Data Quality Schemes

Organizations rely on trusted data to support reporting, analytics, regulatory compliance, and operational decisions. When data contains missing values, invalid formats, duplicate records, or inconsistent values, it can lead to inaccurate reporting and business risk.

Data Quality Schemes provide a structured, scalable framework for organizing and managing multiple data quality rules within a single scheme. A scheme allows users to group related validations for tables or files, execute them together, monitor outcomes centrally, and automate recurring checks through schedules.

Data Quality Schemes support a holistic approach to data quality across different data sources by enabling consistent rule application, centralized visibility, remediation workflows, and governance controls.

Why Data Quality Schemes Matter

Without a scheme-based approach, users often manage rules individually, making execution and monitoring difficult.

Data Quality Schemes help organizations:

• Group multiple rules into a single executable unit

• Standardize business and technical validations

• Apply consistent checks across tables and files

• Automate rule execution through schedules

• Review results through dashboards and execution history

• Trigger alerts and service requests on failures

• Improve trust in enterprise data

Business Use Case

Telecom Customer Churn Data Validation

A telecom company stores customer churn data in a table named: Telecom_Customer_Churn

This dataset is used for churn prediction and retention analysis.

Business Problem

Poor data quality affects churn models because:

• Email addresses are invalid

• Tenure contains negative values

• Contract type is blank

• Monthly charges contain null values

• Duplicate customer IDs exist

Solution Using Data Quality Scheme

Create a scheme named: Customer Churn Data Validation

Add rules:

Rule Name	Validation
Validate Email Format	Email must contain a valid pattern
Validate Tenure	Tenure must be greater than 0
Validate Contract Type	Must not be blank
Validate Charges	Monthly Charges must not be null
Validate Customer ID	Must be unique

Outcome

• The scheme runs daily at 2 AM

• Failures trigger alerts

• Service Requests are created automatically

• Data teams fix issues before analytics refresh

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