{"id":37538,"date":"2026-10-05T10:13:00","date_gmt":"2026-10-05T08:13:00","guid":{"rendered":"https:\/\/msfabric.pl\/?p=37538"},"modified":"2026-10-05T19:38:29","modified_gmt":"2026-10-05T17:38:29","slug":"the-master-data-layer-in-microsoft-fabric-how-to-standardize-customers-products-and-suppliers","status":"publish","type":"post","link":"https:\/\/msfabric.pl\/en\/blog\/fabric-news\/the-master-data-layer-in-microsoft-fabric-how-to-standardize-customers-products-and-suppliers","title":{"rendered":"The Master Data Layer in Microsoft Fabric: How to Standardize Customers, Products, and Suppliers"},"content":{"rendered":"\n

The same customer may appear under three different names in a sales system, a product may have different identifiers in the warehouse and online store, and a supplier may have separate records across several subsidiaries. Management, however, needs a consistent view of the entire business. These discrepancies make it harder to assess profitability and plan purchases. A master data layer in Microsoft Fabric<\/a> provides a common foundation for these analyses. Building it requires agreed-upon definitions, data quality rules, and clear accountability for data.<\/p>\n\n\n\n

What Is Master Data, and Why Does It Determine the Quality of Business Analytics?<\/strong><\/h2>\n\n\n\n

Master data describes customers, products, suppliers, and organizational units. Transactional data describes an event, while master data identifies who or what the event relates to. An invoice documents a sale, but the customer record makes it possible to assign that sale to the correct buyer and segment. When records are inconsistent, even accurate amounts can end up in the wrong categories. A company may then see understated sales to a corporate group, an inflated customer count, or an incomplete picture of purchases from a supplier. A shared master data layer brings consistency to these relationships and definitions across reports.<\/p>\n\n\n\n

What Role Does Microsoft Fabric Play?<\/strong><\/h2>\n\n\n\n

Microsoft Fabric provides an environment for integrating and processing<\/a> data from different systems, as well as analyzing it. OneLake<\/a> provides a shared environment for analytical data that can be made available to additional teams. The master data layer still needs to be designed: identifiers, matching rules, and procedures for resolving discrepancies must be defined. A three-layer structure can help: source data, cleansed data, and data prepared for business use. This approach preserves the original records while allowing consistent master records to be created for Power BI reports. Fabric also allows selected data assets to receive a \u201cMaster data\u201d designation, identifying them as an authoritative source of organizational data. The designation itself, however, does not remove duplicates or replace a data quality management process.<\/p>\n\n\n\n

Standardizing Customer Data: A Shared Identifier and the Right Relationships<\/strong><\/h2>\n\n\n\n

The first step is to determine which records refer to the same customer. In B2B sales, a tax identification number, such as Poland\u2019s NIP, can help, provided that the country and the number\u2019s validity are taken into account. Similar names or a shared address are not enough to justify automatically merging records. Companies within the same corporate group should remain distinct, even when their results are analyzed together. The payer, delivery recipient, and order fulfillment location may also need to be treated separately. A shared identifier must be linked to the identifiers used in source systems while preserving these business relationships.<\/p>\n\n\n\n

For example, a distributor can analyze sales to an entire retail chain while maintaining separate records for its headquarters and individual stores. Negotiations can then reflect the full value of the business relationship, while accounts receivable analysis remains tied to the correct payer. Ambiguous cases require review by the person responsible for customer data.<\/p>\n\n\n\n

Standardizing Product Data: Comparable Assortment and Margin Analysis<\/strong><\/h2>\n\n\n\n

For products, common problems include different codes, names, units of measure, and assortment classifications. The same item may be sold individually, purchased by the case, and reported by weight. Adding these quantities together without agreed-upon conversion rules leads to incorrect conclusions about demand and inventory. A shared product record should define the base unit of measure, variant, packaging, and mappings to source system codes. Individual products must also be distinguished from product families because these levels serve different analytical purposes. Variants should not be merged simply because they have similar names if their specifications or sales formats differ.<\/p>\n\n\n\n

An \u201caccessories\u201d category should include an agreed-upon set of products in every report so that margin comparisons are meaningful. In manufacturing<\/a>, shared product records help connect sales with costs, customer complaints, and inventory. Assessing profitability also requires transactional data and a consistent cost calculation method.<\/p>\n\n\n\n

Standardizing Supplier Data: A More Complete View of Purchasing and Dependencies<\/strong><\/h2>\n\n\n\n

Fragmented supplier records make it difficult to determine how much a company actually purchases from a particular business. When identifiers differ, purchases from one supplier can look like transactions with several separate companies. Shared identification makes it possible to compare spending, payment terms, delivery performance, and complaints. The distinction between an individual legal entity and a group of affiliated suppliers must still be preserved. Analysis at the group level can reveal purchasing concentration that remains hidden in individual branch reports. Procurement teams gain a stronger basis for negotiations and for identifying alternative sources of supply.<\/p>\n\n\n\n

In an illustrative example, three plants purchase materials from the same supplier for PLN 400,000, PLN 600,000, and PLN 500,000 per year. Once their records are linked, the total purchasing value becomes visible: PLN 1.5 million. A hypothetical 2% price reduction would generate PLN 30,000 in annual savings, assuming purchasing volumes remain unchanged. The actual outcome, however, depends on contract terms and the effectiveness of negotiations.<\/p>\n\n\n\n

The Golden Record: Which Source Takes Priority?<\/strong><\/h2>\n\n\n\n

Once relationships have been identified, the next step is to determine which information should make up the agreed-upon master record, known as the golden record. It can combine reliable information from several systems. A customer\u2019s billing details may come from the financial system, its segment from the CRM, and product specifications from the product catalog. Source priority should therefore be defined for individual fields rather than entire systems. The latest entry is not always the most accurate, especially when it comes from an unverified form. Every rule needs a rationale and a designated person responsible for approving it.<\/p>\n\n\n\n

In practice, organizations should agree on:<\/p>\n\n\n\n