If you've ever spent hours rebuilding the same report for different clients or struggled to blend data from multiple ad platforms, Data Builder solves these exact challenges. It handles all the data preparation - organizing sources, selecting fields, and applying filters - so when your data reaches its destination, it's already business-ready and you can focus on what matters most.

What is Data Builder?

Data Builder is a data preparation tool within your Power My Analytics hub that allows you to:

• Create business-ready datasets from your data warehouse
• Blend data from multiple sources into unified tables
• Control exactly which fields and date ranges to include
• Create custom calculated fields using formulas or conditional rules
• Send datasets to Data Studio, BigQuery, etc.
  
• Get AI-powered insights on your data

Think of it as your data workshop where you can craft exactly the data views you need for your reports and analysis.

How to Create a Dataset

How to Create Data Tables

How to Create Blended Data Tables

How to Send a Dataset to Data Studio

Step 2: Create Your First Dataset

1. Click "+ Create Dataset"
2. Name your Dataset - Choose a clear, descriptive name (e.g., "Client ABC Reports", "Social Media Performance", "Q1 Campaign Analysis")
3. Set Available Data Range - Choose how much historical data to include:
   • This controls the maximum date range for all tables in this dataset
   • You can override this for individual tables later
     
     
   • This range is also the ceiling for every reporting tool fed from this dataset (Data Studio, Power BI, Google Sheets, Excel, and exports): a date setting in the reporting tool cannot reach data the dataset does not make available. If a report ever shows less history than you expect, widen this range first.

• Last 24 months: Ideal for year-over-year comparisons and trend analysis
  
• Custom start to date: Perfect for growing datasets that expand over time
• Last 60 days: Best for recent performance tracking with week-over-week and month-over-month comparisons
  

Choosing the Right Date Range

The date range you select depends on your reporting needs:

Running Date Ranges (e.g., Last 24 Months, Last 60 Days)

• Best for: Year-over-year comparisons, seasonal analysis, recent performance tracking
• How it works: Always shows the most recent X period of data
• Examples:
  • "Last 24 Months" for annual trends and YoY analysis
  • "Last 60 Days" for recent campaign performance and quick pivots

Growing Date Ranges (Custom Start to Date)

• Best for: Historical tracking, cumulative metrics, Data Studio reports
• How it works: Starts from a fixed date and grows as new data comes in
• Example: Set start date to when you began managing an account - your dataset will include all data from that date forward

Fixed Date Ranges

• Best for: Specific period analysis, quarterly reports
• How it works: Shows data only between specific start and end dates
• Example: Q2 2025 only (April 1 - June 30, 2025)
  

Step 3: Add Data Tables

Data tables are where your actual metrics and dimensions live.

1. Click "Create Data Table" within your dataset
2. Choose Your Sources:
   • Single Source: Select one platform (e.g., just Facebook Ads)
   • Blended: Combine multiple platforms (e.g., Facebook Ads + Google Ads + LinkedIn Ads)
     
     
     
3. Select Your Fields:
   • Use the search bar to find specific metrics
   • Browse categories: "Most Popular" (expanded by default) or "All"
   • Toggle between Dimensions and Metrics
   • Drag fields to reorder them
     
     
     
     
4. Configure Options:
   • Name your data table
   • Set sorting preferences
   • Apply filters if needed
   • Adjust row limits (default: 1,000,000)

Create Calculated Fields with Data Builder

If you've built calculated fields in Data Studio, this will feel familiar: Data Builder's Create Calculated Field screen is designed to work the same way.

Step 1: Open the Create Calculated Field Screen

1. Create or edit a data table, then open the middle Fields tab.
2. Click Create Field at the bottom of the left panel.

Step 2: Choose Dimension or Metric and Set the Field Type

1. At the top of the screen, select Create Dimension (for text or date values, like a product category or region) or Create Metric (for numeric values, like a cost ratio).
2. Name your field: Choose a clear, descriptive name (e.g., "Cost per click", "Region", "Profit Margin"). This is the name you'll see in the field list and in your reports.
3. Select the Field Type:
   • For dimensions: String or Date
   • For metrics: Number, Percent, Currency, or Duration
4. For metrics, also select the Default Aggregation: Sum or Average.

Step 3: Define Your Field's Value

1. Click Add new rule.
2. Under WHEN, select the field to evaluate, a condition, and a value:
   • Conditions for dimensions: Equal to, Contains, Starts with, Is whitespace (blank), Regex (case-insensitive), Regex (case-sensitive), or In comma-separated list
   • Conditions for metrics: Equal to (=), Greater than (>), Greater than or equal to (>=), Less than (<), or Less than or equal to (<=)
   • Click + Add condition to require multiple conditions in the same rule.
3. Under THEN, set the result:
   • For dimensions: Set to (enter the new value) or Value of (use the value of another field)
   • For metrics: enter the number to assign
4. Click Create Rule. Repeat with Add new rule to add more rules.

• For dimensions, use text functions and reference existing dimensions (e.g., CONCAT(City, ", ", Country)).
• For metrics, use basic math operators, numbers, and existing metrics (e.g., Clicks / Impressions).
• Start typing to see suggestions for functions and fields you can insert. You can also reference other calculated fields you've created (a field can't reference itself).

1. Click Create Field, then select Create Metric.
2. Name the metric "Cost per click", set the Field Type to Number, and keep the Default Aggregation as Sum.
3. Select the Formula tab and enter: SUM(Cost) / SUM(Clicks)
4. Click Save Field.

Step 4: Save and Use Your Custom Field

1. Click Save Field.
2. Your new field appears in the Calculated Fields category at the bottom of the left panel in the Fields tab.
3. Check the box to add it to your data table like any other field; it will flow through to your reports along with the rest of your data table.

AI Insights

What are AI Insights?

AI Insights is an automated analysis feature that provides:

• Performance summaries with key metrics & trends
• Anomaly detection and critical alerts
• Period-over-period comparisons
• Actionable recommendations
• Strategic guidance based on your data

Two Types of AI Insights

1. Data Table Insights

• Analyze individual data tables
• Deep single-source analysis
• Specific metric focus
• Ideal for platform-specific optimization

2. Dataset Insights

Cost: 15 credits

• Analyze entire datasets
• Cross-platform pattern identification
• Holistic performance view
• Best for strategic planning

How to Use AI Insights

Instructions for Data Tables:

1. Navigate to your data table
2. Click the Action menu (three dots)
3. Select "Get AI Insights"
4. Configure your analysis:
   • Choose date range (e.g., "Last 3 months")
   • Toggle "Include Today" if needed
   • Review credit cost
5. Click "Generate Insights"

Instructions for Datasets:

1. From the dataset overview
2. Click the Action menu
3. Select "Get AI Insights"
4. Configure and generate (same as above)

AI Insights Report Contents

Your AI-generated report includes:

Performance Summary

• Key metrics overview
• Trend identification
• Success indicators

Most Significant Changes

• Period-over-period analysis
• Notable increases/decreases
• Contributing factors

Critical Anomalies

• Outliers and unusual patterns
• Specific dates and metrics
• Impact assessment

Recommendations

• Optimization opportunities
• Risk mitigation strategies
• Areas for further investigation
• Budget allocation suggestions

AI Credits System

• Each account includes monthly credits based on plan
• Additional credit packages available
• Credits refresh monthly
• View remaining credits before generating insights

Best Practices for AI Insights

1. Field Selection is Key: Choose 5-10 strategic metrics that tell a complete performance story. Too few and you miss patterns; too many and insights lose focus.
   

2. Use Cache Strategically: Running the same analysis twice? The second run is free. Plan your date ranges to maximize credit value.
   

3. Start with Problem Areas: Generate insights for underperforming channels first - AI excels at finding hidden issues and missed opportunities.
   

4. Dataset vs. Data Table: Run Dataset Insights for strategic planning, Data Table Insights for tactical optimization.
   

5. Date Range Sweet Spots:
   

1. monthly, quarterly, yearly for reports
   
2. 60-90 days for trend analysis
   
3. 14-60 days for campaign optimization
   
4. 7 days for troubleshooting sudden changes

6. Read Between the Lines: When AI flags an anomaly on a specific date, that's your cue to check what changed - new campaign, tracking issue, or market event.
   

7. Export and Share: PDF exports make perfect client reports. Add your recommendations to AI findings for comprehensive analysis.