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Data Enrichment

Data enrichment adds value to your existing data by computing derived fields and combining multiple sources.

What is Data Enrichment?

Section titled “What is Data Enrichment?”

Enrichment transforms raw data into more useful information through:

  • Calculated fields: Add computed columns like profit margins or ratios
  • Data joins: Combine data from multiple sources
  • Aggregations: Summarize data by time periods or categories
  • Classifications: Categorize records based on rules

Enriching Data in Querri

Section titled “Enriching Data in Querri”

In Querri, you enrich data through natural language requests during your analysis:

Adding calculated fields:

"Add a profit margin column calculated as (revenue - cost) / revenue"
"Calculate the percentage of total for each category"
"Add a 'days since last order' column"

Combining data sources:

"Join this with the customer data on customer_id"
"Combine the Q3 and Q4 sales data"
"Merge orders with the product catalog"

Categorizing data:

"Classify orders as 'Large' if over $10,000, otherwise 'Standard'"
"Flag customers with no orders in the last 90 days as 'Inactive'"
"Group products by price tier"

Common Enrichment Patterns

Section titled “Common Enrichment Patterns”

Joining Tables

Section titled “Joining Tables”

Combine related data sources:

"Join orders with customers on customer_id to get customer names"
"Add product details from the catalog to each order line"
"Combine employee data with department information"

The AI agent handles the join logic automatically based on your request.

Calculated Metrics

Section titled “Calculated Metrics”

Add derived values:

"Calculate year-over-year growth for each product"
"Add a running total column"
"Compute average order value per customer"

Data Classification

Section titled “Data Classification”

Group or categorize records:

"Segment customers into High/Medium/Low value based on total purchases"
"Mark orders as 'On Time' or 'Late' based on delivery date"
"Categorize products by margin: High (>30%), Medium (15-30%), Low (<15%)"

Best Practices

Section titled “Best Practices”

Start simple: Begin with basic calculations, then add complexity as needed.

Verify results: Check that enriched values make sense before using them in reports.

Be specific: Clear instructions produce better results than vague requests.

Limitations

Section titled “Limitations”

Enrichment in Querri happens within your analysis sessions. The enriched data exists within your project steps, not as permanent changes to the original sources.

For data that needs regular enrichment:

  • Consider pre-processing before upload
  • Use database views for complex calculations
  • Schedule project refreshes to update derived data

Next Steps

Section titled “Next Steps”