Modeling Slowly Changing Dimensions
In today's dynamic and competitive landscape, modern organisations heavily invest in their data assets. This investment ensures that teams across the entire organisational spectrum – ranging from leadership, product, engineering, finance, marketing, to human resources – can make informed decisions.
Consequently, data teams have a pivotal role in enabling organisations to rely on data-driven decision-making processes. However, merely having a robust and scalable data platform isn't enough; it's essential to extract valuable insights from the stored data. This is where data modeling comes into play.
At its essence, data modeling defines how data is stored, organised, and accessed to facilitate the extraction of insights and analytics. The primary objective of data modeling is to ensure that data needs and requirements are met to support the business and product effectively.
Data teams strive to offer organisations the ability to unlock the full potential of their data but usually encounter a big challenge that relates to how the data is structured such that meaningful analyses can be performed by the relevant teams. This is why modeling dimensions is one of the most important aspects when designing data warehouses.
Dimensions and Data Modeling
As organisations evolve and adapt to changing needs, the simplicity of early data models often gives way to complexity. Without proper modeling, this complexity can quickly spiral out of control, leading to inefficiencies and challenges in managing and deriving value from the data.
Dimensions are crucial components of data modeling as they offer a structured framework that allows data teams to organise their data. Essentially, dimensions represent the different perspectives from which data can be analysed and understood.
Put simply, dimensions provide a lens through which data can be interpreted, facilitating decision-making processes. Whether analysing sales trends, user engagement patterns, customer segmentation, or product performance, dimensions play a pivotal role in measuring and understanding various aspects of the data.
To illustrate, let's consider a business that offers multiple products across different countries or markets. The following diagram depicts a cube representing the model, consisting of three dimensions: product, market, and time. By incorporating these dimensions, the business can extract different measures to inform decision-making processes.
In essence, dimensions assist organisations in several key ways:
- Organising Data: Dimensions streamline data organisation and make data navigation more intuitive. By categorising data into distinct perspectives, dimensions facilitate easier access and retrieval of relevant information
- Establishing Clear Relationships: Dimensions define clear relationships with fact tables, which typically store measures, transactions, or events. These relationships enable seamless integration of context with quantitative data, ensuring a comprehensive understanding of the underlying information
- Enhancing Analytical Capabilities: Dimensions enhance analytical capabilities by enabling data users to extract insights and build meaningful reports or dashboards. By slicing, dicing, and drilling down data along different dimensions, organisations can gain deeper insights into various aspects of their operations
- Improving System Performance: Dimensions play a crucial role in improving the performance of data systems. By structuring data efficiently and optimising queries, dimensions facilitate the extraction of valuable insights in a cost-efficient and timely manner, ultimately enhancing decision-making processes
Dimensions are indeed a critical aspect of data modeling, with the potential to significantly impact the effectiveness of your data products. However, they pose a unique challenge due to the dynamic nature of data. Data is not static; it continuously changes over time. Therefore, it becomes increasingly important to implement techniques that ensure changes are accurately captured and seamlessly integrated into the data models.
Understanding Slowly Changing Dimensions
Slowly Changing Dimensions (SCDs) represent a foundational concept in the context of Data Warehouse design, having a direct influence on the operational capacity of data analytics teams.
SCDs is a concept used to address how to capture and store data changes of dimensions over time. Put simply, Slowly Changing Dimensions offer a framework that enables data teams track data historicity within the data warehouse.
Failing to model SCDs in a way that is both proper and aligned with the needs of the business and product can have profound consequences. It may lead to an inability to accurately capture historical data, jeopardising the organisation's capacity to report essential metrics. This poses a substantial risk, as it undermines the reliability and completeness of the analytical insights derived from the data.
In more technical terms, SCDs have the same natural key and an additional set of data attributes that may (or may not) change over time. The way teams handle these records could determine whether historicity is tracked and subsequently, whether the business metrics of interest can be extracted.
Furthermore, the implementation of SCDs within a data warehouse could also significantly impact other aspects of the data platform. For instance, neglecting to model SCDs properly could lead to the creation of non-idempotent data pipelines, which, in turn, may introduce various challenges in data management.
The five types of Slowly Changing Dimensions
Dealing with the challenges arising from changes to data over time involves employing various methodologies known as SCD Types.
SCD Type 0: Retain original
The first type of Slowly Changing Dimensions, known as SCD Type 0, deals with data that remains static over time. Examples of such data include Date of Birth, National Insurance Number (or Social Security Number for those in the US), and date dimensions.
SCD Type 1: Overwrite
Type 1 refers to data records that are overwritten each time a change occurs. This means that historical records are not retained, making it impossible to track changes over time.
Before implementing this dimension, it's crucial to determine whether historical data for these attributes is necessary. Otherwise, the loss of historical data could limit the team's analytics capabilities.
For instance, let's consider a business that ships products to customers and needs to store shipping addresses. From an operational standpoint, the business only requires the customer's latest address for delivery. However, if the business also aims to analyse how often a customer changes their address over time, SCD Type 1 may not be suitable. This type does not retain historical changes, potentially hindering the extraction of such insights.
SCD Type 2: Create new record
SCD Type 2 involves the creation of a new record each time a change occurs. This means that for the same natural key, a new record with a distinct surrogate key is generated to represent the change. This is the way SCD Type 2 preserves historical data.
By associating each natural key with at least one surrogate key, the system retains a trail of changes over time. This approach allows for the tracking of historical variations while maintaining a clear lineage of data evolution.
