Introduction

Data Quality Sense (DQS) is a Salesforce-native managed package for monitoring, measuring, and improving the quality of your CRM data. It runs entirely within your Salesforce org — no external integrations, no data leaving your environment.

The standard Salesforce platform doesn’t provide built-in tools for measuring data quality at scale. DQS fills this gap with automated scanning, configurable quality dimensions, and rich analytics.

A multi-step wizard for creating scan configurations. Select objects, pick fields, choose quality dimensions, and set thresholds — all through a guided UI.

Dashboards and analytics for scan results. Scores, trends, field health matrices, scan comparison, CSV exports, and AI-powered recommendations.

Automated batch scanning with flexible scheduling, data retention policies, error management, and platform event notifications.

DQS evaluates data across 6 quality dimensions:

Each dimension can be configured globally or overridden per field, with configurable thresholds and scoring.

• 100% Salesforce-native — no external dependencies or callouts
• Any SObject — scan standard and custom objects
• Automated scheduling — CRON-based recurring scans
• Data retention — configurable auto-purge of old results
• CSV export — download violation details for any dimension
• Error management — dedicated console for monitoring scan issues
• Managed package — installs cleanly with dataqualitysens namespace

• Install the package
• Configure permissions
• Create your first scan

Installation

• Salesforce org (Enterprise, Unlimited, or Developer Edition)
• System Administrator profile or equivalent permissions
• Lightning Experience enabled

1. Get the installation link

   Contact us at dataqualitysense.com to receive the managed package installation URL for your org.

2. Choose installation scope

   Select who should have access:

   • Install for Admins Only — recommended for initial setup
   • Install for All Users — if all users need immediate access
   • Install for Specific Profiles — for granular control

   

3. Approve third-party access

   The package does not require any external callouts. All processing happens within your Salesforce org.

4. Wait for installation to complete

   The installer will show progress. For larger orgs, installation may take a few minutes — you will receive an email once it completes.

   

   

5. Verify installation

   Navigate to Setup → Installed Packages and confirm Data Quality Sense appears with namespace dataqualitysens.

   

After installation, Data Quality Sense is not yet active. When you open the app for the first time, you will see a lock screen indicating that activation is required.

Click Contact Us to Activate to open a pre-filled email with your Org ID and details. You can also email us directly at hello@tucario.com.

We will enable the app remotely — no additional installation steps are needed on your side. Once activated, the lock screen disappears and the app is ready to use with 20 scans included.

The package installs all required components — custom objects, Lightning components, and permission sets. No additional configuration of these components is needed.

To upgrade to a newer version, use the same installation link — Salesforce will detect the existing package and offer an upgrade path. The installer shows your currently installed version and the new version available. Choose the same installation scope as before (or change it if needed) and click Upgrade.

Your scan definitions, results, and configuration are preserved during upgrades — no data is lost.

Permissions

Data Quality Sense ships with permission sets that control access to different parts of the application.

Full access to all features:

• Create, edit, and delete scan definitions
• Configure capabilities and thresholds
• Run and schedule scans
• View all results in Insight Studio
• Access the Error Management Console
• Manage data retention settings

Read-only access to results:

• View scan results in Insight Studio
• Export data to CSV
• View scan schedules

1. Navigate to Setup → Users → Permission Set Assignments for the target user
2. Click Edit Assignments
3. In the Available Permission Sets list, find DQS Admin or DQS User
4. Move the desired permission set to Enabled Permission Sets using the arrow button
5. Click Save

The permission sets automatically grant access to DQS custom objects:

All fields on DQS objects are visible to both permission sets. Metric results, scores, and configuration fields are read-only for DQS User.

Quick Start

1. Open DQS Builder

   Navigate to the Data Quality Sense app in Salesforce. If permissions are configured correctly, the Builder tab loads automatically.

2. Create a new definition

   Click New Definition. Give it a name (e.g., “Account Quality Check”) and select the target object — for example, Account.

3. Select fields

   The field picker shows all available fields on the selected object. Pick the fields you want to monitor. You can use the search bar to filter, and sort by field type or label.

4. Choose capabilities

   Select which quality dimensions to evaluate:

   • Completeness — are the fields populated?
   • Validity — do values match expected formats?
   • Uniqueness — are there duplicate values?
   • Timeliness — is the data current and up-to-date?
   • Consistency — are related fields logically consistent?
   • PII Detection — does free text contain personally identifiable information?

   Start with Completeness for the quickest setup.

   

5. Review and activate

   Review your configuration on the summary screen. Click Activate to move the definition from Draft to Active status.

6. Run the scan

   Go to Insight Studio and trigger a scan manually, or set up a schedule.

   

7. View results

   After the scan completes, Insight Studio shows your data quality scores with drill-down to field-level details.

• Learn about all 7 quality capabilities
• Explore Insight Studio dashboards
• Set up automated scan schedules

Builder Overview

The DQS Builder is a multi-step configuration wizard that lets you define exactly how your data quality should be measured. It guides you through selecting objects, picking fields, choosing quality dimensions, and setting thresholds.

The Builder uses a 3-zone layout:

• Sidebar (orange) — Navigation tree showing your definitions and their configuration stages (Capabilities, Scope, Fields, Configure, Review)
• Stage (green) — Main content area where you configure each step of the definition
• Guidance Panel (red) — Contextual help and guidance that adapts to your current step

1. Getting Started — Select the target Salesforce object
2. Field Selection — Pick which fields to include in the scan
3. Capability Configuration — Enable and configure quality dimensions
4. Summary — Review the complete definition before activation

Each stage validates your input before allowing you to proceed to the next step.

• Creating a Definition
• Field Selection
• Configuring Capabilities
• Definition Lifecycle

Creating a Definition

A definition is the core configuration unit in DQS. It specifies:

• Which Salesforce object to scan
• Which fields to evaluate
• Which quality dimensions (capabilities) to measure
• Optional per-field threshold overrides

1. Click “New Definition”

   In the Builder tab, click the New Definition button in the top-left corner. A creation dialog opens with object search, recent definitions, and suggested objects.

2. Select the target object

   Pick the SObject you want to scan. You can search all objects in your org, choose from Recent Definitions (objects you’ve already built definitions for), or select from the Suggested list (Account, Opportunity, Contact, Case, Lead). The dialog shows how many records and existing definitions each object has.

3. Enter a definition name

   Once you select an object, the right-hand panel shows a name field with auto-generated suggestions based on the object (e.g., “Opportunity Quality Profile”, “Opportunity Completeness Audit”). Pick a suggestion or type your own name.

4. Click ”+ Create”

   The definition is created in Draft status and opens in the Builder wizard.

After creation, you can configure:

Open any definition from the Home tab’s recent activity table, or from the Builder’s sidebar navigation tree. Definitions in Draft status can be edited — click Continue Building to resume configuration.

The delete button (trash icon) is only available when both conditions are met:

• The definition is in Draft status
• You have the DQS Admin permission set assigned

Active or completed definitions cannot be deleted — retire them to Obsolete status instead.

Deleting a definition removes all associated definition details, but preserves historical scan results for audit purposes.

Field Selection

The field picker is the second stage of the Builder wizard. It displays all available fields on your selected object in a paginated table.

• Search — Filter fields by label or API name
• Field Type Filter — Show only specific field types (Text, Number, Picklist, etc.)
• Sortable Columns — Sort by field label, API name, or field type

For each field, the picker displays:

Use the Select All link to add all available fields to your scope at once — the link shows the total field count (e.g., “Select All (71)”). To remove all selections, click Clear All. The header bar displays live counters for Selected and Available fields so you always know how many fields are in scope.

Selections persist across pages — you can select fields on page 1, navigate to page 2, and your selections are preserved. The Scope Stats panel on the right summarizes your current selection.

For a comprehensive scan, include:

• Key identifier fields — Name, Email, Phone
• Address fields — for Completeness and Validity
• Date fields — for Timeliness
• Picklist fields — for Validity and Consistency
• Free text fields — for Completeness and PII Detection

• System-generated fields (Id, CreatedDate, LastModifiedDate) — these are always populated
• Formula fields — their values are computed, not entered by users
• Encrypted fields — may not be readable by the scan engine

The Scope view is the main workspace for selecting and reviewing fields:

1. Scope step — Click the Scope step in the Builder wizard sidebar to load all fields for the target object. This is where you define which fields will be included in your data quality scan.

2. All Fields — The default view showing every field on the object. Use this tab to browse the full list, check or uncheck fields, and see each field’s label, API name, type, and whether it’s a standard or custom field. Fields you select are highlighted in the list.

3. Selected filter — Switch to this view to see only the fields you’ve already added to scope. The counter shows how many fields are selected (e.g., “Selected: 2”). Use it to quickly review your choices and remove any fields you no longer need.

4. Available filter — Shows only the fields that are not yet in your scope. The counter displays how many remain (e.g., “Available: 16”). Useful when you want to browse what’s left to add without scrolling past already-selected fields.

5. Type filter — The All Types dropdown lets you narrow the field list to a specific data type — Text, Number, Date, Picklist, Boolean, Lookup, and more. Combine it with the other filters to quickly find the exact fields you need (e.g., show only available Picklist fields).

The Scope Stats panel on the right summarizes your selection with a field count and shows a Ready to proceed indicator when at least one field is selected.

Configuring Capabilities

The third stage of the Builder wizard lets you enable quality dimensions and configure their settings. Each capability evaluates a different aspect of your data quality.

Select a capability from the list to enable it. Each capability has:

• Global configuration — Default settings applied to all fields
• Per-field overrides — Custom settings for individual fields that differ from the global defaults

Some capabilities require specific field types. For example, Timeliness only works with Date and DateTime fields. If none of the fields in your definition match, the capability displays a “Not Applicable” message and cannot be configured until you go back to scope and add compatible fields.

Most capabilities allow you to set global thresholds and then override them for specific fields. For example:

• Completeness: Set a global “expected fill rate” of 90%, but require 100% for Email fields
• Validity: Define global format rules, but set custom regex patterns for specific text fields
• Timeliness: Set a global freshness window of 30 days, but use 7 days for critical date fields

The Defaults section at the top controls global settings for the capability (e.g., Blank Handling and Placeholder Detection for Completeness). These apply to all fields unless overridden. The Field Overrides table below lists each field in scope with its current status — “Default” means the field uses the global settings.

To override a specific field, click on it in the Field Overrides table. Its status changes to indicate a custom configuration. The red arrow below points to the Reason field after applying a per-field override.

1. Enable the capability — Toggle it on from the capability list
2. Set global settings — Configure defaults that apply to all selected fields
3. Add field overrides (optional) — Click on individual fields to customize their settings
4. Review — The summary shows which fields have overrides

Click the remove button on any enabled capability to disable it. This clears all global and per-field configurations for that capability. The action requires confirmation.

Definition Lifecycle

Every scan definition progresses through a series of stages:

Draft  →  Ready  →  Active  →  Obsolete

The initial state when a definition is created. In Draft status:

• The definition can be freely edited
• Fields and capabilities can be added or removed
• No scans can be executed
• The definition is not visible in Insight Studio

A transitional state indicating the definition has been reviewed. In Ready status:

• All required configuration is complete
• The definition has passed validation
• It can be activated

The operational state. In Active status:

• Scans can be executed against the definition
• Results appear in Insight Studio
• The definition can be scheduled for recurring scans
• Editing is still possible — changes take effect on the next scan

The retired state. In Obsolete status:

• No new scans can be triggered
• Existing results remain visible in Insight Studio for historical analysis
• The definition can be reactivated if needed

Before a definition can be activated, DQS validates:

• Draft → Active: Click “Mark as Complete” on the summary screen — choose between Complete Only, Complete & Schedule, or Complete & Run Scan
• Active → Obsolete: Use the definition settings to retire it
• Obsolete → Active: Reactivate from the definition settings

Quality Capabilities

Data Quality Sense evaluates your data across 6 distinct quality dimensions (capabilities). Each dimension focuses on a different aspect of data quality and produces independent scores that roll up into an overall quality rating.

Each capability produces a score from 0 to 100 for every scanned field:

• 100 — Perfect quality for this dimension
• 75–99 — Good, with minor issues
• 50–74 — Moderate quality, attention recommended
• 0–49 — Poor quality, action required
• 0 — No data to measure (e.g., all fields empty)

Scores are aggregated:

1. Field Score — Individual field result per capability
2. Dimension Score — Average across all fields for one capability
3. Definition Score — Weighted average across all dimensions

Not all capabilities apply to all field types. DQS automatically handles non-applicable combinations:

Completeness

Completeness measures the fill rate of your fields — the percentage of records where a field contains a non-null, non-blank value.

For each field included in the scan, the Completeness strategy:

1. Counts the total number of records in scope
2. Counts how many records have a non-empty value for that field
3. Calculates the fill rate: (populated records / total records) × 100

Override the expected fill rate for individual fields. Common overrides:

• Required fields (Email, Name) → 100%
• Optional fields (Description, Notes) → 50%
• Rarely used fields → 20%

• Identify fields that are frequently left blank by sales reps
• Monitor data entry compliance for required fields
• Track completeness trends over time after data quality initiatives
• Ensure critical fields (Email, Phone) are consistently populated

Use the Bulk Config option to set the same fill rate override across multiple fields at once — useful when you have many fields that share the same completeness requirement.

Validity

Validity measures whether field values conform to expected formats, ranges, and patterns. A field can be populated (complete) but still contain invalid data — Validity catches these issues.

The Validity strategy evaluates each field value against expected rules:

1. Picklist fields — Checks that values match the defined picklist values (including metadata and live values)
2. Text fields — Validates against format patterns (e.g., email format, phone format)
3. Number fields — Validates against expected ranges
4. Date fields — Checks for reasonable date ranges

The Defaults section controls global validation options that apply to all fields:

The Field Overrides table below lists each field in scope with its current pattern and status. Fields marked “Default” use the global settings, “None” means no pattern is assigned yet.

Click on a field in the Field Overrides table to open its configuration modal. Here you can assign a specific validation pattern for that field — choose from predefined patterns (Email, URL, Fixed Length) or select Custom to enter your own regex. Each field override also lets you toggle Include blank values and Case-sensitive matching independently from the global defaults. Use the Revert to Global link to reset the field back to the global settings.

DQS uses Java-compatible regular expressions for text field validation. When you select Custom in the pattern picker, a text field appears where you can enter your own regex pattern.

See the Regex Tester for an interactive tester and a library of ready-to-use patterns for email, phone, URL, postal codes, and more.

• Ensure email fields contain properly formatted email addresses
• Verify picklist fields only contain approved values
• Detect free-text entries in fields that should use controlled vocabularies
• Validate phone number formats across regions

Uniqueness

Uniqueness measures whether field values are distinct across records. High uniqueness means each record has a different value for the field — low uniqueness indicates duplicates.

For each field, the Uniqueness strategy:

1. Collects all non-null values across records in scope
2. Identifies duplicate values
3. Calculates: (unique values / total populated values) × 100

The Defaults section controls global uniqueness options:

The Field Overrides table below lists each field with its current Case Sensitive, Include Blanks settings, and status.

Click on a field in the Field Overrides table to open its configuration modal. You can toggle Case-sensitive matching and Include blanks in uniqueness checks independently from the global defaults. Use the Revert to Global link to reset the field back to global settings.

Uniqueness analysis processes up to 40,000 records per scan. For objects with more records, results reflect a representative sample. This limit exists to prevent Salesforce heap memory overflow, since the engine builds an in-memory map of value counts per field. Fields that exceed 40,000 distinct values are flagged as high cardinality fields.

Uniqueness is most meaningful for:

• Email — should be unique per contact/lead
• Phone — often unique per person
• External IDs — must be unique by definition
• Text fields — names, descriptions

Less meaningful for:

• Boolean — only two possible values
• Picklist — limited value set by design
• Date — many records may share dates

• Detect duplicate email addresses across Contacts or Leads
• Verify external ID fields are truly unique
• Identify data entry issues where the same value is copied across records
• Monitor deduplication efforts over time

Timeliness

Timeliness measures whether date and datetime fields contain recent, up-to-date values. It answers the question: “Is this data still fresh?”

For each date field, the Timeliness strategy:

1. Reads the date/datetime value
2. Calculates the age (difference between the value and the current date)
3. Compares the age against the configured freshness window
4. Marks values older than the window as “stale”

The Defaults section controls global timeliness options:

The Field Overrides table lists each date field with its current threshold, overdue, and window settings.

Click on a field in the Field Overrides table to open its configuration modal. You can set a custom Freshness Threshold (in days), toggle Null Handling, Overdue Tracking, and Operational Range independently from the global defaults. Use the Revert to Global link to reset the field back to global settings.

Different date fields may have different freshness requirements:

• Monitor whether sales reps are keeping opportunity dates current
• Track stale contacts that haven’t been updated recently
• Ensure compliance-related dates are being maintained
• Identify “dead” records that may need cleanup or archival

Consistency

Consistency measures whether related fields contain logically compatible values. Data can be complete and valid individually, but still be inconsistent when fields contradict each other.

The Consistency strategy evaluates relationships between pairs or groups of fields:

1. Identifies configured consistency rules (field relationships)
2. For each record, checks whether the related fields satisfy the rule
3. Calculates the percentage of records passing all consistency checks

The Defaults section controls global consistency options:

The Field Overrides table lists each field with its current allowed values source, case sensitivity setting, and status.

Click on a field in the Field Overrides table to open its configuration modal. You can define allowed values for that field by adding them manually or importing from field metadata. Toggle Case-sensitive matching and configure Advanced Analysis settings (Top N Values, Min Frequency) independently from the global defaults. Use the Revert to Global link to reset.

For picklist fields, click Import from Field to load all existing values directly from the field metadata. The import dialog shows each value with a checkbox and the number of records using it, so you can select which values to treat as valid. Click Apply Selection to confirm.

• Detect opportunities where Close Date is before Created Date
• Find accounts where Billing and Shipping addresses contradict
• Identify leads where Country doesn’t match the phone number format
• Monitor cross-field data entry quality

PII Detection

PII Detection scans free-text fields for personally identifiable information that shouldn’t be stored in those fields. It helps organizations comply with data privacy regulations like GDPR, CCPA, and HIPAA.

The PII Detection strategy analyzes text content for patterns that match:

1. Social Security Numbers (SSN) — numeric patterns like XXX-XX-XXXX
2. Credit Card Numbers — 13–19 digit sequences matching card network patterns
3. Email Addresses — in fields where email storage is not expected
4. Phone Numbers — in free-text fields (not dedicated phone fields)
5. National ID Numbers — country-specific patterns

The Defaults section provides three preset groups of detection patterns:

Each pattern shows its regex expression and can be individually enabled or disabled. You can also add your own patterns in the Add Custom Pattern section by entering a regex and a label.

Click on a field in the Field Overrides table to open its configuration modal. You can select which detection patterns apply to that field — choose a preset or enable/disable individual patterns. The modal also lets you add custom patterns specific to that field. Use the Revert to Global link to reset.

DQS uses Java-compatible regular expressions to detect PII in free-text fields. See the Regex Tester for an interactive tester and a full library of PII patterns — including SSN, credit cards (Visa, Mastercard, Amex), IBAN, passport numbers, PESEL, NIP, and more.

• Audit Description and Notes fields for accidentally stored credit card numbers
• Monitor free-text fields for social security numbers
• Compliance reporting for data privacy regulations
• Pre-migration data cleansing

Insight Studio Overview

Insight Studio (DIS) is the visualization and analytics layer of Data Quality Sense. It consumes scan results and presents them as interactive dashboards, charts, and recommendations.

Insight Studio uses a 3-zone layout:

• Sidebar (orange) — Object/definition navigation, filters, and breakdown options
• Stage (green) — Main content area with scores, charts, and data tables
• Portfolio Overview (red) — Summary metrics, quick links, and contextual actions

• Navigation
• Scores & Trends
• Field Health
• Actions
• Exports

Navigation

Insight Studio provides a 6-level drill-down hierarchy:

Home → Object → Definition → Scan → Field → Dimension

The top-level view showing all scanned objects with their latest quality scores. Use this to identify which objects need attention.

The Home page also includes a Configuration panel where administrators can edit default parameters such as Scan Result Retention Days, Purge Batch Size, Purge CRON Expression, and Error Log Retention Days directly from the UI.

Shows all definitions for a selected object with the overall quality score, last scan date, status, and definition cards. Compare different scan configurations and their results side by side.

The main dashboard for a single definition. It has four tabs:

• Overview — Overall quality score, dimension score bars, Score Trend chart, and Record Scores panel
• Fields — Field Health Matrix with per-field scores across dimensions
• Scans — Full scan history with per-run results
• Exports — Download violation details as CSV per dimension and field, or take action via Create Tasks and Post Chatter

The Fields tab shows each field’s score per dimension in a color-coded matrix — red for poor, green for good. Use it to quickly spot the weakest fields.

The Exports tab lists available exports per dimension and field, with download buttons and export history.

From the Overview tab, click Compare on the Score Trend chart to compare two scans side by side — see dimension-level and field-level deltas between a baseline and comparison scan.

Detailed results for a specific scan execution. The Scans tab shows the full scan history with status indicators, scores, and per-dimension breakdowns for each run.

Results for a single field across all dimensions. Useful for understanding why a specific field is scoring poorly.

The deepest level — individual metric results for one field in one dimension. Shows the raw evaluation data.

The right-hand sidebar shows context and actions for the current definition:

• Header — Definition name, overall score with grade, scan count, and a refresh button
• Run Scan — Trigger a scan manually, with last scan date displayed below
• Analyze — Actions dropdown menu with Export Report, Create Tasks, and Post Chatter (details)
• Configure — Manage Schedule with countdown to the next scheduled run (e.g., “Next in 8h 28m”)
• Navigate — Open in Salesforce (external link to the definition record) and View Records (browse scanned records)
• Quick Fix — Highlights the worst-performing field with its score (e.g., “Description at 9.6% — worst field”) and a direct Investigate link to drill into it

A breadcrumb trail at the top of the stage area shows your current position in the hierarchy. Click any breadcrumb segment to navigate back to that level.

Scores & Trends

Every level in Insight Studio shows quality scores with visual indicators:

The definition dashboard shows dimension cards — one for each enabled capability — with:

• Numeric score (0–100)
• Grade indicator (color-coded)
• Change arrow (↑ improvement, ↓ regression, → stable)
• Sparkline showing recent trend

Small inline charts next to each score showing the last several scan results. Useful for spotting trends without navigating to detailed views.

A larger trend chart available at the definition level showing:

• Score over time for each dimension
• Overall score trend line
• Scan dates on the x-axis

Compare two specific scans to see detailed deltas:

• Dimension deltas — Which capabilities improved or regressed
• Field deltas — Which fields changed and by how much
• Select any two scans from the scan history

Some components adapt based on data availability:

• No scans yet — Shows a prompt to trigger the first scan
• Results available — Shows the full dashboard with scores and charts

The top of the definition view shows key performance indicators:

• Overall Score — Weighted average across all dimensions
• Fields Scanned — Total fields included
• Records Evaluated — Total records in scope
• Last Scan — Timestamp of the most recent scan

Field Health

The field health matrix is a grid view showing every scanned field against every enabled dimension. Each cell displays the field’s score for that dimension, color-coded by grade.

• Green cells — Score ≥ 90 (Excellent)
• Yellow cells — Score 50–89 (Fair to Good)
• Red cells — Score < 50 (Poor to Critical)
• Gray cells — Not applicable (dimension doesn’t apply to this field type)

Scan the matrix for rows with multiple red/yellow cells. These fields have quality issues across multiple dimensions.

Look for columns with many red cells. These dimensions need attention across your dataset.

Click any cell to navigate to the detailed field-dimension view, showing the specific metrics and records that contribute to that score.

• Sort by score — Show the worst-performing fields first
• Filter by dimension — Focus on a specific quality dimension
• Filter by field type — Show only Text, Number, or Date fields

Actions

The Actions menu in the Mentor panel provides a unified dropdown for taking action on records that failed quality checks. It replaces the standalone Export button with an extensible menu that currently offers three actions:

All actions share the same scope model — you choose which fields and dimensions to act on, and the system processes violations for each combination as a separate background job.

When you open any action modal, you first select the scope:

1. Fields — Choose “All fields” or select specific fields from the definition
2. Dimensions — Choose which quality dimensions to include (Completeness, Validity, Uniqueness, etc.)

The system re-evaluates violations at the time of the action, so results reflect the current state of your data — not a cached snapshot from the last scan.

Each field-dimension combination runs as a separate batch job. For example, selecting 3 fields and 2 dimensions produces 6 jobs that execute sequentially.

Creates Salesforce Task records linked to each impacted record. Tasks are assigned to record owners by default and appear in their standard Salesforce task list.

Before creating tasks, the system checks for existing open tasks on each record with a matching subject prefix. Records that already have a matching open task are skipped — this prevents duplicate tasks when you run the action multiple times.

The skip count is reported in the completion summary (e.g., “Created 45 tasks. 12 skipped (existing tasks). 0 errors.”).

1. Open the Actions menu in the Mentor panel and select Create Tasks
2. Select the fields and dimensions to include
3. Configure the task settings (subject, description, due date, priority, assignee)
4. Click Create Tasks — the modal switches to progress mode
5. Each field-dimension job shows its status (pending, running, complete, or failed)
6. When all jobs finish, you receive a custom notification with a summary
7. Click Done to close the modal

Posts a Chatter feed message on each impacted record. Optionally @mentions the record owner to trigger a Salesforce notification.

Chatter uses a 24-hour dedup window — if the current user already posted a matching quality message on a record within the last 24 hours, that record is skipped. This prevents flooding Chatter feeds when running the action repeatedly.

1. Open the Actions menu and select Post Chatter
2. Select the fields and dimensions to include
3. Edit the message template and choose whether to @mention owners
4. Click Post Messages — the modal switches to progress mode
5. When complete, the summary shows posted/skipped/error counts
6. Click Done to close the modal

All actions run as Apex batch jobs in the background. Key details:

• Batch size: 500 records per chunk (smaller than export to allow for DML operations per chunk)
• Notifications: A Salesforce custom notification is sent when the batch completes, showing created/skipped/error counts
• Partial success: If some records fail (e.g., validation rules, field-level security), successfully processed records are preserved. The error count and details are included in the summary
• Batch queue: Salesforce allows up to 5 concurrent batch jobs per org. If the queue is full, you will receive feedback before the action starts

• Task creation for data stewards — Automatically assign cleanup tasks to record owners when quality issues are detected
• Stakeholder notification via Chatter — Alert record owners about data quality problems directly on their records
• Remediation workflows — Combine with Salesforce automation (Flow, Process Builder) to route tasks to specific teams
• Audit trails — Created tasks and Chatter posts serve as a record of quality issues and when they were flagged

Exports

Insight Studio supports CSV export of violation details for all dimensions. This lets you take action on data quality issues outside of Salesforce.

Each export contains the specific records that failed quality checks for a given dimension:

Each CSV includes:

• Record ID — Salesforce record identifier
• Record Name — Display name of the record
• Field — The field that failed the check
• Current Value — The value that triggered the violation
• Violation Type — Specific type of quality issue
• Score — The field’s score for this dimension

1. Navigate to the dimension or field view in Insight Studio
2. Click the Export button
3. The export runs as a background process — you receive a custom notification in the bell icon when it completes. Each notification shows the dimension, field, and number of violations exported (e.g., “Validity — Status: 0 violations exported”)
4. Click the notification to navigate to the Field Result page in Salesforce, where the CSV is attached in the Files panel

The Field Result page shows the full context — field identity, dimension, scoring details (score, threshold, records empty/populated), and an AI Insight section. The exported CSV file is available for download in the Files panel on the right.

Below is an example of the exported CSV file contents:

• Share data quality reports with stakeholders who don’t have Salesforce access
• Import violation lists into data cleansing tools
• Create action items in project management tools
• Compliance documentation and audit trails

Processing Overview

The Processing engine is the execution layer of Data Quality Sense. It reads scan definitions created in the Builder, queries live Salesforce data, and produces quality scores visible in Insight Studio.

Scans can be triggered in three ways:

1. Manual — Click “Run Scan” in Insight Studio
2. Scheduled — Via CRON-based scheduling
3. Programmatic — Via Apex (for custom integrations)

• Scheduling
• Data Retention
• Error Management

Scheduling

DQS supports automated recurring scans via CRON-based scheduling. Once configured, scans run automatically without manual intervention.

1. Navigate to the definition in Insight Studio
2. Open the Scan Schedules modal from the sidebar
3. Click Create Schedule

4. Configure the schedule — set the frequency, time, and optionally a name:

5. Review and Save the schedule:

Under the hood, schedules use Salesforce CRON expressions. DQS provides a user-friendly UI that generates the CRON expression for you, but advanced users can also set custom expressions.

The Scan Schedules modal shows all configured schedules with their status, frequency, and next run time. The counter shows how many scheduled jobs are used out of the Salesforce limit (e.g., 2/100).

Click the edit icon to modify schedule settings. The existing schedule is replaced with the new configuration.

Use the Active toggle to temporarily pause a schedule without deleting it.

Click the delete icon to remove a schedule. Manual scans remain available.

Every scan records who triggered it:

• Manual scans — Records the user who clicked “Run Scan”
• Scheduled scans — Records “Scheduled” as the trigger source
• Timestamp — When the scan started and completed

• Only Active definitions can be scheduled
• Running scans consume Salesforce governor limits (batch Apex)
• Schedule scans during off-peak hours to minimize impact
• Multiple definitions can be scheduled at different times

Data Retention

DQS includes automated data purging to prevent unbounded growth of scan results and error logs. Retention policies are configured via Custom Metadata Types and can be adjusted by administrators.

All retention settings are stored in DQS_Configuration__mdt (Category: “Retention”):

The purge process runs as a chained batch job:

DQS_DataPurgeScheduler (CRON trigger)
  └── DQS_ErrorLogPurgeBatch
       (deletes error logs where Expires_At <= NOW)
       └── DQS_ResultPurgeBatch
            (deletes dimension results older than retention window)
            └── Cascade: Field Results + Metric Results
                 (deleted automatically via master-detail relationship)

When a DQS_Dimension_Result__c record is deleted:

• All child DQS_Field_Result__c records are automatically deleted
• All grandchild DQS_Metric_Result__c records are automatically deleted

This happens via Salesforce’s master-detail cascade — no additional batch processing is needed.

To change retention periods, edit the values directly from the Configuration panel on the DQS Home page:

Alternatively, you can update them via Salesforce Setup:

1. Navigate to Setup → Custom Metadata Types → DQS Configuration
2. Find the relevant record (e.g., Scan_Result_Retention_Days)
3. Edit the value
4. Changes take effect on the next purge run

• Check the Error Management Console for purge job errors
• Review DQS_DataPurgeScheduler in Setup → Scheduled Jobs to verify the purge is running
• Use Salesforce reports to monitor result volume over time

Error Management

The Error Monitor tab in the DQS application provides a dedicated interface for monitoring and resolving errors that occur during scan processing. It gives full visibility into batch failures, strategy errors, and platform issues — all in one place.

The Error Management Console uses a 3-zone layout:

• Filters sidebar (left) — Filter errors by Error Type, Source Class, Error Message, time range (From/To), and IP address. A Breakdowns section groups errors by type, source, or other criteria, showing counts next to each category for quick triage.
• Error log table (center) — Sortable table listing all errors with summary columns. Displays total error counts across three time windows: Last 24 Hours, Last 7 Days, and Expiring Soon. Click any row to view full details.
• Actions panel (right) — Refresh the log, create a downloadable CSV export, delete selected entries, and configure the retention period.

Each error row in the table shows the following columns:

The top of the table shows aggregate counters — Total Errors, Last 24 Hours, Last 7 Days, and Expiring Soon — giving you an at-a-glance health overview.

Click any error row to open a detail modal with the full context:

• Error ID and Type (highlighted in red for quick identification)
• Source — the originating Apex class
• uniqueIdentifier_01 — the related record or batch identifier
• Insert Type — whether the operation was an insert, update, or other DML type
• Event Time and First Event Time — timestamps for the error occurrence
• Message — full error message including the Salesforce exception type (e.g., System.AsyncException, System.DmlException)
• Stack Trace — complete Apex stack trace for debugging

DQS uses DQS_Processing_Error__e platform events to surface errors in real time. When an error occurs during batch processing:

1. The error is caught and logged
2. A platform event is published
3. The EMC receives the event and displays it

This replaces silent catch blocks with visible error reporting.

Error logs are automatically purged based on the configured retention period. The default retention is 7 days.

To change the retention period, use the Retention Configuration section in the Actions panel on the right side of the console. Enter the desired number of days in the Error Log Retention (days) field and click Save. Error logs older than the specified period will be automatically purged. Changes are deployed via the Metadata API and may take a moment to take effect.

• Check the EMC regularly after setting up new scan definitions
• Review errors after changing capability configurations
• Use error patterns to identify systematic issues (e.g., field access permissions)
• Use the Expiring Soon counter to review errors before they are automatically purged
• Export error logs to CSV before they expire if you need to keep them for longer analysis
• Contact support if platform errors persist across multiple scans

Data Model

DQS uses a set of custom objects to store definitions, results, and scheduling data. All objects use the dataqualitysens namespace prefix.

DQS_Definition__c
  ├── DQS_Definition_Detail__c (1:N)
  ├── DQS_Batch_Schedule__c (1:1)
  └── DQS_Dimension_Result__c (1:N per scan)
       └── DQS_Field_Result__c (1:N, master-detail)
            └── DQS_Metric_Result__c (1:N, master-detail)

CMTs drive the configuration of capabilities and their evaluation logic. They are package-controlled and not editable by subscribers (except where noted).

Limits & Considerations

Each Data Quality Sense activation comes with a fixed number of scans (20 by default). Every scan execution — manual or scheduled — counts toward this quota.

The definition card in Insight Studio displays a progress bar showing how many scans have been used. When the limit is approaching, the bar turns red and shows a Scan limit reached warning.

Once all scans are used, attempting to run a new scan triggers an error dialog informing you that the scan limit has been reached.

Click the Upgrade button in the error dialog to open a pre-filled email requesting additional scans. The email is addressed to hello@tucario.com and includes your Org ID automatically.

DQS runs entirely within Salesforce and is subject to standard governor limits. The processing engine is designed to work within these constraints.

More fields in a definition means more processing per record. A definition with 50+ fields will take longer than one with 10 fields.

Each enabled capability adds a dimension strategy execution per chunk. Enabling all 7 capabilities takes approximately 7x longer than enabling just one.

• Schedule during off-peak hours — Minimize impact on users
• Start small — Begin with one capability and a few key fields, then expand
• Monitor batch jobs — Use Setup → Apex Jobs to check scan progress
• Use retention policies — Prevent unbounded result growth
• Stagger schedules — Don’t schedule all definitions at the same time

Scan results consume Salesforce data storage. Each scan creates:

• 1 Dimension Result per enabled capability
• 1 Field Result per field per capability
• Multiple Metric Results per field

With data retention configured, old results are automatically purged.

Known Issues

When “Create Tasks” is run and a record’s owner is an inactive user, the task for that record fails. Other tasks in the same batch are preserved — only the affected record is skipped. The completion summary reports it as an error.

Workaround: Reassign ownership of records with inactive owners before running Create Tasks, or use the “Assign To” field in the Task modal to assign all tasks to a specific active user.

The “Post Chatter” action requires Chatter to be enabled on the org and Feed Tracking to be enabled on the specific object being scanned. If Chatter is not enabled, the action returns a clear error message. If Feed Tracking is disabled for the object, the posts fail per-record and the completion summary reports errors.

Workaround: Enable Feed Tracking for the target object in Setup > Chatter > Feed Tracking before running Post Chatter.

When “Mention Record Owner” is checked, each Chatter post is created individually rather than in bulk. For large violation sets (500+ records), processing time may be noticeably longer.

Workaround: For large datasets, consider unchecking “Mention Record Owner” for faster processing, then manually notifying owners if needed.

All actions (Export, Create Tasks, Post Chatter) re-evaluate violations against the current state of the data — not the scan results displayed in the UI. If records have been updated since the last scan, the action may find different violations than what is shown on screen.

Workaround: Run a fresh scan before taking actions if data freshness is critical.

When an action is run across multiple fields and dimensions, a record that violates in multiple combinations will receive one task or Chatter post per combination. Duplicate prevention only operates within a single field+dimension scope.

Example: A record with violations on both Email (Validity) and Phone (Completeness) will receive two separate tasks with different subjects.

If two users run the same action on the same definition at the same time, both deduplication checks pass and duplicate tasks or Chatter posts may be created.

Workaround: Coordinate with team members to avoid running the same action on the same definition simultaneously.

Scans evaluate all monitored fields, including formula and rollup summary fields. If violations are found on these read-only fields, tasks or Chatter posts are still created — but the record owner cannot directly fix the value since it is calculated.

Workaround: When configuring the scan definition, consider excluding formula and rollup fields from dimensions where violations are not remediable by the record owner.

Definitions with heavy export usage may not show all exported files in the Export History panel — the oldest files are excluded. The files still exist as Salesforce Files and can be found via the Files tab.

Workaround: Delete old exports to make room for newer ones in the panel.

The “Download All” action triggers multiple file downloads in sequence. Some browser security settings or extensions may block or warn about multiple simultaneous downloads.

Workaround: Allow multiple downloads for the Salesforce domain in your browser settings, or download files individually.

FAQ

Data Quality Sense works with Enterprise, Unlimited, and Developer editions. It requires Lightning Experience to be enabled.

No. DQS is 100% Salesforce-native. All processing, storage, and visualization happens within your Salesforce org. No data leaves your org.

It’s the managed package namespace for Data Quality Sense. All custom objects, Apex classes, and LWC components are prefixed with this namespace to avoid naming conflicts with your existing customizations.

Yes, and we recommend it. Install in a sandbox to evaluate the product, then install in production when ready.

No. DQS only reads your existing data during scans. It never modifies your business data. It creates its own custom objects to store scan configurations and results.

Use the same installation URL. Salesforce detects the existing package and offers an upgrade path. Your definitions and results are preserved.

There is no hard limit on the number of definitions. However, each definition with an active schedule consumes one Salesforce scheduled Apex slot (maximum 100 per org).

Yes. DQS can scan any standard or custom SObject that you have read access to.

No. The target object is set at creation time and cannot be changed. Create a new definition for a different object.

It depends on the number of records, fields, and capabilities. A typical scan of 10,000 records with 3 capabilities takes a few minutes. See Limits for detailed estimates.

Each scan uses one Salesforce batch job. Salesforce allows up to 5 concurrent batch jobs per org, so theoretically yes — but we recommend staggering scans to avoid governor limit issues.

The error is logged in the Error Management Console via platform events. Partial results from completed dimensions are preserved.

By default, scan results are retained for 30 days and error logs for 7 days. These can be adjusted via retention settings.

Yes. Insight Studio supports CSV export of violation details for any dimension.

A score of 0 means there was no data to measure (zero denominator) — not necessarily that all data is bad. For example, if all fields are null, Completeness can’t calculate a meaningful fill rate.

Visit dataqualitysense.com for support options, documentation updates, and contact information.