Data stewards use Data Cookbook to document data quality standards, maintain lists of valid values, and track issues that affect institutional data. This work helps the University identify data problems, clarify expectations, and improve data over time.

Use this guide to create and manage quality rules, reference data, and quality issues.

Data Quality Features

Data Cookbook includes three related tools:

Quality Rules
Data standards used to assess the quality of a specific data element.

Reference Data
Lists of codes or values that are valid for a specific data element.

Quality Issues
Documented issues created when data does not meet a quality rule or when a data problem is identified.

Quality Rules

A quality rule documents what acceptable data should look like for a specific data element. Rules may represent University-wide standards or requirements that apply only within a particular system.

For example, a quality rule may establish that a date must follow a specific format, a value must come from an approved list, or a field cannot exceed a set number of characters.

In a future phase, Data Cookbook may run scheduled checks against system data and automatically create quality issues when a rule exceeds its allowed error threshold. For example, a rule could allow up to 2% of records to contain errors before creating a quality issue.

Creating a Quality Rule

1. From the Quality Rules menu, select Create a Quality Rule.

2. Complete required fields, marked with red asterisks (*). Ensure that the Quality Rule name follows the naming convention listed in the next section. 

3. Use the available Data Cookbook options to identify the validation type, such as valid value, character length, format, or required field.

4. Identify whether the rule is a University-wide standard or a system-specific rule.

5. Select Save to keep the rule as a draft.

6. Select the appropriate workflow action when the rule is ready for review.

Quality Rule Naming Convention

Use the following format:

Data Element-Classification-Validation Type-Population-System

Naming component	Guidance
Data Element	Use the element name and terminology established in the Data Cookbook glossary.
Classification	Use Standard for a Data Management Council-approved University-wide standard. Use Rule for a system-specific requirement.
Validation Type	Select the applicable validation type, such as Valid Value, Length, or Format.
Population	Include the population when the rule applies only to a specific group, such as employees, applicants, enrolled students, faculty, or staff.
System	Identify the system to which the rule applies.

Examples:

• Date of Birth-Standard-Format-TXST

• Phone Number-Rule-Length-Employees-Banner

Quality Rule Workflow

Quality rules follow either the standard workflow or the editorial update workflow.

1. Drafting – The creator prepares a new rule or new version.

2. Division Review – The Divisional Data Management Subcommittee reviews the rule for accuracy, completeness, and alignment with divisional practices.

3. DAIR Review – The data governance team within Data, Analytics, and Institutional Research (DAIR) completes the final review and approves the rule.

Use the editorial update workflow only for minor revisions, such as correcting a typo, updating contact information, or adding clarification. Send substantive changes through the standard workflow.

Reference Data

Reference data documents the approved values for a specific data element, such as a list of codes, classifications, or statuses. Quality rules can use reference data to validate whether a data value is allowed.

Reference data may also serve as a central record of valid values when an automated quality check is not in place.

Creating Reference Data

1. From the Reference Data menu, select Create Reference Data.

2. Enter a clear name and description that explain the data element and the list of valid values.

3. Document each approved code or value and its meaning.

4. Select Save to keep the item as a draft.

5. Select the appropriate workflow action when it is ready for review.

Reference Data Workflow

Reference data follows either the standard workflow or the editorial update workflow.

1. Drafting – The creator prepares a new reference data item or new version.

2. Division Review – The Divisional Data Management Subcommittee reviews the item for accuracy and completeness.

3. DMC Review – Data Management Council members review the item for consistency across divisions.

4. DAIR Review – DAIR completes the final review and approves the item.

Use the editorial update workflow only for minor revisions. Send changes to approved codes, values, or their meanings through the standard workflow.

Quality Issues

A quality issue documents a data problem that occurs when a data element fails to meet a quality rule threshold or when a steward identifies an issue through routine work.

Quality issues may be created manually or automatically in a future phase when Data Cookbook runs a quality check.

Note: Do not create a test quality issue. Quality issues cannot be deleted.

Quality Issue Resolution

Quality issues move through the following steps but will stay with the functional area that it pertains to:

1. Document the Issue – A creator or automated check records the issue in Data Cookbook.

2. Functional Area Triage – The Divisional Data Management Subcommittee reviews the issue, confirms its scope, and identifies the appropriate next steps.

3. Correct the Cause – The Divisional Data Management Subcommittee works to correct the process, configuration, or source of the issue so it does not continue.

4. Clean Affected Data – The Divisional Data Management Subcommittee corrects affected records when needed.

5. DAIR Review – DAIR reviews the resolution and moves the issue to Resolved.

Document the cause of the issue, the records or population affected, corrective actions taken, and any follow-up work needed. This information creates a record of how the University addressed the problem and helps identify recurring issues.

Unlike other workflows in Data Cookbook, quality issues will remain with the appropriate functional area for the first four steps of the resolution workflow. 

Reviewing and Approving Items

You can find items awaiting your action through:

• The link in your Data Cookbook notification email

• My Tasks

• The appropriate approval queue

• Search and browse pages for Quality Rules, Reference Data, or Quality Issues

When reviewing an item, confirm that the information is accurate, complete, clearly named, and consistent with institutional standards. Add a comment when returning an item for revision so the creator understands what needs to be addressed.

Versioning, Retiring, Deleting, and Discarding (Quality Rules & Reference Data Only) 

Versioning or Retiring

When a quality rule or reference data needs to be modified or retired, create a new version. Add a comment explaining the change, make the needed updates, and send the new version through the appropriate workflow.

Use Submit as Editorial Update only for minor revisions, such as correcting a typo, adding context, updating a name, or changing contact information. All other changes should follow the standard workflow. 

Deleting or Discarding a Draft

Use Delete this Quality Rule/Reference Data or Discard this Version only when a draft or new version was created by mistake.

Do not delete an approved quality rule or reference data. Approved items that should no longer appear in the active list must be retired through the workflow.