How Data Quality in Retail Powers Business Outcomes in 2025

Updated January 31st, 2025

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In modern retail, where real-time decisions shape both physical and digital storefronts, maintaining data quality is crucial for effective operations.

Accurate, consistent, and reliable data fuels seamless customer experiences, optimizes inventory management, and drives business growth. Whether it’s managing vast product catalogs, tracking purchasing patterns, or streamlining supply chains, retailers rely on high-quality data to stay competitive.
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This article explores the significance of data quality in retail, its key benefits, common challenges, and practical steps to improve it.


Table of contents #

  1. What is data quality in retail?
  2. Business benefits of data quality in retail
  3. Five key considerations for ensuring data quality in retail
  4. Improving data quality in retail with seven practical steps
  5. Data quality in retail: A competitive advantage
  6. Data quality in retail: Related reads

What is data quality in retail? #

Data quality in retail refers to the accuracy, consistency, completeness, and reliability of data used within retail operations. Systematic data quality management in retail enables optimized inventory control, informed decision-making, and enhanced customer satisfaction, particularly during high-demand periods.

According to the 2023 Annual State of Data Quality survey by Monte Carlo Data, data quality issues led to revenue losses in over half of the organizations surveyed. The average impacted revenue increased from 26% in 2022 to 31% in 2023, underscoring the need for retailers to manage their data effectively.

Retailers that prioritize data quality can ensure:

  • Seamless omnichannel experiences (ensuring uniformity across online and offline channels)
  • Interoperability between retail platforms (avoiding miscommunication between systems)
  • Regulatory compliance (preventing GDPR or CCPA violations)
  • Faster, data-driven innovation (enabling predictive analytics and AI-driven recommendations)

For example, when a retailer integrates high-quality data across online and offline channels, customers receive consistent pricing and personalized recommendations regardless of where they shop.

Retailers can also leverage accurate purchase history and behavioral insights to deliver highly targeted marketing campaigns, boosting engagement and conversions.

A historical perspective on the role of data quality #


The importance of data quality is not new—history offers compelling examples of how precise data builds and transforms industries.

A striking case is Matthew Maury’s 19th-century naval data collection initiative. His systematic analysis of wind patterns and ocean currents revolutionized maritime navigation by reducing operational risks and improving voyage efficiency. By standardizing oceanographic data, Maury helped ships optimize trade routes, avoid delays, and reduce risks.

The same principle applies to retail:

  • Just as inaccurate nautical data led to lost ships and disrupted trade, poor retail data causes operational inefficiencies, stockouts, and pricing errors.
  • Just as standardized ocean data improved navigational accuracy, data consistency in retail ensures correct pricing, accurate stock availability, and seamless omnichannel integration.

Also, read → Data quality measures to know in 2025


Business benefits of data quality in retail #

Effective data quality management directly impacts a retailer’s bottom line by reducing costs, increasing efficiency, and boosting revenue. Key benefits include:

  • Improved customer loyalty: High-quality customer data enables personalized experiences, such as targeted promotions and tailored product recommendations. For instance, Netflix leverages accurate viewing data to suggest relevant shows, boosting user retention and loyalty.
  • Operational efficiency: Data errors slow down supply chains, disrupt inventory tracking, and cause fulfillment delays. Clean data eliminates redundancies and minimizes costly mistakes, like shipping delays or overstocking.
  • Cost savings: Poor data quality leads to duplicate records, manual corrections, and inefficiencies that increase operational costs. Meanwhile, accurate and consistent data minimizes redundancies and manual corrections, reducing operational costs. Retailers like Walmart save millions annually by leveraging clean data for efficient logistics.
  • Regulatory compliance: Retailers must comply with regulations like GDPR, CCPA, and PCI DSS to avoid hefty fines and maintain customer trust. High-quality data ensures accurate record-keeping and proper data governance, reducing legal risks.
  • Faster decision-making: Reliable data allows retailers to act swiftly on market trends, optimizing pricing strategies and inventory levels. Real-time, high-quality data enables dynamic pricing adjustments based on demand, competitor pricing, and consumer behavior, ensuring optimal revenue generation.
  • Increased revenue: High-quality data supports better demand forecasting, enabling retailers to stock the right products at the right time. Accurate customer segmentation enhances the effectiveness of marketing campaigns, driving higher sales.

Also, read → Data quality in modern platforms explained


Five key considerations for ensuring data quality in retail #

To ensure and maintain data quality in retail, retailers should:

  1. Set up a unified control plane for data and metadata
  2. Enable end-to-end, column-level data lineage across systems
  3. Maintain data consistency across your data estate
  4. Automate data quality checks for scalability
  5. Formalize data contracts across teams

1. Set up a unified control plane for data and metadata #


Retailers often operate with fragmented data systems, making it challenging to maintain consistency across channels. A unified control plane consolidates data and metadata, providing a single source of truth for all stakeholders.

By centralizing data and metadata management, it eliminates data silos and simplifies workflows, making it easier to maintain data quality and compliance.

2. Enable end-to-end, column-level data lineage across systems #


Data lineage tracks the flow of data across an organization, from its origin to its final destination. Cross-system, column-level data lineage that’s active and actionable is crucial for tracking inconsistencies, preventing errors, and ensuring your data estate is transparent for audits.

For instance, when a pricing discrepancy arises – a product listed at $5 online but scanned at $7 in-store, tracing the data lineage can help. You can go through the lineage mapping to pinpoint errors—whether from incorrect data input, a misconfigured promotion, or a syncing failure between systems.

3. Maintain data consistency across your data estate #


Retailers manage massive datasets across POS systems, CRMs, supply chains, and ERP systems. Ensuring data consistency—so that the same data is accurate and up-to-date across all platforms—is essential to avoid discrepancies.

Let’s look at the same example of pricing discrepancy mentioned above. If a customer notices the in-store price to be higher than the online price, that can create a frustrating experience, leading to a potentially lost sale.

By ensuring real-time data consistency, the retailer avoids mismatched information, standardizes data quality practices across departments and geographies, and improves sales

4. Automate data quality checks for scalability #


Manually identifying data errors is inefficient and error-prone, especially for large retailers handling millions of SKUs, transactions, and customer records. Automating data quality checks helps identify missing, duplicate, or incorrect data in real time, allowing retailers to scale their operations efficiently.

Automated data quality checks can catch a wide range of data quality issues – duplicate product entries, inconsistent product descriptions, incorrect/missing product attributes, misclassified products, SKU mismatches, and more.

They can also support regulatory compliance by flagging missing opt-in records or incorrectly stored customer information.

5. Formalize data contracts across teams #


Data contracts define agreements between data producers and consumers to ensure quality standards, schema integrity, and effective governance. Formalizing data contracts ensures all teams handle and process data uniformly. It also improves trust in data, as every department works with accurate, high-quality data.

Without data contracts, retailers often face misaligned expectations—a supplier might send product details in a different format than what the e-commerce team expects, leading to errors in listings, delayed launches, or even compliance risks.


Improving data quality in retail with seven practical steps #

To improve the overall data quality of your data estate, you should:

  1. Establish clear data quality standards for all data assets so that every system—whether an ERP, CRM, or POS—interprets and processes data in the same way.
  2. Set up a single source of truth – a one-stop shop for people to consolidate all tribal knowledge and create a living breathing repository of information.
  3. Enable personalized access controls governing access of sensitive information, defined based on user roles, personas, projects, data domains, and more.
  4. Use active, cross-system, column-level data lineage mapping to trace errors, understand data transformations, and ensure auditability across multiple systems (CRM, e-commerce, inventory, etc.).
  5. Automate data validation checks in all workflows to eliminate manual errors, and ensure real-time checks for duplicates, missing attributes, inconsistent pricing, incorrect classifications, and more.
  6. Automate compliance management – audit trails, versioning, risk assessments, regulatory reporting – while ensuring data security.
  7. Set up real-time alerts for flagging data anomalies and policy breaches as they happen (and not years later).

Let’s see these capabilities in action at Takealot.

How Takealot cut costs by deprecating irrelevant, outdated data assets #


Takealot, a South African e-commerce retailer, serves 1.8 million online shoppers and aspires to become the number one eCommerce provider in Africa. Central to that goal is data, and migrating to Big Query and Looker was the stepping stone to modernizing their data estate.

Retailers often accumulate large volumes of redundant, outdated, or unused data across inventory, sales, and customer databases, leading to excessive storage and processing costs. Takealot faced a similar challenge when its Analytical Engineering and Business Intelligence teams uncovered tables and models in BigQuery with little to no usage.

Deprecating them would lead to huge savings in storage and compute costs. This initiative has already resulted in $6,000 in annual savings, with continued efforts expected to generate even higher returns.

At the moment, we’re saving close to $500 per month based on some of the initial work that we’ve done. And we’ll obviously continue to build that out and come up with an overall savings this has provided us.” - Group BI Manager at Takealot


Data quality in retail: A competitive advantage #

In an industry where margins are tight and customer expectations are sky-high, data quality is a competitive advantage. As seen with Takealot, improving data management practices can lead to significant cost savings, reducing redundant storage and improving overall business agility.

By implementing automated quality checks, a unified data control plane, real-time lineage tracking, and robust data contracts, retailers can enhance customer experiences, optimize inventory, improve operational efficiency, and maximize revenue opportunities.

Start by auditing existing data practices, closing gaps, and implementing an AI-powered data platform for improved data quality in retail.



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