Strengthening E-Commerce KPI Reliability Through Data Quality Validation

Client Overview

A leading e-commerce brand approached Infoveave with a clear objective to establish a set of brand-level KPIs that would accurately track business performance. These KPIs would cover critical metrics such as total orders, dispatch timelines, cancellation rates, and return trends.

At first glance, the client’s datasets appeared complete and well-structured. Orders were recorded, products were listed, and cancellation or return reasons were captured. However, as the project progressed and individual metrics were connected to create a holistic view, discrepancies began to surface. Metrics that should have aligned told conflicting stories, casting doubt on the reliability of the reports.

The brand needed more than just a dashboard refresh, they needed a way to ensure that every KPI was built on accurate, validated, and context-rich data.

The Challenge

While the operational data had the appearance of completeness, Infoveave’s deeper analysis revealed a different reality. The issues stemmed from subtle but critical mismatches between transactional data and master datasets. These inconsistencies were not easily visible at a glance, yet they had a profound effect on decision-making.

The immediate challenges were -

• Under-reported orders for specific categories or brands, leading to skewed sales performance views.
• Misleading operational insights around cancellations, which made it difficult to address root causes effectively.
• Inaccurate SLA and zone-level tracking due to misaligned logistics data, resulting in misplaced accountability.

For a business operating at scale, these flaws meant strategic and operational decisions were at risk of being based on unreliable insights.

Key Data Quality Issues Identified

1. Item IDs Not Matching the Product Master

Problem:

Several order records contained Item IDs that did not exist in the brand’s central product master. Without this link, essential product attributes such as category, brand ownership, and product type were missing from reports.

Impact:

• Orders without matching IDs were excluded from category and brand summaries.
• Inventory tracking was distorted because unlinked products were invisible in stock movement reports.
• Fulfillment performance tracking by seller was inaccurate.

Solution:

Infoveave implemented a data quality validation process to flag any order with missing or unmapped Item IDs. These records were paused from KPI computation until they were reconciled with the master data.

2. Cancellation Reasons Outside the Approved List

Problem:

Cancellation reasons often appeared in inconsistent free-text formats or were left blank altogether. Variations like “wrong address,” “address invalid,” and “cancelled” existed alongside the approved reason “Address Not Found.”

Impact:

• Cancellations could not be segmented into meaningful categories such as logistics issues, customer-driven cancellations, or product defects.
• Operations teams lacked clear direction on which issues to prioritise for resolution.
• Sellers faced disputes over returns due to incomplete or unclear documentation.

Solution:

A strict data quality validation check was introduced, allowing only business-approved cancellation reasons. Variations were mapped to the correct approved term where possible; others were flagged for correction before they could be included in reports.

3. Pin Code and Pickup Zone Mismatches

Problem:

Thousands of records contained pin codes that didn’t match the assigned pickup or delivery zones in the logistics master. This was often due to manual entry errors or missing values.

Impact:

• Logistics SLAs were negatively impacted because orders were routed incorrectly.
• SLA breach reports misidentified causes, blaming delays rather than incorrect routing data.
• Zone-level resource allocation reports became unreliable.

Solution:

Infoveave validated each pin code against an authoritative DQ pin–zone mapping table. Mismatched entries were flagged and excluded from SLA calculations until corrected.

4. Inconsistent Date and Number Formats

Problem:

Incoming datasets used multiple date formats (DD-MM-YYYY vs MM/DD/YYYY) and numeric formats, sometimes in scientific notation (e.g., 1E+05 instead of 100000).

Impact:

• Duplicate entries appeared because the system failed to recognise records with differently formatted dates as identical.
• Quantity and value calculations produced incorrect totals.
• Data reconciliation between systems became highly manual and error-prone.

Solution:

DQ standardisation rules were applied during the ingestion process to unify all dates and numbers into a single, consistent format before any further processing.

The Infoveave Approach: Data Quality Checkpoint

Infoveave recognised that fixing issues after they reached the dashboard was inefficient. Instead, they implemented a Data Quality (DQ) Checkpoint to intercept and validate data before KPI calculations began.

Each record was tested against four critical DQ rules:

1. The Item ID exists in the product master.
2. The cancellation reason is valid and approved.
3. The pin code matches the designated pickup/delivery zon e.
4. All dates and numeric fields follow the standard formats.

Records that failed these checks:

• Were diverted to an Exceptions Report detailing the mismatch type, order ID, and required correction.
• Did not progress to reporting until fixed and revalidated.

This approach ensured that only “Ready for Reporting” data powered the client’s dashboards.

The Results

1. Restored Trust in Dashboards

Business teams could confidently use the reports for strategic and operational planning, knowing the metrics were grounded in validated data.

2. Stable, Consistent Reporting

Metrics no longer shifted with each data load, removing confusion and avoiding last-minute adjustments to performance summaries.

3. Operational Alignment

SLAs, cancellation insights, and category-level breakdowns finally reflected the realities on the ground — enabling faster and more targeted responses to issues.

Key Takeaway

Data can appear complete yet still mislead if it doesn’t match the definitions your business depends on. Infoveave’s proactive Data Quality Checkpoint ensured that every KPI was built on a foundation of structured, reconciled, and meaningful data.

By addressing the root causes before they reached the dashboard, the client avoided days of confusion, weeks of troubleshooting, and months of decision-making risk.