Our focus at DashboardFox is making report and dashboard creation simple and cost-effective for businesses (while maintaining very strong data security). A lot of our attention focuses on the front-end and data visualization or display.
However, because we query data from a database or data warehouse, it is beneficial if the data is correctly organized. When you don’t have control over the database but are reporting from a vendor’s application database, they are unlikely to include measures, facts, or dimensions.
As you build the semantic layer (app) in DashboardFox, you may create some of these elements to aid in report creation from the perspective of a BI report builder. As a result, knowing these words is beneficial.
In this guide, we’ll look at how measures, facts, and dimensions apply to typical database warehouse concepts, as well as how you may utilize them outside of that context and solely in the process of creating reports.
When it comes to organizing and modeling data in databases and data warehousing, we frequently use the terms facts, measurements, and dimensions. However, in this context, the definitions of facts, measures, and dimensions are not exactly what you’d find in a dictionary. Let’s take a look at these notions because they’ll help you comprehend how DashboardFox’s data warehouse cloud interprets your data.
A fact is the component of your data that shows a specific occurrence or transaction, such as the sale of a product or receiving a shipment of a specified quantity of products from a supplier, in the context of a data warehouse. Consider the statistics that would display in the data warehouse of our hypothetical corporation, Best Hats Around:
Does that make sense? It’s crucial to understand that a fact (such as “I sold a pair of shoes”) comprises numerous measures.
A measure might be qualitative, such as a Product ID, or quantitative, such as a product’s price. Following the example above, the act of selling a hat online entails several factors, including:
A measure is a name given to each of these facts about reality. Applying computations or aggregations to quantitative measures as needed for your data model is a part of data modeling. The qualitative measures can then be linked to the measure’s specific properties, which are referred to as dimensions.
Dimensions in the context of a data warehouse are pieces of information that help you comprehend and index measures in your data models. Dimensions are characteristics of a measure or data points that help contextualize a fact.
For instance, here are some of the dimensions found in our company’s data warehouse, Best Hats Around:
The distinction between facts and dimensions affects your data models and how they are represented graphically.
A measure can be beneficial in a variety of situations. A measure is a numerical value that indicates a business metric and is usually additive. You aren’t restricted to a particular metric, either. Within a fact table, you can have numerous measures. If your fact table is used to track foreign purchases, for example, you might have measures for each sort of currency. If you’re putting together a fact table for the retail industry, you’ll probably want to include the following measures: cost, list price, and average sale price.
Using facts or fact tables is advantageous in various situations. Fact tables come in a variety of shapes and sizes.
The most fundamental table is a transactional table. A transactional fact table’s grain is typically described as “one row per line in a transaction,” for example, every line on a receipt. A transactional fact table typically stores data at the highest level of detail, resulting in many dimensions.
As the name suggests, the periodic snapshot captures a “picture of the moment,” where the instant can be any defined period of time, such as a salesman’s performance summary for the previous month. A periodic snapshot table is reliant on the transactional table since it requires the transactional fact table’s precise data to produce the desired performance output.
Accumulating fact tables display the activities of a process with a well-defined start and end, such as order processing. It goes through a series of processes until an order is entirely processed. The associated row in the fact table is updated as actions toward fulfilling the order are accomplished.
Multiple date columns are shared in an accumulating snapshot table, each reflecting a process milestone. Because many milestone dates are unknown at the time of the row’s creation, it’s critical to include an entry in the corresponding date dimension that reflects an uncertain date.
Dimensions, like the previous two notions, are incredibly beneficial. When facts or fact tables are employed, dimensions should be used. The dimensions of a fact are stored in a dimension table linked to the fact table by a foreign key.
robustMuchreport in the context of a data warehouse, a is no limit to the number of dimensions that can be used, and each dimension can include one or more hierarchical relationships. There are also different dimensions that can be useful for different things, such as conformed, outrigger, role-playing, and many more.
We have established how essential measures. Facts. And dimensions are in business intelligence. It would be best if you had these to produce the best results that would help your business thrive and prosper, especially in the current situation where businesses, in general, are struggling to keep themselves afloat.
However, while you can implement these things in the most basic of situations, not all situations require only the basic needs. Sometimes, you need to step up and be ahead of the pack. You can’t do that with your existing tools, but with DashboardFox, you can do it all in a snap.
With DashboardFox’s semantic layer through the DashboardFox app, it won’t be hard for you to get what you want in the quickest time possible. DashboardFox was created because not all business owners are proficient in technology, so you can use it efficiently and effectively.
No need for fluency in computer programming languages with DashboardFox.
DashboardFox is also equipped with a formula builder, making everything seamless and smooth in building formulas you need in business intelligence. We all know how sensitive business intelligence can be in terms of formulas, so this comes in handy when you use the ‘Fox.
Lastly, with its codeless report building, a self-hosted approach that guarantees data security and cybersecurity in general, and affordable pricing without compromising the quality, you can be assured that DashboardFox has covered you on all bases.
What are you waiting for? Reach out to us through a meeting or take advantage of the free live demo sessions we offer prospective clients to see what the ‘Fox can do to your business.