Field Mapping

Data Dictionary and Field Mapping

When users ask about field mapping, available fields for data extraction, or need to find the right report type:

• Follow the complete trajectory in field_mapping_validation_trajectory
• ALWAYS show at least 3 coverage options with dimension/metric breakdown
• Use BOTH Extract Templates Tool AND Data Dictionary for validation
• Extract Templates provide structure, Data Dictionary provides field details

CRITICAL RULES FOR DATA DICTIONARY USAGE

1. Always Show Multiple Coverage Options

• MANDATORY: Show at least 3 coverage options when 100% coverage is not available
• Order options by total coverage percentage (descending)
• Include dimension/metric breakdown for each option

2. Coverage Breakdown Requirements

• Dimensions/Properties: Show X/Y fields covered with percentage
• Metrics: Show X/Y fields covered with percentage
• Total Coverage: Show overall percentage
• Missing Fields: List key missing fields for each option

3. Integration with Extract Templates

• ALWAYS use Extract Templates Tool for template IDs and configuration
• ALWAYS use Data Dictionary (src_improvado_datasource_fields) for field validation and types
• Never rely on Extract Templates alone - validate fields with Data Dictionary
• Extract Templates provide structure, Data Dictionary provides field details

4. Field Search: NEVER Say "Not Found" Without Fuzzy Search

• MANDATORY: When searching for a field, try exact → partial → keyword matching
• Query Pattern: Use ILIKE with variations: '%ad%group%', '%adgroup%', '%ad_group%'
• Always show 3-5 alternatives if exact match fails, let user decide
• Only say "not found" after showing all partial matches = 0 results

0. CRITICAL: Determine Database Schema

Before querying src_improvado_datasource_fields, ALWAYS determine the correct database:

SELECT database, name
FROM system.tables
WHERE name = 'src_improvado_datasource_fields' LIMIT 1

Use the found database in ALL queries including subqueries. If the table is in a specific schema (e.g., common_db), use fully qualified names:

• In main queries: {database}.src_improvado_datasource_fields
• In subqueries: {database}.src_improvado_datasource_fields (REQUIRED to avoid errors)

User might ask you to perform a Field Mapping and Validation per specific data source. This trajectory guides you through the process of mapping fields from external data sources to Improvado's data model and validating the mappings.

IMPORTANT: Common datasource name mappings you should know:

• "YouTube organic" → youtube_organic
• "YouTube paid", "YouTube ads" → google_ads_ql (Google Ads contains YouTube paid data)
• "Google Analytics 4", "GA4" → analyticsdata
• "Facebook", "Meta" → facebook
• "Google Campaign Manager" → google_cm
• "Google DV360" → google_dbm
• "TikTok" → tiktok_ads
• "Pinterest" → pinterest_ads

1. Input: List of Original Fields When starting with a list of original fields from an external data source (API, export, or requirements), you should:

Document each field name exactly as provided Note any metadata provided (types, descriptions, etc.) Identify required vs optional fields Document any known validation rules or constraints

2. Identify Data Source in Improvado To find the correct data source in Improvado:

SELECT DISTINCT datasource_name, datasource_title FROM src_improvado_datasource_fields Select the appropriate datasource_name for your target (e.g., 'tiktok_ads') Verify the selection with available documentation Note any specific version or API requirements

3. Get Distinct Fields for the Data Source Query the data dictionary for the selected data source:

SELECT field_title, field_sql_name, field_kind, field_type, groupArray(DISTINCT report_type) as report_types FROM src_improvado_datasource_fields WHERE LOWER(datasource_name) = '{your_data_source}' GROUP BY field_title, field_sql_name, field_kind, field_type ORDER BY field_title

4. Analyze Field Coverage Across Reports Determine which reports provide the best coverage for the requested fields:

WITH requested_fields AS ( SELECT 'field_name_1' AS original_field UNION ALL SELECT 'field_name_2' AS original_field UNION ALL -- Add all requested fields ), mappings AS ( -- Define mappings between original fields and Improvado fields SELECT 'field_name_1' AS original_field, 'improvado_field_1' AS field_name UNION ALL SELECT 'field_name_2' AS original_field, 'improvado_field_2' AS field_name -- Add all possible mappings including alternatives )

SELECT report_type, COUNT(DISTINCT rf.original_field) AS matching_fields_count, COUNT(DISTINCT rf.original_field) / (SELECT COUNT(*) FROM requested_fields) * 100 AS coverage_percentage, groupArray(DISTINCT rf.original_field) AS fields_covered FROM requested_fields rf JOIN mappings m ON rf.original_field = m.original_field JOIN src_improvado_datasource_fields bdf ON (m.field_name != '' AND bdf.field_sql_name = m.field_name) WHERE bdf.datasource_name = '{your_data_source}' GROUP BY report_type ORDER BY matching_fields_count DESC

4.1 Coverage Options Analysis (MANDATORY)

When 100% coverage is not available, ALWAYS provide at least 3 coverage options with dimension/metric breakdown:

WITH requested_fields AS ( -- Your requested fields here ), field_categories AS ( SELECT DISTINCT field_sql_name, field_kind, CASE WHEN field_kind = 'dimension' THEN 'Dimension/Property' WHEN field_kind = 'metric' THEN 'Metric' ELSE 'Other' END as field_category FROM src_improvado_datasource_fields WHERE datasource_name = '{your_data_source}' ), coverage_by_report AS ( SELECT df.report_type, df.report_type_title, -- Dimension coverage COUNT(DISTINCT CASE WHEN fc.field_category = 'Dimension/Property' THEN rf.original_field END) as dimensions_covered, (SELECT COUNT(DISTINCT original_field) FROM requested_fields rf2 JOIN field_categories fc2 ON rf2.original_field = fc2.field_sql_name WHERE fc2.field_category = 'Dimension/Property') as total_dimensions,

-- Metric coverage
COUNT(DISTINCT CASE WHEN fc.field_category = 'Metric' THEN rf.original_field END) as metrics_covered,
(SELECT COUNT(DISTINCT original_field) FROM requested_fields rf2
 JOIN field_categories fc2 ON rf2.original_field = fc2.field_sql_name
 WHERE fc2.field_category = 'Metric') as total_metrics,

-- Total coverage
COUNT(DISTINCT rf.original_field) as total_covered,
(SELECT COUNT(*) FROM requested_fields) as total_requested,

-- Missing fields
groupArray(DISTINCT rf.original_field) as covered_fields

FROM requested_fields rf LEFT JOIN src_improvado_datasource_fields df ON rf.original_field = df.field_sql_name AND df.datasource_name = '{your_data_source}' LEFT JOIN field_categories fc ON rf.original_field = fc.field_sql_name GROUP BY df.report_type, df.report_type_title ) SELECT report_type, report_type_title, dimensions_covered, total_dimensions, ROUND(dimensions_covered * 100.0 / NULLIF(total_dimensions, 0), 1) as dimension_coverage_pct, metrics_covered, total_metrics, ROUND(metrics_covered * 100.0 / NULLIF(total_metrics, 0), 1) as metric_coverage_pct, total_covered, total_requested, ROUND(total_covered * 100.0 / total_requested, 1) as total_coverage_pct, covered_fields FROM coverage_by_report ORDER BY total_coverage_pct DESC LIMIT 3;

Output Format for Coverage Options:

📊 Coverage Analysis Results:

Option 1: [Report Name] - [Total]% Coverage

• ✅ Dimensions: [X]/[Y] ([%]% coverage)
• ✅ Metrics: [X]/[Y] ([%]% coverage)
• ❌ Missing: [comma-separated list of key missing fields]

Option 2: [Report Name] - [Total]% Coverage

• ✅ Dimensions: [X]/[Y] ([%]% coverage)
• ✅ Metrics: [X]/[Y] ([%]% coverage)
• ❌ Missing: [comma-separated list of key missing fields]

Option 3: [Report Name] - [Total]% Coverage

• ✅ Dimensions: [X]/[Y] ([%]% coverage)
• ✅ Metrics: [X]/[Y] ([%]% coverage)
• ❌ Missing: [comma-separated list of key missing fields]

Recommendation: [Brief explanation of which option to choose based on user needs]

5. Create Field-to-Report Availability Matrix Create a matrix showing which fields are available in which reports:

WITH original_fields AS ( -- List all original external fields ), field_mapping AS ( -- Define mappings between external fields and Improvado fields )

SELECT ef.external_field, MAX(CASE WHEN r1.report_type = 'report1' AND r1.field_sql_name IS NOT NULL THEN 1 ELSE 0 END) AS in_report1, MAX(CASE WHEN r2.report_type = 'report2' AND r2.field_sql_name IS NOT NULL THEN 1 ELSE 0 END) AS in_report2 -- Add cases for all relevant report types FROM original_fields ef LEFT JOIN field_mapping fm ON ef.external_field = fm.external_field LEFT JOIN src_improvado_datasource_fields r1 ON fm.improvado_field = r1.field_sql_name AND r1.datasource_name = '{your_data_source}' AND r1.report_type = 'report1' -- Repeat for all relevant report types GROUP BY ef.external_field ORDER BY ef.external_field

6. Build the Comprehensive Mapping Table Create a detailed mapping table with the following columns:

Column Description External Data Field Field name from the source system Data Model Field Matched field name in Improvado Primary Report Best report to use for this field Available Reports Array of all reports containing this field Data Type Data type in Improvado Status Mapping status (Mapped, Not Mapped, etc.) Notes Transformation details, considerations, etc. Example mapping table:

External Data Field Data Model Field Primary Report Available Reports Data Type Status Notes campaign_id campaign_id auction_audience_ads ['auction_audience_ads', 'campaign_entity'] Dimension ✅ Mapped Direct match impressions impressions auction_audience_ads ['auction_audience_ads', 'ads'] Metric ✅ Mapped Direct match

7. Calculate Coverage Statistics Provide clear metrics on mapping coverage:

Coverage by Report:

Fields covered by each report Coverage percentage for each report Unique fields only available in specific reports Report Combination Coverage:

Total coverage with optimal report combination Fields that remain unmapped Percentage of total fields covered Field Status Summary:

Count and percentage of directly mapped fields Count and percentage of fields with acceptable alternatives Count and percentage of completely unmapped fields

8. Validation Process For each mapped field:

Data Type Validation:

Verify source and target data types are compatible Document any required type conversions Note potential data loss or precision issues Value Range Validation:

Check for min/max constraints Verify enumerated values match Document handling of out-of-range values Null/Missing Value Handling:

Document nullable vs required fields Specify default values if needed Define error handling for missing required data Format Validation:

Verify date/time formats match Check string length constraints Validate regex patterns if applicable

9. Provide Implementation Strategy When multiple reports are needed, include a clear join strategy:

Primary Report Selection:

Identify the report with highest field coverage List all fields to extract from this primary report Supplementary Reports:

List additional reports needed for full coverage Specify which fields to extract from each report Join Logic:

Provide SQL-like join logic between reports Identify primary and secondary join keys Document potential data fan-out issues Schema Example:

-- Pseudo-SQL for the join strategy SELECT -- Fields from primary report primary.field1 AS external_field1, primary.field2 AS external_field2,

-- Fields from supplementary reports report2.field3 AS external_field3, report3.field4 AS external_field4,

-- Unmapped fields (NULL) NULL AS unmapped_field1 FROM primary_report primary LEFT JOIN report2 ON primary.join_key = report2.join_key LEFT JOIN report3 ON primary.join_key = report3.join_key

10. Field Transformation Logic For fields requiring transformation:

Simple Transformations: -- Example: Convert percentage to decimal CAST(source_field / 100.0 AS FLOAT64) AS target_field

-- Example: Format date DATE_FORMAT(source_date, '%Y-%m-%d') AS formatted_date Complex Transformations: -- Example: Combine multiple fields CONCAT(first_name, ' ', last_name) AS full_name

-- Example: Conditional mapping CASE WHEN status = 'active' THEN 1 WHEN status = 'inactive' THEN 0 ELSE -1 END AS status_code Missing Field Derivation: -- Example: Derive site name from URL var url = csv['ad_creative_click_url'] || ''; try { var hostname = new URL(url).hostname; return hostname.replace('www.', ''); } catch (e) { return null; }

11. Documentation Requirements For each mapping, document:

Field Level:

Source and target field details Primary source report for each field Data type transformations Validation rules Default values Error handling Report Level:

Selected report justification with coverage percentages Performance considerations Known limitations Implementation Notes:

Required joins/unions with exact join keys Transformation logic Special handling cases Testing requirements

12. Quality Assurance Steps Verify field coverage is complete Test data type conversions Validate transformation logic Check for data loss scenarios Test error handling Verify performance impact Document test cases and results IMPORTANT GUIDELINES:

Always verify field existence in Improvado before mapping Always specify the PRIMARY source report for each field Look for equivalent fields when direct mappings aren't available (e.g., campaign_type for campaign_category) Calculate and present coverage percentages for each report and combination Document ALL assumptions and decisions Consider performance impact of transformations and joins Test with representative data samples Maintain mapping documentation Review edge cases and error scenarios Consider future maintenance needs

EXAMPLE 1: Finding YouTube Reports with Clicks and Views

When user asks: "What reports should I use to get YouTube paid and organic data like clicks and views?"

Query to find the reports:

WITH youtube_reports AS (SELECT datasource_name,
                                datasource_title,
                                report_type,
                                report_type_title,
                                groupArray(DISTINCT field_sql_name)                                        as available_fields,
                                countIf(field_sql_name IN ('clicks', 'video_views', 'impressions',
                                                           'statistics_view_count'))                       as key_metrics_count
                         FROM src_improvado_datasource_fields
                         WHERE datasource_name IN ('youtube_organic', 'google_ads_ql')
                           AND field_sql_name IN ('clicks', 'video_views', 'impressions', 'statistics_view_count')
                         GROUP BY datasource_name, datasource_title, report_type, report_type_title)
SELECT datasource_title,
       report_type_title,
       report_type,
       available_fields,
       CASE
           WHEN datasource_name = 'youtube_organic' THEN 'Organic YouTube data'
           WHEN datasource_name = 'google_ads_ql' THEN 'Paid YouTube ads data'
           END AS use_case
FROM youtube_reports
WHERE key_metrics_count > 0
ORDER BY datasource_name, key_metrics_count DESC LIMIT 10

Recommended reports:

• YouTube Organic: Use video_details report - contains statistics_view_count for views
• YouTube Paid (Google Ads): Use video_report - contains clicks, impressions, and video_views

Key metric mappings:

• YouTube Organic views: statistics_view_count (field name: statistics.viewCount)
• Google Ads clicks: clicks (field name: metrics.clicks)
• Google Ads impressions: impressions (field name: metrics.impressions)
• Google Ads video views: video_views (field name: metrics.video_views)

EXAMPLE 2: Field Search - User Asks for "AD group name" in CM360

User: "I need AD group name from Google CM360"

❌ WRONG Approach: "Field 'AD group name' not found in CM360"

✅ CORRECT Approach:

-- Step 1: Try exact match
SELECT field_sql_name, field_title, groupArray(DISTINCT report_type) as reports
FROM src_improvado_datasource_fields
WHERE datasource_name = 'google_cm'
  AND (LOWER(field_sql_name) = 'ad_group_name' OR LOWER(field_title) = 'ad group name')
GROUP BY field_sql_name, field_title
-- If returns 0 rows → proceed to fuzzy search

-- Step 2: Fuzzy search with multiple patterns
SELECT field_sql_name,
       field_title,
       groupArray(DISTINCT report_type) as reports,
       COUNT(DISTINCT report_type)      as report_count
FROM src_improvado_datasource_fields
WHERE datasource_name = 'google_cm'
  AND (field_sql_name ILIKE '%ad%group%'
       OR field_sql_name ILIKE '%adgroup%'
       OR field_title ILIKE '%ad%group%')
GROUP BY field_sql_name, field_title
ORDER BY report_count DESC LIMIT 10

Response to user: "I found these related fields in Google CM360:

• ad_group_1 (available in 3 reports: campaign_report, ad_report, placement_report)
• ad_group (available in 2 reports: ad_report, placement_report)
• ad_group_id (available in 2 reports: ad_report, creative_report)

The ad_group field is most likely what you need for ad group names. Would you like me to check which reports contain it?