Categorizing Ticket Types and Transforming Data Using Bold Data Hub

In this article, we will demonstrate how to import tables from a CSV file, categorize ticket types through transformations, and move the cleaned data into the destination database using Bold Data Hub. Follow the step-by-step process below.

Sample Data Source:
Sample CSC Data

Step-by-Step Process in Bold Data Hub

Step 1: Open Bold Data Hub

• Click on the Bold Data Hub.

Step 2: Create a New Pipeline

• Click Add Pipeline in the left-side panel.
• Enter the pipeline name and click the tick icon.

Step 3: Choose the Connector

• Select the newly created pipeline and opt for the CSV connector. You can either double-click or click on the Add Template option to include a template.

Step 4: Upload Your CSV File

• Click the “Upload File” button to select and upload your CSV file.

Step 5: Set the Properties

• Copy the file path and paste it into the filePath property field.

Step 6: Save and Choose the Destination

• Click Save, choose the destination, and confirm by clicking the Yes button.

Note: On-Demand Refresh will be triggered when the pipeline is saved. If needed, the pipeline can be scheduled in the Schedules tab.

Step 7: View Logs and Outputs

• Click the pipeline name in the left-side panel and switch to the Logs tab to view logs.

Step 8: Apply Transformations

• Go to the Transform tab and click Add Table.

• Enter the table name to create a transform table for customer satisfaction summary.

Note: The data will initially be transferred to the DuckDB database within the designated {pipeline_name} schema before undergoing transformation for integration into the target databases. As an illustration, in the case of a pipeline named “customer_service_data”, the data will be relocated to the customer_service_data table schema.

Learn more about transformation here

Categorizing Ticket Types

Overview

If ticket descriptions or categories are unstructured or inconsistent, standardizing them into predefined categories can improve data clarity and reporting. Common categories include “Billing Issue,” “Technical Support,” and “General Inquiry.”

Approach

We use a CASE statement to categorize tickets based on the Ticket_Category and Issue_Description fields. This ensures uniform classification of ticket types for better analysis.

SQL Query for Categorizing Ticket Types

SELECT 
    Ticket_ID,
    Customer_ID,
    Customer_Name,
    Ticket_Allocation_Timestamp,
    Ticket_Status,
    Priority,
    Region,
    City,
    Country,
    Ticket_Cost,
    Phone,
    CASE 
        WHEN LOWER(Ticket_Category) LIKE '%billing%' THEN 'Billing Issue'
        WHEN LOWER(Ticket_Category) LIKE '%technical%' OR LOWER(Issue_Description) LIKE '%error%' THEN 'Technical Support'
        WHEN LOWER(Ticket_Category) LIKE '%general%' OR LOWER(Issue_Description) LIKE '%inquiry%' THEN 'General Inquiry'
        ELSE 'Other'
    END AS Standardized_Ticket_Category
FROM {pipeline_name}.sample_csc_data;