In a retail context, master data management refers to the different data domains commonly used by retail businesses, like product, location and customer data. Different information systems such as point of sale (POS), ERP, marketing automation and CRM generate and store vast amounts of data that tend to live in silos, limiting its usefulness and value to the business.

In addition to being siloed, this data is also often inconsistent, incomplete, duplicated or inaccurate, especially when a business uses several different systems for a given function. A master data management solution like Profisee addresses these pain points by integrating data from disparate systems — be it from multiple CRM or ERP systems or between inventory and POS systems. Profisee then matches related records, merges them, removes duplicate records and standardizes them according to the organization’s data governance policies.

Retail MDM works with the same basic components as other industries but with a few areas of special focus. Here’s a breakdown of the main components of a retail MDM program:

1. Data Integration: Retail MDM often starts with centralizing your master data into one repository. This can be achieved through a third-party data integration tool like Azure Data Factory or through the built-in data integration feature in an MDM tool like Profisee.
2. Data Cleansing: Data cleansing often consists of merging duplicate records and checking data for completeness and accuracy. MDM tools like Profisee feature automated match and merge capabilities and let you set custom survivorship rules to retain the best attributes for each record.
3. Data Governance: Governance in retail MDM ensures that consistent policies and standards are applied across product, customer and supplier data. This includes defining roles and responsibilities for data stewards, enforcing data quality rules and tracking changes through audit logs to ensure accountability and compliance.
4. Data Modeling: Retail MDM requires flexible modeling to support hierarchies and relationships, such as category trees, product bundles, store regions or customer segments. A well-designed model helps retailers analyze performance across different groupings and optimize assortments, promotions, and operations.
5. Workflow and Stewardship: MDM platforms like Profisee offer workflow automation that helps route data changes and validations through the appropriate users. This is especially important in retail where frequent product updates, seasonal changes and supplier inputs require collaborative oversight.
6. Data Syndication and Publishing: Once mastered, data needs to be shared across systems — from inventory management to ecommerce websites. Retail MDM includes capabilities for exporting clean, standardized data to downstream applications and external partners.

Retailers face several unique challenges when implementing MDM:

• High data volume and variety: With thousands or millions of SKUs, customers and suppliers, retail data is large scale and highly diverse. Managing this complexity requires robust MDM capabilities and flexible data models.
• Data coming from multiple sources: Data may be spread across legacy systems, ecommerce platforms, physical store databases and third-party vendors. This makes integration and consistency difficult without MDM.
• Frequent changes and seasonality: Retail data is constantly changing — new products, suppliers, employee, pricing and store updates happen frequently, especially around seasonal events. MDM helps automate and govern these changes. Note the difference between master and transactional data — MDM would not, for instance, help with managing seasonal promotions.
• Duplicate and inconsistent records: Customer and product data are often duplicated or inconsistently formatted across systems. MDM tools help eliminate these redundancies and create a trusted “single version of the truth.”
• Poor data quality affecting downstream decisions: Inaccurate or incomplete data can lead to inventory issues, sourcing/purchasing inefficiencies, poor customer experiences or failed marketing campaigns. MDM ensures that data used for analytics, personalization and operations is reliable.

The end result of master data management in retail is not only clean, centralized data, but, just as importantly, data that’s usable by other systems across the enterprise, like business intelligence (BI), analytics and even the very source systems where the data originated, like ERP or CRM. This gives businesses access to accurate, timely, trustworthy data ready for a myriad of applications and initiatives, including better insights, improved customer experiences, better sales forecasting, inventory management, supply chain management, marketing campaigns and increased revenue.

MDM best practices for retail are relatively consistent with those of other industries, with some variation to account for nuances of the retail industry. Retail MDM best practices include:

• Build 360-degree views of your customers by integrating data from different systems and enriching it, when possible, with third-party data
• Implement data governance and set strong data governance policies to maintain high data quality
• Choose a flexible MDM solution that can easily adapt to ever-changing regulatory requirements for protecting consumer data and privacy
• Use MDM to create consumable data for AI tools, especially ones built for enhanced customer segmentation and personalization
• View MDM as a journey rather than a destination, fostering a culture of continuous improvement to constantly refine your MDM strategy for improved business outcomes
• Use MDM to manage product and location data for your brick-and-mortar stores, creating detailed profiles and segmentation to tailor sales and marketing strategies for specific stores

Profisee works with tools you already use to help you get more out of your investments. You can integrate product and location master data generated by SAP for Retail with data from other systems to maximize its usefulness and identify more opportunities for cross- and upselling.

MDM improves retail data management by creating a centralized, accurate and governed source of truth for key business entities such as products, customers, suppliers and stores. Here’s how:

• Enhances customer experience: By unifying customer profiles across channels, MDM helps retailers deliver personalized experiences, improve loyalty programs, drive effective cross-sell and upsell and better understand customer behavior.
• Optimizes product information: MDM ensures consistency in product names, attributes, pricing and categories across ecommerce, storefront and marketing systems. This contributes to faster time-to-market and fewer errors.
• Improves inventory and supply chain accuracy: With clean and consistent supplier and item data, MDM supports better demand forecasting, replenishment planning and fulfillment.
• Enables trusted analytics and reporting: MDM provides reliable data that feeds into BI tools like Microsoft Power BI and Azure Synapse Analytics, helping retailers make better decisions on pricing, assortment, promotions and store performance.
• Supports regulatory compliance: With strong governance and data lineage, MDM helps retailers meet industry regulations and internal audit requirements.