What is Reference Data?

Daisy McLogan

23 February 2024

Data management is a broad discipline that includes a variety of types of data. Within this spectrum, the concept of Reference Data holds a significant place. This article will delve into the intricacies of reference data, its role in master data management, and its importance in maintaining data consistency and quality.

What is Reference data?

Reference data is a critical component in various organizations' management and operational frameworks. It is the backbone for numerous business processes, acting as a shared reference point that ensures consistency, accuracy, and reliability across different departments and systems. Reference data is a predefined, standardized set of values or codes to classify or categorize other organizational data. Its primary role is facilitating interoperability and data integrity across diverse business applications and databases.

What makes data "Reference data"?

Stability: Reference data changes infrequently. Once established, these data sets remain relatively static, with updates made only on a need-to-basis basis.
Governance: Given its widespread application, reference data is subject to strict governance and control measures to maintain its integrity and reliability.
Standardization: It is standardized across the organization to ensure uniformity in its application and interpretation.

In B2B SaaS

For B2B SaaS companies, reference data plays a pivotal role in ensuring that their platforms can seamlessly integrate with the diverse systems of their clients, thereby enhancing operational efficiency and customer satisfaction. Examples include:

Industry Codes (e.g., NAICS, SIC codes): These codes are essential for segmenting markets, personalizing content, and tailoring services to meet clients' specific needs across different industries.
Currency Codes and Conversion Rates: For SaaS platforms that serve global markets, maintaining accurate and up-to-date currency codes and conversion rates is vital for billing, financial reporting, and analytics.
User Role Definitions: Defining user roles within the software helps customize access permissions and functionalities based on the user's role within their organization, thereby enhancing security and user experience.

In B2C Retail

In the B2C retail sector, reference data ensures that products are correctly categorized and transactions are processed efficiently, contributing to a better shopping experience. Examples include:

Product Codes (e.g., SKU, UPC): These codes are crucial for inventory management, product tracking, and facilitating smooth checkout processes.
Size Charts and Conversion Tables: For apparel and footwear retailers, providing accurate size charts and conversion tables as reference data helps in reducing returns and exchanges due to size issues.
Geographical Codes: Retailers use country and postal codes to optimize shipping logistics, calculate taxes, and tailor product offerings to regional preferences.

The Importance of Reference Data

Reference data serves as the backbone for maintaining the consistency and accuracy of information within an organization. A standardized set of values ensures data across different departments or systems aligns, reducing errors and discrepancies. This uniformity is crucial for retail companies that deal with vast amounts of product information, customer data, and transaction details.

Integrating systems internally and with external partners or platforms is essential for seamless operations in the retail industry. Reference data aids this integration by offering a common language or set of standards that different systems can recognize and process. This ensures that data flows smoothly across the organization, from inventory management to sales analytics, enhancing the ability to share and analyze data comprehensively.

Improving Decision Making

Accurate and consistent reference data empowers organizations to make informed decisions. For retail companies, this could mean better inventory management, more effective marketing strategies, and enhanced customer service. With reliable reference data, retailers can analyze trends, predict customer behavior, and tailor their offerings to meet market demands, improving business outcomes.

Evergreen retail

The Story of EverGreen Retail

To illustrate the impact of reference data, consider the story of EverGreen Retail, a mid-sized company specializing in eco-friendly products. EverGreen faced challenges in managing its inventory efficiently and struggled with inconsistent customer data across its online and physical stores. This led to missed sales opportunities and a disjointed customer experience.

Recognizing the need for change, EverGreen implemented a robust reference data management strategy. They standardized product categories, unified customer identifiers, and synchronized data across their sales channels. This initiative not only improved the quality and consistency of their data but also facilitated smoother integration of their e-commerce platform with in-store systems.

As a result, EverGreen Retail experienced significant benefits:

Inventory Management: With accurate reference data, EverGreen could better predict product demand, optimize stock levels, and reduce waste.
Customer Experience: A unified view of customer data enabled personalized marketing campaigns and offers, improving customer satisfaction and loyalty.
Decision Making: Enhanced data quality and integration gave EverGreen deeper insights into sales trends and customer preferences, guiding strategic decisions to expand their product range and enter new markets.

In the competitive retail sector, the strategic use of reference data can be a game-changer. As demonstrated by EverGreen Retail, adopting a comprehensive reference data management strategy not only streamlines business processes but also significantly boosts operational efficiency and effectiveness. For companies aiming to thrive in today's data-driven world, investing in reference data management is not just beneficial—it's essential.

Reference Data vs. Master Data Management

It's important to distinguish reference data from Master Data Management. While both forms of data provide context for business transactions, there are key differences. Master data represents the business entities shared across an organization, such as customer, employee, and product information. On the other hand, reference data is concerned with classification and categorization.

A change to the reference data values may require an associated change in business processes to support the change. In contrast, a change in master data will always be managed as part of existing business processes.

Here are some examples of Master data that are not reference data.

Customer 360: Comprehensive view of customer data from various touchpoints and interactions.
Product Information Management (PIM): Centralized system managing product data across multiple channels and platforms.
Supplier Data: Detailed information about suppliers, including performance, reliability, and contact details.
Asset Management: Tracks organizational assets, ensuring optimal usage and maintenance scheduling.
Employee Data: Contains employee information, roles, performance metrics, and HR-related data.

Reference Data Management & Solutions

Effective reference data management is key to maintaining data integrity and consistency. It involves the application of strict controls and business rules to manage reference data. This ensures that data remains consistent and reliable, even when shared across various systems and business processes.

The role of technology in supporting reference data management is paramount. Requirements include out-of-the-box support for multiple domains, efficient relationship management between domains, and advanced deduplication algorithms.

Here are some existing legacy solutions; as you can see, Reference Data is part of the Master Data management practice.

1. Informatica MDM : A comprehensive, scalable Master Data Management solution centralizes and cleanses data but may be complex and costly for small businesses.

2. IBM InfoSphere Master Data Management: The solution provides a comprehensive suite of tools for creating an accurate enterprise-wide reference data view, featuring robust data matching and linking, and strong industry reputation and support, though it may present a steep learning curve and high costs for new users.

3. SAP Master Data Governance: offers centralized data management with strong quality control, yet is limited to SAP environments and less flexible elsewhere.

4. Oracle Master Data Management: provides uniform reference data for accurate decision-making with broad features, but can be complex and costly.

Conclusion & What's Next

For organizations striving to achieve data excellence, understanding the nuances of reference data is indispensable. It ensures the uniformity and accuracy of data, which is fundamental for effective decision-making and operational efficiency. As data continues to be an invaluable asset for businesses, the governance and control of reference data will remain a cornerstone of effective data management strategies.

To dive deeper into the world of master and reference data management, consider exploring resources such as industry-specific articles, webinars, and training courses. These resources can provide additional insights and practical applications of reference data in various business scenarios.