Anomaly Flagging and Transforming Data Using Bold Data Hub

In this article, we will demonstrate how to import tables from a CSV file, flag anomalies 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

Anomaly Flagging

Overview

Identifying anomalies in response and resolution times helps detect inefficiencies and potential service issues. Anomalies can also highlight customer dissatisfaction, requiring further investigation.

Approach

We use statistical thresholds to flag anomalies:

• “High Resolution Time” → Tickets with resolution times exceeding 2 standard deviations above the mean
• “Low Satisfaction” → Tickets with customer satisfaction scores below 2
• “Normal” → All other cases

SQL Query for Anomaly Flagging

SELECT 
    Ticket_ID, 
    Customer_ID, 
    Agent_ID, 
    Resolution_Time, 
    Customer_Satisfaction, 
    CASE 
        WHEN Resolution_Time > (
            SELECT AVG(Resolution_Time) + 2 * STDDEV(Resolution_Time) 
            FROM {pipeline}.sample_csc_data
        ) THEN 'High Resolution Time' 
        WHEN Customer_Satisfaction < 2 THEN 'Low Satisfaction' 
        ELSE 'Normal' 
    END AS Anomaly_Flag 
FROM {pipeline_name}.sample_csc_data;