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On this page
  • Data Search & Filtering
  • Retrieving specific details such as categories, descriptions and basic types.
  • Basic Aggregation
  • Provide essential aggregations like average, sum, min, max, and count.

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  1. Using Lumi
  2. Lumi Use Cases

Data Exploration

PreviousLumi Use CasesNextBusiness Metrics

Last updated 5 months ago

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This category helps users investigate and better understand their datasets through basic statistics, relationships, and visualizations.

Data Search & Filtering

Retrieving specific details such as categories, descriptions and basic types.

Purpose: To provide a high-level summary and quick access to key data points, such as categories, counts, and relationships along with the ability to apply filters, search for specific information, or focus on particular subsets of data.

Use Case: To give users a high-level overview of their available data, helping them quickly understand the structure, types of categories, and key fields for inventory, sales, or product analysis. This is especially useful when beginning data analysis or exploratory data queries.

Typical Prompts:

  • "List the total number of products in each category."

  • "For customer ABC, show all orders with status 'Not Paid Yet'."

  • "List all products with stock levels below XX units."

Example Output: What are all the item categories and ids we have?

Example Output: Who are all our distinct customers?

Example Output: For CUST002, are there any orders with order status 'Not Paid Yet'? Include relevant details.

Basic Aggregation

Provide essential aggregations like average, sum, min, max, and count.

Purpose: Lumi AI provides powerful aggregation capabilities, enabling users to calculate essential business statistics such as maximum, minimum, sum, average, profit, revenue, and costs. This allows business users to quickly analyze performance, financial metrics, and operational efficiency without requiring technical expertise in SQL or Python.

Use Case: A procurement manager at a manufacturing company wants to analyze total procurement costs, average supplier costs, and identify the best and worst-performing suppliers.

Typical Prompts:

  1. "What was the total revenue and average profit for each product category in YTD?"

  2. "What is the highest operational cost category by department for Q1 2024."

  3. "Give me the total procurement costs for all suppliers in 2023, and identify the supplier with the lowest cost."

  4. "What is the average revenue per region in Q2 2024, and which region had the highest revenue?"

Example Output: What is the average weekly quantity sold of item 1001 in 2023?