Data Quality

Ensuring clean and consistent data is critical for reliable analysis. This category focuses on data validation, consistency checks, and cleaning.

Missing Data & Duplicate Detection

Identifying gaps and ensuring data completeness

Purpose: Lumi helps users ensure data quality by detecting missing values and identifying duplicates within datasets. By identifying gaps or incomplete data, businesses can prevent operational errors and maintain the integrity of their analytics.

Use Case: An inventory manager might use Lumi AI to identify missing stock quantities in their warehouse data, ensuring that all products have accurate counts. Lumi enables users to check for missing data and duplicates, ensuring all records are complete and accurate.

Typical Prompts:

1. "Identify any products in the inventory that are missing stock quantities or SKU numbers."

2. "Find all duplicate customer records in the CRM system based on customer name and email."

3. "Check the sales orders from the last 12 months for missing shipping addresses or incomplete payment information."

4. "Which suppliers are missing contact details in the vendor database? Include the vendor ID and company name."

Example Output: Are there any actual pick up dates with null values with trip status completed?

Data Validation: Logic & Consistency Checks

Ensuring the data has logical consistency

Purpose: Lumi helps users validate data for logical consistency across datasets. This ensures that values within a dataset adhere to expected rules or relationships, such as dates being in the correct range or quantities not being negative.

Use Case: A warehouse manager can use Lumi AI to ensure that all shipment dates are after order dates, preventing reporting errors. Similarly, inventory records showing zero stock but a positive inventory value would result in inaccurate reporting.

Typical Prompts:

1. "Find any products where the sale date is earlier than the manufacture date."

2. "Check all purchase orders to ensure that the unit price is greater than 0 for each item."

3. "Are there any items with negative inventory?"

Example Output: Do we have any items where the inventory quantity is greater than 0 but the stock value is = 0

Example Output: Are there any components that have itself as the BOM?