18.1 Filtering Data

Filtering allows you to focus on specific subsets of your data, making it easy to find what matters most. This section shows you how to create powerful filters to view exactly the rows you need.

Overview: Why Filtering Matters

Without Filters

Problem: Information overload

Imagine you have a Taible with 10,000 leads. Someone asks: "Show me high-priority leads from enterprise companies that haven't been contacted yet."

Manual approach:

Scroll through 10,000 rows
Visually scan each row
Note qualifying rows
Time: Hours ✗

With Filters

Solution: Instant precision

Same 10,000 lead Taible. Same question.

Filter approach:

Set filter: priority = "high"
AND: company_size = "enterprise"
AND: last_contact_date is empty

Result: 47 matching rows Time: 2 seconds ✓

Filters = Your data search engine

Part 1: Filter Basics

Accessing Filters

Filters are accessed from the Taible toolbar at the top of your grid.

Taible Toolbar

10,000 rows

Click the Filter button to open the filter configuration dialog.

Steps:

Open your taible - Navigate to any taible view
Click the "Filter" button - Located in the toolbar with a filter icon
Filter dialog opens - A dialog appears where you can configure your filters

Configure Filters

Configure filters to show only rows that match specific conditions

No filters configured

Click "Add Filter" to get started

The dialog shows:

Instructions about what filters do
An empty state if no filters are configured
An "Add Filter" button to create your first filter
Cancel and Apply buttons at the bottom

Creating Your First Filter

Let's create a simple filter to show only rows where enrichment failed.

Step 1: Click "Add Filter"

When you click the "Add Filter" button, a new filter configuration appears with three main fields:

Field - Which column to filter on
Operator - How to compare the values
Value - What to compare against (for operators that need it)

Step 2: Select the Field

Click the "Field" dropdown and choose which column you want to filter. For this example, select Company Enrichment.

Step 3: Select the Operator

Click the "Operator" dropdown and choose how you want to compare values. For showing failures, select Equals.

Step 4: Enter the Value

In the "Value" field, type FAILED (this is what appears when a column execution fails).

Configure Filters

Configure filters to show only rows that match specific conditions

Value

Your filter is now configured:

Field: Company Enrichment
Operator: Equals
Value: FAILED

Step 5: Apply the Filter

Click the "Apply Filters" button at the bottom of the dialog.

Showing 8 of 100 rows (filtered)

Email	Company Name	Company Enrichment
john@example.com	Acme Corp	Failed
jane@company.com	Tech Solutions	Failed
bob@business.net	Global Industries	Failed

All visible rows have Company Enrichment = Failed ✓

The grid updates to show only the 8 rows (out of 100) where Company Enrichment equals FAILED. Notice:

The Filter button now shows "1 Filter" to indicate an active filter
The grid displays: "Showing 8 of 100 rows (filtered)"
All visible rows have a red "Failed" badge in the Company Enrichment column

Clearing Filters

To remove filters and show all rows again:

Click the "Filter" button again to reopen the dialog
Remove filters using one of these options:
- Click the trash icon next to a specific filter to remove just that one
- Click "Clear All Filters" to remove all filters at once
- Delete all filter rows and click "Apply Filters"
Grid shows all rows - After applying, all 100 rows are visible again

Part 2: Filter Operators

Understanding Operators

Operators determine how the system compares your column value to your filter value.

There are 13 operators available for different types of comparisons.

Equality Operators

Equals

Use when: You want an exact match

Examples:

status = "complete"
→ Shows rows where status is exactly "complete"

lead_score = 100
→ Shows rows where lead_score is exactly 100

is_qualified = true
→ Shows rows where is_qualified is true

Common uses:

Specific status values
Exact numeric matches
Boolean true/false fields

Not Equals

Use when: You want everything except a specific value

Examples:

status ≠ "complete"
→ Shows all rows except those with status "complete"

company_size ≠ empty
→ Shows rows that have a company_size value

assigned_to ≠ "john@company.com"
→ Shows rows not assigned to John

Common uses:

Exclude specific values
Find non-empty entries
Negative matching

Comparison Operators (Numbers & Dates)

Greater Than

Use when: You want values above a threshold

Examples:

lead_score > 80
→ Shows rows where lead_score is greater than 80

employee_count > 500
→ Shows companies with more than 500 employees

created_date > "2024-10-01"
→ Shows rows created after October 1st

Greater Than or Equal

Use when: You want values at or above a threshold

Examples:

lead_score >= 80
→ Shows rows where lead_score is 80 or higher (includes 80)

budget >= 10000
→ Shows deals worth $10,000 or more

Less Than

Use when: You want values below a threshold

Examples:

lead_score < 50
→ Shows rows where lead_score is less than 50

employee_count < 10
→ Shows small companies (fewer than 10 employees)

Less Than or Equal

Use when: You want values at or below a threshold

Examples:

priority_score <= 3
→ Shows low priority items (score 3 or less)

Text Operators

Contains

Use when: You want text that includes a substring (case-insensitive)

Examples:

email CONTAINS "@gmail.com"
→ Shows rows with Gmail addresses

company_name CONTAINS "Tech"
→ Shows companies with "Tech" anywhere in name
  (matches "Acme Tech", "TechStart", "BioTechnology")

notes CONTAINS "follow up"
→ Shows rows mentioning "follow up" in notes

Tip: Very flexible for text searches!

Does Not Contain

Use when: You want text that does NOT include a substring

Examples:

email NOT CONTAINS "@company.com"
→ Shows external emails (not company internal)

error_message NOT CONTAINS "timeout"
→ Shows errors that aren't timeouts

List Operators

In List

Use when: A value matches any item in a list

Examples:

status IN ["pending", "in-progress", "review"]
→ Shows rows with any of these statuses

priority IN [1, 2]
→ Shows high and medium priority (1 or 2)

assigned_to IN ["alice@co.com", "bob@co.com"]
→ Shows rows assigned to Alice or Bob

When to use: You have multiple acceptable values

Value input: You'll see an array editor where you can add items

Not In List

Use when: A value does NOT match any item in a list

Examples:

status NOT IN ["complete", "cancelled", "archived"]
→ Shows only active statuses

industry NOT IN ["Adult", "Gambling", "Crypto"]
→ Excludes specific industries

Special Operators

Is Empty

Use when: You want to find fields with no value (null or empty)

Examples:

last_contact_date IS_EMPTY
→ Shows leads that have never been contacted

email_sent IS_EMPTY
→ Shows rows where email hasn't been sent yet

company_data IS_EMPTY
→ Shows rows missing enrichment data

Note: No value needed - the operator just checks if the field is empty

Common uses:

Find incomplete data
Identify pending tasks
Locate failures that need retry

Is Not Empty

Use when: You want to find fields that have a value

Examples:

phone_number IS_NOT_EMPTY
→ Shows leads with phone numbers

error IS_NOT_EMPTY
→ Shows rows that have errors

completed_date IS_NOT_EMPTY
→ Shows completed items

Note: No value needed

Contains Any Of

Use when: Text contains at least one of multiple substrings

Examples:

tags CONTAINS_ANY ["vip", "enterprise", "urgent"]
→ Shows rows tagged with any of these

description CONTAINS_ANY ["api error", "timeout", "failed"]
→ Shows rows mentioning any error type

Value input: Array of strings to search for

Part 3: Multiple Filter Conditions

Combining Filters with AND

Use when: All conditions must be true

Example: High-value, uncontacted leads

Configure Filters

Configure filters to show only rows that match specific conditions

Value

This operator checks if the field is empty or not - no value is needed.

This filter configuration shows:

Filter 1:

Field: Lead Score
Operator: Greater Than or Equal
Value: 80

Combination: AND

Filter 2:

Field: Last Contact Date
Operator: Is Empty
(No value needed)

Result: Only rows where BOTH conditions are true:

Lead score is 80 or higher AND
Last contact date is empty

Combining Filters with OR

Use when: Any condition can be true

Example: High or medium priority

Filter 1:

Field: Priority
Operator: Equals
Value: "high"

Combination: OR

Filter 2:

Field: Priority
Operator: Equals
Value: "medium"

Result: Rows where priority equals "high" OR priority equals "medium"

Tip: For this specific case, you could also use the In List operator:

Field: Priority
Operator: In List
Value: ["high", "medium"]

Complex Logic Examples

Example 1: Enterprise Leads Ready for Outreach

Goal: Large companies, high score, not yet contacted

Filters:

employee_count >= 500
- AND
lead_score > 70
- AND
last_contact_date IS_EMPTY

Result: Large, qualified, uncontacted leads ✓

Example 2: Rows Needing Attention

Goal: Failed or pending review

Filters:

status = "failed"
- OR
status = "needs_review"

Result: All rows requiring action

Example 3: Recent High-Value Orders

Goal: Large orders from last 7 days

Filters:

order_total > 10000
- AND
order_date > "2024-10-24"

Result: Recent big orders ✓

Example 4: External Emails, Not Gmail

Goal: Non-company, non-Gmail addresses

Filters:

email NOT CONTAINS "@company.com"
- AND
email NOT CONTAINS "@gmail.com"

Result: Other external providers

Part 4: Advanced Filtering Techniques

Filtering by Cell State

You can filter cells based on their execution state.

Finding cells in specific states:

Failed executions:

Field: [column_name]
Operator: Equals
Value: FAILED

Pending processing:

Field: [column_name]
Operator: Equals
Value: IDLE

Completed successfully:

Field: [column_name]
Operator: Equals
Value: DONE

Skipped cells (condition not met):

Field: [column_name]
Operator: Equals
Value: SKIPPED

Note: These state values correspond to what appears in the cell status badges.

Part 5: Practical Filtering Workflows

Workflow 1: Daily Lead Review

Goal: Review qualified, uncontacted leads

Steps:

Open Leads taible
Click Filter button
Add three filters:
- lead_score >= 75
- AND last_contact_date IS_EMPTY
- AND status = "qualified"
Click "Apply Filters"
Work through the filtered list:
- Review each lead
- Make contact
- Update status
Clear filter when done to see all leads again

Workflow 2: Error Debugging

Goal: Find and fix all failures in a specific column

Steps:

Open taible
Click Filter
Create failure filter:
- Field: [problem_column]
- Operator: Equals
- Value: FAILED
Click "Apply Filters"
Click first failed cell to view error details
Identify error pattern - Are multiple rows showing the same error?
Fix the configuration in the column settings
Select all filtered rows:
- Click the checkbox in the row header
- Right-click → Re-run Selected
Clear filter to verify all rows processed successfully

Workflow 3: Export Specific Segment

Goal: Export a subset of data for reporting

Steps:

Apply filters to select your segment:
- created_date > "2024-10-01"
- AND created_date < "2024-11-01"
- AND status = "complete"
Select all filtered rows:
- Click checkbox in the header
Export:
- Right-click → Export Selected
- Choose format (CSV)
- Save file
Result: Clean export of exact segment needed ✓

Workflow 4: Bulk Assignment

Goal: Assign specific leads to a team member

Steps:

Filter for target leads:
- industry = "Technology"
- AND lead_score > 70
- AND assigned_to IS_EMPTY
Select filtered rows (click header checkbox)
Bulk edit the assigned_to column:
- Right-click column header
- Update value
- Set to: alice@company.com
Clear filter to see assignments across full list

Part 6: Filter Tips and Best Practices

Tip 1: Build Filters Incrementally

Don't try to create the perfect filter on the first try

Better approach:

Step 1: Start broad
Filter: status = "active"
Result: 500 rows

Step 2: Add constraint
+ AND lead_score > 50
Result: 200 rows

Step 3: Refine further
+ AND industry = "Technology"
Result: 45 rows ✓

Perfect!

Start with one simple filter, see the results, then add more conditions to narrow down.

Tip 2: Use "Is Empty" for Missing Data

Find gaps in your data:

Want: Leads without phone numbers
Filter: phone_number IS_EMPTY
Result: 127 leads

Action: Run phone enrichment on these

Want: Failed enrichments
Filter 1: company_data IS_EMPTY
Filter 2: AND email IS_NOT_EMPTY
Result: 23 failed enrichments

Action: Retry with different service

Tip 3: Remember Field Types

Number comparisons: Use GT, LT, GTE, LTE

✅ employee_count > 100
❌ employee_count CONTAINS "100"

Text searches: Use CONTAINS, IN

✅ email CONTAINS "@gmail"
❌ email > "@gmail" (doesn't make sense)

Booleans: Use EQUALS

✅ is_qualified = true
✅ is_qualified = false

Tip 4: Use "In List" for Multiple Values

Instead of multiple OR conditions:

Verbose way (works but clunky):

status = "pending"
OR status = "in-progress"
OR status = "review"

Better way (cleaner):

status IN ["pending", "in-progress", "review"]

Tip 5: Clear Filters After Use

Don't leave filters active unintentionally

Problem: Forgot filter is active

You: "Where are my rows? I only see 10 but should have 1000!"
→ Filter still active from yesterday ✗

Solution: Always clear filters when done

After filtering for specific task:
→ Clear filter
→ Verify full dataset visible
→ Avoid confusion later ✓

Check the Filter button - if it shows a number, filters are active.

Quick Reference: Filter Operators

Operator	Description	Example
Equals	Exact match	`status equals "qualified"`
Not equals	Everything except this	`status not equals "closed"`
Greater than	Values higher	`score > 80`
Greater or equal	Values this or higher	`score >= 75`
Less than	Values lower	`age < 30`
Less or equal	Values this or lower	`age <= 25`
Contains	Text includes substring	`name contains "Smith"`
Does not contain	Text doesn't include	`email not contains "test"`
In list	Matches any in list	`status in ["pending", "active"]`
Not in list	Doesn't match any in list	`status not in ["closed", "cancelled"]`
Is empty	Field has no value	`notes is empty`
Is not empty	Field has a value	`email is not empty`

Summary: Filtering Data

You now know how to filter effectively:

✅ Filter basics:

Click Filter button in toolbar
Add filter rules (field, operator, value)
Apply to show matching rows
Clear to show all rows

✅ 13 operators available:

Equality: Equals, Not Equals
Comparison: Greater Than, Greater or Equal, Less Than, Less or Equal
Text: Contains, Does Not Contain, Contains Any Of
Lists: In List, Not In List
Special: Is Empty, Is Not Empty

✅ Multiple conditions:

Combine with AND (all must be true)
Combine with OR (any can be true)
Build complex logic incrementally

✅ Common use cases:

Find failed cells for debugging
Filter by cell state (Failed, Done, Idle)
Find incomplete data (Is Empty)
Segment for export or bulk operations
Daily review workflows

✅ Best practices:

Build filters incrementally
Use "Is Empty" for finding gaps
Match operator to field type
Use "In List" for multiple values
Clear filters when done

Next Steps

You've completed Section 18.1: Filtering Data!

Next: Continue to Chapter 19: Dashboards and Reporting to learn about visualizing your automation data!

Master your data! 📊

18.1 Filtering Data ​

Overview: Why Filtering Matters ​

Without Filters ​

With Filters ​

Part 1: Filter Basics ​

Accessing Filters ​

Taible Toolbar

Configure Filters

Creating Your First Filter ​

Configure Filters

Clearing Filters ​

Part 2: Filter Operators ​

Understanding Operators ​

Equality Operators ​

Equals ​

Not Equals ​

Comparison Operators (Numbers & Dates) ​

Greater Than ​

Greater Than or Equal ​

Less Than ​

Less Than or Equal ​

Text Operators ​

Contains ​

Does Not Contain ​

List Operators ​

In List ​

Not In List ​

Special Operators ​

Is Empty ​

Is Not Empty ​

Contains Any Of ​

Part 3: Multiple Filter Conditions ​

Combining Filters with AND ​

Configure Filters

Combining Filters with OR ​

Complex Logic Examples ​

Example 1: Enterprise Leads Ready for Outreach ​

Example 2: Rows Needing Attention ​

Example 3: Recent High-Value Orders ​

Example 4: External Emails, Not Gmail ​

Part 4: Advanced Filtering Techniques ​

Filtering by Cell State ​

Part 5: Practical Filtering Workflows ​

Workflow 1: Daily Lead Review ​

Workflow 2: Error Debugging ​

Workflow 3: Export Specific Segment ​

Workflow 4: Bulk Assignment ​

Part 6: Filter Tips and Best Practices ​

Tip 1: Build Filters Incrementally ​

Tip 2: Use "Is Empty" for Missing Data ​

Tip 3: Remember Field Types ​

Tip 4: Use "In List" for Multiple Values ​

Tip 5: Clear Filters After Use ​

Quick Reference: Filter Operators ​

Summary: Filtering Data ​

Next Steps ​

18.1 Filtering Data

Overview: Why Filtering Matters

Without Filters

With Filters

Part 1: Filter Basics

Accessing Filters

Creating Your First Filter

Clearing Filters

Part 2: Filter Operators

Understanding Operators

Equality Operators

Equals

Not Equals

Comparison Operators (Numbers & Dates)

Greater Than

Greater Than or Equal

Less Than

Less Than or Equal

Text Operators

Contains

Does Not Contain

List Operators

In List

Not In List

Special Operators

Is Empty

Is Not Empty

Contains Any Of

Part 3: Multiple Filter Conditions

Combining Filters with AND

Combining Filters with OR

Complex Logic Examples

Example 1: Enterprise Leads Ready for Outreach

Example 2: Rows Needing Attention

Example 3: Recent High-Value Orders

Example 4: External Emails, Not Gmail

Part 4: Advanced Filtering Techniques

Filtering by Cell State

Part 5: Practical Filtering Workflows

Workflow 1: Daily Lead Review

Workflow 2: Error Debugging

Workflow 3: Export Specific Segment

Workflow 4: Bulk Assignment

Part 6: Filter Tips and Best Practices

Tip 1: Build Filters Incrementally

Tip 2: Use "Is Empty" for Missing Data

Tip 3: Remember Field Types

Tip 4: Use "In List" for Multiple Values

Tip 5: Clear Filters After Use

Quick Reference: Filter Operators

Summary: Filtering Data

Next Steps