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What are the most common dimensions used in a dimensional model?

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Dimensional modeling is a popular technique for designing data warehouses that supports fast and flexible analysis. In a dimensional model, data is organized into facts and dimensions. Facts are numerical measures of business events, such as sales, orders, or transactions. Dimensions are descriptive attributes that provide context and meaning to the facts, such as date, customer, product, or location. In this article, we will explore some of the most common dimensions used in a dimensional model and how they can enhance your data analysis.

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Syeda Mahrukh Raza

Founder @ LeanOutset | Helping companies create unique value proposition by figuring out user's pain points.
Anna Abramova

Enterprise Data & AI
Divya Jyoti

Solution Architect at NN | Ex-Microsoft | Ex-Oracle | Microsoft Certified Azure Solution Architect Expert | PRINCE2 |…

1 Date dimension

The date dimension is one of the most essential and widely used dimensions in any dimensional model. It allows you to slice and dice your facts by various time periods, such as year, quarter, month, week, day, or hour. The date dimension also enables you to perform time-based calculations, such as year-over-year growth, seasonality, or trends. A good date dimension should include not only the standard calendar attributes, but also any relevant business or fiscal attributes, such as holidays, seasons, or cycles.

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Divya Jyoti

Solution Architect at NN | Ex-Microsoft | Ex-Oracle | Microsoft Certified Azure Solution Architect Expert | PRINCE2 | SAFe? 5 Agilist | Digital Transformation & Cloud | Architecture
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In my opinion Date dimension is THE most important dimension in data warehouse. As the main purpose of Data warehouse is to store 'time-variant' data. So all the measures changes with time. Even in visualization, most common trend is studying various measures over a period of time, be it year on year or month on month.

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2 Customer dimension

The customer dimension is another common and important dimension that helps you understand who your customers are, what they buy, and how they behave. The customer dimension can include attributes such as name, address, phone number, email, gender, age, income, loyalty status, or segmentation. The customer dimension can also capture the changes in customer attributes over time, such as moving to a new location, changing their phone number, or upgrading their loyalty status. This can be done by using a type 2 slowly changing dimension (SCD) technique, which creates a new row for each change in the customer dimension.

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Syeda Mahrukh Raza

Founder @ LeanOutset | Helping companies create unique value proposition by figuring out user's pain points.
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In my opinion, the customer dimension is the most crucial aspect to consider when conceptualizing a product. It guides us to understand customer segments first, followed by creating customer/buyer personas, ultimately leading us to define our ideal customer persona for maximizing customer acquisition.

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Muhammad Jibran

Business Development Manager | MBA | Govt Relations | Public Relation | Sales Professional | Social Activist |
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In continuation of the article based on the perspective three sided discussed highlighting the key important factors of dimensional models. Additionally, there are various prospects to be determined when it comes to sell a product beyond of considering USP & penetration. Importantly a product scientifically examined with consumer buying, transforming short & Long term policies, customer engaging and innovative strategies by addressing the customer needs and wants & evaluating their transformed behavior studying with diagonal sides and dimensions the seller could able to trigger with the help of researches and analytical surveys and thus able to made more attractive characteristics appealing to the customers which brings a majestic increase

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3 Product dimension

The product dimension is a key dimension that describes the products or services that you sell or provide to your customers. The product dimension can include attributes such as name, description, category, subcategory, brand, price, cost, size, color, or features. The product dimension can also reflect the changes in product attributes over time, such as price changes, cost changes, or product discontinuation. This can be done by using a type 2 SCD technique, which creates a new row for each change in the product dimension.

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4 Location dimension

The location dimension is a useful dimension that describes the physical or geographical locations where your business operates or where your customers are located. The location dimension can include attributes such as name, address, city, state, country, region, zip code, latitude, longitude, or time zone. The location dimension can also support spatial analysis, such as distance calculations, maps, or geofencing. A well-designed location dimension should follow a hierarchical structure that allows you to aggregate or drill down your facts by different levels of granularity.

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Michael Strau?

Data Analytics L?sungen für den Mittelstand mit Power BI und DBT
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Am Sinnvollsten ist die Standortdimension z.B. für E-Commerce Unternehmen oder Group Controller die die verschiedenen Standorte für Filialen des Einzelhandels verwalten. Denn hier k?nnen Informationen zu Kaufkraft oder Kaufverhalten gemessen werden und im Anschluss gezielte Marketingma?nahmen sowohl in der Filiale als auch des direkten Umfelds eingeleitet werden.

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5 Other dimensions

Depending on your business domain and analytical needs, you may also use other dimensions in your dimensional model, such as employee, supplier, order, invoice, promotion, channel, or event. These dimensions can provide additional context and detail to your facts and enable you to answer more specific and complex questions. However, you should also be careful not to overcomplicate your dimensional model with too many dimensions or too many attributes per dimension, as this can affect the performance and usability of your data warehouse.

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6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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Anna Abramova

Enterprise Data & AI
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I would also add that the most common dimension types are Type 1 and Type 2 dimensions. A Type 1 dimension is a static dimension in a data warehouse where the attributes or values do not change over time. When updates are made to in a source, the changes overwrite the existing values in the dimension, and historical data is not preserved. A Type 2 dimension is a historical dimension that maintains a history of changes to dimension attributes over time. New dimension records are created (inserted) when there are changes to the source attribute values, allowing the data warehouse to track historical data and analyze how data have evolved over time.

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What are the most common dimensions used in a dimensional model?

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6

1 Date dimension

2 Customer dimension

3 Product dimension

4 Location dimension

5 Other dimensions

6 Here’s what else to consider

Data Warehousing

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What are the most common dimensions used in a dimensional model?

1

2

3

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6

1 Date dimension

2 Customer dimension

3 Product dimension

4 Location dimension

5 Other dimensions

6 Here’s what else to consider

Data Warehousing

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