Modellierung von Adressdaten

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Modeling Address Data: Key Insights and Recommendations

Address data is one of the fundamental components in various business databases, especially where detailed customer information is essential. This complexity can make the modeling process challenging, particularly when aiming for a single “Hub” that consolidates all address information. In a recent discussion on Data Vault Friday, Michael Olschimke of Scalefree explored the best ways to model address data effectively, considering key business and regulatory factors. This article will provide a summary of those insights, offering recommendations for creating a robust, scalable, and efficient address data model.

The Context: Address Data in a Single Hub

In this scenario, the challenge presented was how to model various address types within a single Hub. The primary objective was to avoid redundant address data and simplify handling of NULL values. As per ISO20022 standards and European Union regulations, the data model included attributes such as STREET_NAME, BUILDING_NUMBER, BUILDING_NAME, ROOM, FLOOR, POSTAL_CODE, TOWN_NAME, COUNTRY_CODE, ADDRESS_LINE_1und ADDRESS_LINE_2. Each of these elements is part of a composite business key used to uniquely identify each address.

A practical but complex solution proposed by the team was to replace NULL values with a placeholder (e.g., “-2”) to streamline the loading process and minimize handling issues. However, Olschimke proposed several alternative approaches to ensure the model is both sustainable and scalable.

Challenges with Replacing NULL Values

Replacing NULL values in business keys can simplify loading but has significant drawbacks downstream, particularly when managing dimensional data and maintaining business logic clarity. For instance, when NULL values are replaced with a placeholder like “-2,” this value could appear in downstream reports, causing confusion. Olschimke suggested a more nuanced approach that utilizes “fixed hash values,” such as all zeros or all F’s, to represent empty or erroneous values.

By using fixed hash values, it becomes easier to identify default or error states directly within the data structure. This approach avoids unnecessary complexity when filtering data downstream and improves the clarity and manageability of data processing operations.

Avoid Overloaded Hubs and Null Values in Composite Keys

One of the main points of consideration was the risk of “overloading” Hubs, which occurs when multiple business objects with different semantic meanings are stored within the same Hub. This is particularly common when different types of addresses are stored under a single business key, where each address type may not require all fields (e.g., ROOM or FLOOR).

According to Olschimke, overloaded Hubs introduce complexity due to differing data granularities and missing values across address types. For example, multiple buildings, floors, or rooms could exist under a single address, resulting in multiple granularities within a single Hub. This makes it challenging to maintain clear, meaningful data relationships. Instead, he advised defining clear granularity levels and possibly separating address types or using more flexible data structures.

Alternative Modeling Solutions: JSON-Based and Reference Tables

In cases where multiple address types require flexibility, Olschimke suggested using JSON-based data structures. JSON provides flexibility in defining address attributes dynamically, storing only the keys available for a particular address. This approach reduces the risk of overloading and accommodates varying address structures without creating a complex, rigid schema.

JSON-based Hubs allow for hashing the address data as a single JSON object, ordered by key names to prevent duplicates. However, this approach requires a consistent, standardized order of attributes when hashing to ensure duplicate-free keys. For instance, JSON formatting could streamline the Hub and enable more adaptive data loading while simplifying downstream data extraction.

Additionally, using reference tables is another approach for frequently accessed address data, enabling deduplication without over-complicating the Hub. Reference tables act as dedicated sources of address data, indexed by a unique address ID, which reduces redundancy across other Hubs.

Considering Address Data as Descriptive in Satellites

Instead of adding addresses as business keys in the Hub, it can be more effective to store them as descriptive attributes within a Satellite structure. This avoids overloading the Hub with attributes that may not always be needed for identifying the business key itself. By storing address data in Satellites linked to the primary business entity (e.g., customers, stores), you can achieve a balance between deduplication and schema simplicity.

Olschimke recommended this approach particularly when the main goal is to eliminate redundancies across address data. This approach aligns with a best practice in Data Vault modeling: Satellite tables should contain descriptive data that change over time, while Hubs contain only essential business identifiers.

Applying Business Rules in Data Vault Modeling

Address data often requires additional business rules, especially when handling complex keys or duplicates. Olschimke pointed out that handling NULL values with a placeholder complicates creating downstream dimensions. Instead, a two-step approach was advised: (1) defining the business keys within the Hub with fixed placeholders (e.g., all zeros or all F’s) for default and error handling and (2) standardizing the Satellite structure to handle varying address formats dynamically.

Ultimately, each business has unique requirements, and the choice between single Hubs, JSON structures, and Reference tables will depend on how critical the address data is to the core business operations. By focusing on avoiding overloading and ensuring scalability, businesses can set up a Data Vault model that minimizes long-term maintenance while maximizing data clarity and accessibility.

Schlussfolgerung

Modeling address data in a Data Vault context can be intricate, especially when attempting to create a unified Hub that supports various address types. The key considerations discussed by Olschimke emphasize flexibility, simplicity, and adherence to business rules without overloading Hubs. JSON-based keys, reference tables, and Satellite structures offer alternative approaches to managing address data, allowing you to avoid pitfalls associated with NULL placeholders and composite keys.

For businesses tackling complex address data requirements, experimenting with these alternatives may yield significant benefits, particularly in managing data deduplication, compliance, and future scalability.

Interested in learning more? Check out Scalefree’s webinars and consider joining the Data Innovators Exchange community for discussions on data modeling, cloud computing, and Data Vault 2.0 best practices.

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