing whitespaces from strings.

Squared

Squares the value of an integer, float, or long data type attributes.

Round

Rounds the value of an integer, float, or long data type attributes to the nearest whole number.

Average

Averages the value of integer, float, or long data type attributes (available only when using aggregation rules and grouping).

Min

The minimum of string, integer, or long data types attributes (available only when using aggregation rules and grouping).

Max

The maximum of string, integer, or long data type attributes (available only when using aggregation rules and grouping).

Sum

The sum of of string, integer, or long data type attributes (available only when using aggregation rules and grouping).

Select the required conditions, for example, matches_mask or has length of, and provide the necessary requirements. The options available depend on the data type of the input attributes.

Expand to see all available conditions

Condition Description

Is empty

When the input is not filled.

This checks if the field is filled, but a field is not identified as empty if it is filled with: NULL, Null, null, ., ,, -, _, N/A, n/a, and so on.

Is not empty

When the input is filled.

Is the same as

When the input is the same as the value you define.

Is the same as attribute

When the attribute is the same as the attribute you select.

Is not the same as

When the input is not the same as the value you define.

Is not the same as attribute

When the attribute is not the same as the attribute you select.

Is from the following list

When the attribute belongs to the list you define.

Is not from the following list

When the attribute does not belong to the list you define.

Is from parametrized list

When the attribute belongs to the list of parameters you define (available only when using List of values parameter data type).

Is not from parametrized list

When the attribute does not belong to the list of parameters you define (available only when using List of values parameter data type).

Has length of

Is the same as the length you define.

Contains

When the input contains a substring you define.

Is from catalog item

When input belongs to a lookup item built from specified catalog item attribute or reference data in ONE Data.

To make sure the rule always uses the latest data available, select ON DATA CHANGE in Data updates. If you select a catalog item that is not managed using ONE Data, no automatic updates are available.

For more information, see Use Lookups in Rules.

Is not from catalog item

When input doesn’t belong to a lookup item built from specified catalog item attribute or reference data in ONE Data.

Is from lookup

When attribute input belongs to the lookup file you select.

Is not from lookup

When attribute input does not belong to the lookup file you select.

Matches mask

When input corresponds to the mask you define.

Does not match mask

When input does not correspond to the mask you define.

Matches regexp

When input corresponds to the regular expression you define.

Does not match regexp

When input does not correspond to the regular expression you define.

Is true

When Boolean input is true.

Is false

When Boolean input is false.

Is unique

When aggregated group is unique.

Is not unique

When aggregated group is not unique.

Select whether the condition defined should produce the result that is, for example, Valid or Invalid. Options are different depending on the dimension chosen.

Optionally, enter an explanation to help distinguish between multiple rules of the same dimension.
(Optional) Assign a score to results that are then used during evaluation. See Assigning scores.

Test rule

You can optionally test the rule by clicking Test Rule and trying different inputs in the Test section. The results for each input are shown automatically in Message next to the test input row.

Publish rule

After the changes are implemented, they need to be published.

Scoring records

The score is used as a numeric expression of the severity of each record’s invalidity according to the rule. This is mainly useful for monitoring projects to detect the most pressing issues and to be able to improve the data quality.

Scores are arbitrary and can be assigned to suit your project.

Typically, the logic follows that the higher the score, the worse the issue. For example, if a non-critical value is missing, the invalidity score could be 100. If a critical value is missing, the invalidity score could be 10000.

Assigning scores

It is also possible to assign scores via the validation component in ONE Desktop. This is available only for power users.

For more information, see Validation Components.

In the example shown, you have a validity rule in which an invalid record due to errors is considered of higher importance than an invalid record due to a null value, the invalid results being scored at 50 and 500 respectively.

Cumulative results of data quality statistics are shown on the Configuration and Results tab of a monitoring project. However, to be able to see the score of each record, it is necessary to export the project.

Next steps

Apply your newly-created DQ rule to a term, or directly to attributes in the data catalog. See Add Rules to Terms and Add DQ Rules to Attributes.

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