Building Better Data Management Workflows in ArcGIS Pro

This is blog 2 of a series of blogs focused on helping you get back your time and grow your ArcGIS Pro skills. Read blog 1 on tailoring ArcGIS Pro to match the way you work. 

Whether you create features, maintain authoritative datasets, or prepare incoming data for production using ArcGIS Pro, you know how difficult it can be to stay ahead. The process of cleaning up data can feel never-ending. Meanwhile, the work you’d actually like to spend time on such as diving deeper into analysis, experimenting with new mapping and visualization techniques, or even learning Python, starts to feel like a luxury.

This blog looks at practical ways to reduce that rework that comes from fixing preventable data issues in ArcGIS Pro. You’ll start by identifying the problems that repeatedly slow you down, then look at ways to standardize editing, building safeguards into the geodatabase, reuse good decisions through templates, and make quality checks repeatable. Improving the way you manage data upfront, could significantly reduce the cleanup waiting for you later and free up time for the work you’d rather be doing.

Start with the Rework, Not the Tool

Before choosing a tool, identify the conditions your data must meet for its intended use. Start with the problems that repeatedly interrupt work: required fields left empty, inconsistent values, invalid combinations, missing related records, or checks performed by hand every week.

Turn those recurring issues into specific requirements. Decide whether ArcGIS Pro should provide a default, limit available choices, calculate a value, prevent an invalid edit, or flag a condition for review. This keeps the workflow focused on reducing rework rather than on implementing features for their own sake.

A few editing settings can help reduce mistakes, improve consistency, and better support workflows you use every day.

Set Up Editing for Consistent Work

Start with the editing experience. ArcGIS Pro editing options control behavior across sessions, while editor settings support project-specific controls such as snapping, field mapping, and constraint modes. A few deliberate choices can reduce accidental edits and keep common behavior predictable.

• Choose whether editing tools are always enabled or must be enabled from the Edit tab.

• Choose whether edits are saved automatically.

• Decide whether newly added layers are editable by default.

• Review attribute validation behavior so supported geodatabase restrictions are checked as users work.

For organizations that need a common starting point, administrators can establish or lock selected application settings to create a shared baseline for editing workflows while still allowing users to personalize other parts of their ArcGIS Pro experience.

This video was produced in 2022, so parts of the interface may differ from the current release, but the workflow scenarios remain relevant.

Put Guardrails in the Geodatabase

Once the editing environment is configured, move repeatable decisions into the data model. These controls work together, but each solves a different problem: domains limit individual values, subtypes organize categories of features, contingent values manage valid combinations, and attribute rules apply logic.

Domains and subtypes: standardize common choices

Domains restrict a field to a coded list or numeric range. Instead of typing a category from memory, an editor chooses an approved value. Subtypes organize features in a layer into categories that can have their own defaults and domain assignments without requiring separate feature classes.

Domains and subtypes require no scripting and are supported in file, mobile, and enterprise geodatabases. They become especially useful when templates preselect the subtype and apply the appropriate defaults, turning several manual choices into a prepared starting point.

Example: A municipality applies a domain to the road surface type field so editors can only choose from the approved list of paving materials. Each road subtype automatically applies the correct default maintenance interval, eliminating a manual lookup step entirely.

Contingent values: prevent invalid combinations

Contingent values make valid choices in one field depend on values in other fields. A domain can confirm that each value is valid on its own; contingent values help confirm that the values make sense together.

Example: After a field crew selects PVC as the pipe material, the lining field presents only the combinations defined as valid for that material. The user sees a shorter, more relevant list, and an incompatible combination is less likely to enter the workflow.

Attribute rules: automate logic at the point of work

Attribute rules add logic to geodatabase workflows. They calculate values, prevent invalid edits from being saved, or evaluate existing data against quality requirements. Rules can use ArcGIS Arcade expressions or ArcGIS Data Reviewer checks, depending on the workflow. We’ll discuss the latter later in the blog.

The three primary attribute rule types each support a different part of the data management process:

For workflows focused on preventing problems before they occur, calculation and constraint rules often provide the biggest productivity gains because they reduce repetitive data entry and stop common errors at the point of editing. Validation rules support a different goal by helping organizations identify issues that already exist in their data. In addition to authoring a user-defined attribute rule, you can create an attribute rule using a template. There are templates available to assist you with authoring attribute rules for specific workflows. Watch a short video on how to set Attribute rules from the Project Options.

Example: A water utility uses a constraint rule to require a pressure-zone value whenever a pipe is created or updated. If the value is missing, the edit cannot be saved, ensuring the issue is addressed immediately rather than becoming part of a future cleanup effort.

Note: File and mobile geodatabases support all three rule types. For enterprise geodatabases, batch calculation and validation rules require the dataset to be branch versioned and published as a feature service with the Validation enabled.

So far, the focus has been on defining what should happen. The next step is making those good decisions easier for editors to repeat.

Turn Good Decisions into Reusable Templates

Domains, subtypes, and attribute rules define what is allowed. Editing templates help users apply those decisions consistently by preconfiguring tools, default values, and related records before editing begins. That reduces repeated choices and common data-entry mistakes.

The following template types are designed to support different editing scenarios:

Note: Group and preset templates are easy to confuse. A group template creates multiple related features together; a preset template places an existing configuration of features in a new location. The video below shows a preset template in action.

Tip: One of the fastest ways to build a useful feature or table template is to create it from a feature that already has the right attribute values. Rather than configuring each field manually, you can select an existing feature with the attributes you want, right-click it in the Attributes pane, and save it as a new template with those values pre-populated.

Tip: In the Create Features pane, you can mark and group frequently used templates as favorites to help keep them visible at the top of the pane. This way, editors are not hunting through a long list at the start of every session.

Preventing errors during editing is one part of the workflow. But what if you could automatically catch issues at scale across existing or incoming datasets before they become a larger cleanup effort? That’s where ArcGIS Data Reviewer comes in handy.

Make Quality Checks Repeatable

Editing settings, templates, and attribute rules can prevent many common mistakes. When quality requirements become more complex or need to be applied across datasets, workflows, and teams, ArcGIS Data Reviewer can make those checks repeatable. Its configurable, no-code checks can be implemented as constraint or validation attribute rules to evaluate feature attribution, feature integrity, and spatial relationships.

Example: A utility validates incoming asset updates before they are loaded into its authoritative dataset. Reusable checks identify missing attributes, duplicate identifiers, and geometry issues, giving the team a consistent review process instead of a new manual checklist for every delivery.

Tip: Start with Data Reviewer’s Run Data Checks tool for a quick initial assessment of data quality. It requires minimal configuration and surfaces the most common issues fast. The same checks can be incorporated into attribute rule-based workflows to support ongoing, automated validation.

Tip: Define what high-quality data means before automating checks. Start with the business requirements for your data, then translate those requirements into measurable technical standards. Once those standards are clear, it becomes easier to identify repetitive checks that are worth automating.

Where to Go Next?

Editing settings, geodatabase controls, templates, attribute rules, and repeatable quality checks each address a different source of rework. You do not need to implement everything at once. Start with the problems that interrupt your work most often, then look for opportunities to prevent them, reduce repetitive decisions, or make quality checks easier to repeat.

Even small improvements can add up. The less time you spend correcting avoidable issues, the more time you have for analysis, mapping, visualization, and the work you actually want to do.

In the next blog in this series, we’ll look at another source of repetitive work: the tasks that genuinely need to happen again and again and where ArcGIS Pro can help automate them.