Outlier Detection and Transforming Data Using Bold Data Hub

In this article, we will demonstrate how to import tables from a CSV file, detect outliers 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

Outlier Detection

Overview

Detecting anomalies in ticket resolution patterns helps identify potential issues such as workload imbalance, inefficiencies, or unusual spikes in customer complaints. This can be achieved by analyzing:

• The number of tickets resolved by each agent
• Unusually high service durations
• Sudden spikes in customer complaints

Approach

We calculate the daily ticket count and use a 7-day rolling average to detect anomalies in ticket volume trends.

SQL Query for Outlier Detection

SELECT 
    Ticket_Creation_Date, 
    COUNT(Ticket_ID) AS Daily_Ticket_Count, 
    AVG(COUNT(Ticket_ID)) OVER (
        ORDER BY Ticket_Creation_Date 
        ROWS BETWEEN 6 PRECEDING AND CURRENT ROW
    ) AS Rolling_Avg_7_Days 
FROM {pipeline_name}.sample_csc_data 
GROUP BY Ticket_Creation_Date 
ORDER BY Ticket_Creation_Date;