Modelling Demographic Data

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Modeling Demographic Data for the Raw Data Vault: A Practical Guide

When working with demographic data for population analysis, one often encounters datasets that include variables such as region, year, age, and sex. For organizations implementing a Data Vault methodology, the task then becomes modeling this data in the Raw Data Vault in a way that enables efficient use in the Business Vault for proportion calculations and other business logic.

In this blog post, we’ll explore how to approach modeling demographic data for the Raw Data Vault. We’ll look at considerations for reference tables, hubs, links, and satellites, and discuss why a simplified approach can be beneficial while still capturing change history when needed. Let’s walk through an example based on a question that was raised during a Data Vault Q&A session.

Defining the Dataset and Objective

Let’s start by outlining the dataset and the main objective. Suppose we have a demographic dataset sourced externally (e.g., from a national bureau) that includes the following columns:

• Region
• Year
• Age
• Sex
• Population count per region, year, age, and sex

The goal is to load this dataset into the Raw Data Vault and make it available for calculations in the Business Vault, such as determining population proportions across different dimensions.

The Simplified Modeling Approach

To understand the modeling approach, let’s consider the basic elements of Data Vault architecture:

• Hubs: Tables that hold unique business keys and serve as identifiers.
• Links: Tables that define relationships between hubs.
• Satellites: Tables that hold descriptive data and track changes over time.

Since this dataset contains demographic attributes with no true business keys, modeling choices hinge on balancing simplification and change capture. Here’s a step-by-step breakdown of the approach:

1. Flatten the Data Structure First

The simplest model for this dataset would be to create a flat, wide table that includes columns for region, year, age, sex, and population value. This structure would load the data directly without further separation into hubs, links, or satellites.

• Pros: Easy to query and manage, especially if there’s no need to track changes over time.
• Cons: Lacks support for tracking updates or changes in population values.

This approach works well if the data is static and updated infrequently, but it limits flexibility for versioning or incremental updates.

2. Introducing Reference Hubs and Satellites for Change Tracking

To address the limitations of a flat table, we can take advantage of reference hubs and satellites. Reference hubs allow us to treat region, year, age, and sex as reference codes. In a reference hub, each unique combination of these codes is treated as a single, composite key.

The structure then includes:

• A Reference Hub with region, year, age, and sex as identifying attributes.
• A Reference Satellite with population counts as the descriptive attribute, which links back to the Reference Hub.

By introducing these reference structures, we ensure that changes in population values can be captured over time. If a new population record comes in with a different population value for a given region, year, age, and sex, it is added to the Reference Satellite as a new row. This provides a change history without needing to modify the original row.

Modeling Strategy and Design

With a focus on simplification, here’s how the structure would look in the Raw Data Vault:

• Reference Hub: Combines the codes for region, year, age, and sex. This setup eliminates the need for separate hubs for each attribute and keeps the model straightforward. The composite key formed by region, year, age, and sex uniquely identifies each record, while the hub serves as a central reference.
• Reference Satellite: Attached to the Reference Hub, the Reference Satellite holds the population value and includes a load date for tracking when data was loaded. This enables us to capture historical changes efficiently. If an update occurs for the population value, a new row is added to the Satellite with an updated load date, creating a versioned history.

Example Structure:

Reference Hub: Demographic_Hub
- Primary Key: Region, Year, Age, Sex
- Additional Columns: Load Date (ldts), Source (src)

Reference Satellite: Population_Satellite
- Foreign Key: Region, Year, Age, Sex (composite key from Hub)
- Population Value
- Load Date (ldts)
- Source (src)

In this setup, all demographic attributes are contained within a single hub and linked to the population value in the satellite. This design maintains a simple and effective data model that supports historical data changes without additional complexity.

Why Avoiding Multiple Hubs and Links Makes Sense

In theory, one could create separate hubs for each demographic attribute—region, year, age, and sex—and link them together. However, this approach introduces unnecessary complexity without adding value in this context. Here’s why:

• Multiple Joins: Multiple hubs and a link table require additional joins, increasing complexity when querying the data.
• Increased Entity Count: Additional hubs and links inflate the number of entities, making the data model harder to understand and maintain.
• Performance Concerns: Each join adds processing cost, which can slow down queries, especially with larger datasets.

By consolidating all demographic attributes into a single hub, we reduce the number of entities and simplify the model, making it easier to use and maintain while still meeting the business needs.

Advantages of a Single Reference Hub with a Satellite

This approach is particularly advantageous because it balances simplicity with flexibility. Here’s how:

• Change Capture: With the reference satellite, we can track historical population data changes over time. Each new row represents an update, identified by load date, making it easy to see when population data was updated.
• Granularity and Scalability: The single hub provides a consistent granularity for the data, ensuring that queries are straightforward and changes are easy to manage.
• Performance Efficiency: Joining based on region, year, age, and sex is computationally efficient. These attributes are typically small (e.g., integer or small character values), reducing the burden on processing.

Using the Model in Business Logic

Once the demographic data is loaded into the Raw Data Vault, it’s ready for use in the Business Vault. Here’s how it could be used for business logic:

1. Joining Data: In the Business Vault, data analysts can join other datasets with the demographic reference satellite on the region, year, age, and sex attributes to incorporate population data.
2. Proportion Calculations: With population counts available, proportion calculations (e.g., the proportion of a certain demographic group in a region) become straightforward.
3. Temporal Analysis: The load date in the satellite provides historical tracking, allowing analysts to view demographic changes over time and analyze trends.

Potential Challenges and Solutions

One common concern is that joining on multiple columns (region, year, age, sex) could impact performance. However, with optimized indexing and the small size of these columns, this concern is minimized. For larger datasets, partitioning on region or year might further optimize query performance.

Conclusion

In summary, a simplified model that leverages a single reference hub with a satellite provides a highly effective way to model demographic data in a Raw Data Vault. This approach allows for straightforward use in the Business Vault and ensures flexibility for tracking historical changes, all while keeping the model manageable and efficient.

This example illustrates how, by focusing on simplification and change capture, organizations can build an effective demographic data model in the Raw Data Vault that meets both current and future needs for population-based analysis.

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