What is a Power BI multidimensional model?
Power BI multidimensional model is a data modeling feature offered by Microsoft for quick execution of queries against business data. It is also known as an Analysis Services Multidimensional solution and primarily uses cube structures to perform analysis of business datasets across multiple dimensions. Using the Power BI multidimensional model, users can create complex, business-centric reports that can help them visualize different datasets within the model.
The Power BI multidimensional model comprises different cubes and annotated and extensible dimensions, which you can use to construct complex queries.
It broadly covers a query and calculation engine for OLAP (Online Analytical Processing) data that supports multiple storage modes such as MOLAP (Multidimensional OLAP), ROLAP (Relational OLAP), and HOLAP (Hybrid OLAP) to provide a balanced performance with extendible data requirements. Power BI multidimensional model is suitable when you need to handle a large volume of data. It supports parent-child hierarchies, the creation of different calculated members, measures, KPIs, and measure groups.
Table of contents
Key Takeaways
- Power BI Multidimensional Model is a powerful tool for handling complex business intelligence requirements.
- Careful design and optimization are crucial for achieving optimal performance.
- Understanding the business context and requirements is essential for effective multidimensional modeling
- MD models excel in handling large, complex datasets and demanding OLAP workloads.
- Understand their architecture, strengths, and limitations before deciding on the model type.
- Use Power BI to connect to MD models and create insightful reports and dashboards.
- Choose the model that best aligns with your data, analysis needs, and security requirements.
How to create a Power BI multidimensional model?
The multidimensional model works only with SQL Server Analysis Service (SSAS). Power BI Desktop doesn’t support creating a multidimensional model. Hence you can create an SSAS multidimensional model (MD) or use an existing SSAS MD to connect with Power BI Desktop for your reporting requirements.
To create a Power BI multidimensional model, follow the instructions below:
Step 1: Install SQL Server Data Tools (SSDT) and SQL Server Management Studio (SSMS) on your machine. SSDT is primarily used for creating and modifying business intelligence solutions whereas SSMS is used primarily to administer instances of SQL Server Analysis Services.
Step 2: Use SQL Server Data Tools (SSDT) to create new SSAS MD projects containing the definition of SSAS objects. This includes cubes, dimensions, measures, KPIs, hierarchies, and other relationships.
Step 3: Specify the database connection to connect with the data sources.
Step 4: Develop SSAS projects by creating measures, dimensions, KPIs, and other definitions. Deploy them into production SSAS servers post thorough testing. This will deploy all the metadata you have created as part of the project.
Alternatively, you can also use SSDT to connect to the existing SSAS instance to amend existing object definitions as well.
Step 5: Once the SSAS multidimensional model is created, connect with Power BI Desktop. Navigate to Home – Get data – Database – SQL Server Analysis Services database and click on Connect.
Provide the details of the SSAS database server and click on OK.
Step 6: Create your reports and data visualization using the dataset and publish it to Power BI Service.
Note: You can connect the SSAS multidimensional model using Power BI live connection in both Desktop as well as Service.
Examples
In this section, we will discuss some of the use cases of the Power BI multidimensional model.
Example #1
In this example, we will understand how you can analyze the Sales performance of an e-commerce giant.
- You can create a multidimensional model using the steps highlighted above to build
- dimensions such as Product, Customer, and Time
- measures such as Total Sales, Total Profit, Total Quantity Sold, Average Revenue Per Customer
- Once the model is created, create data visualization to perform data analysis of Sales performance across different time series, product segments, regions, and product types
- Use the drill-through, filtering, and hierarchy features to compare the measures and explain the variances
Example #2
In this example, we will discuss the use case of creating a multidimensional model using SSAS MD for a financial institution for financial performance key metrics. A global financial institution wants to create a dashboard showing KPIs for their financial performance across different business units and is required to set up a multidimensional model using SSAS MD.
You can create an MD using SSAS and define the dimensions such as P&L, Working Capital, Liquidity, and Ledger. Similarly, create the measures to capture the revenue, expenses, assets, liabilities, and funding requirements to create the model.
Once the MD is created, connect with Power BI Desktop using the steps outlined previously. Use the dataset to create data visualization and add drill-down features in Power BI to extrapolate the information at each business unit level.
Example #3
In this example, we will discuss the use case of creating a multidimensional model to support supply chain planning and inventory management for a global supply chain giant. A supply chain organization generates huge amounts of data using which it wants to build a strong inventory management dashboard to plan the budget and overall inventory levels.
Given the data complexity and volume involved, you can create a multidimensional model using SSAS MD by defining dimensions such as Product SKU, Department, Branch, Region, etc.
- Create measures to calculate the lead time, inventory level, stock order, price details, etc.
- Implement the row-level security to ensure only authorized representatives have access to the dataset.
- Connect the SSAS MD with the Power BI Desktop and create the dashboard to track the inventory levels, generate lead orders based on the available stock quantity, and enhance your supply chain mechanism.
Pros and Cons
| Pros | Cons |
|---|---|
| • Multidimensional models are useful in handling large datasets and complex business scenarios • They provide fast query performance with low response time • They support efficient querying and performance optimization through pre-aggregated data. • You can use advanced analytics functionalities such as predictive modeling, data mining, etc. • It easily integrated with BI tools such as Power BI, excel, and other third-party apps • It has advanced OLAP capabilities with both row-level and object-level security features • You can perform MDX calculations, MDX queries, DAX queries, and ASSL. • It supports calculated measures, custom rollups, drill-through functionality, hierarchies, KPIs, partitions, etc. • It offers efficient and balanced use of memory and disk resources. | • Require a steep learning curve to familiarize with the key concepts and terms compared to other models such as the Tabular model • Doesn’t provide a lot of flexibility to amend any changes to the database schema • Power BI multidimensional model is not supported in Power BI Premium/Fabric or Azure analysis services • It requires additional efforts during the design phase due to data complexity and may have a higher development cycle compared to other models. • It doesn’t support calculated columns or tables. |
Important Things to Note
- With Power BI Desktop, you can connect with SQL Server Analysis Services (SSAS) multidimensional models i.e. SSAS MD
- If you are using a live connection, note only enterprise and BI editions of SQL Server 2014 are supported. For the standard edition, you will need SQL Server 2016 or later versions
- For SSAS MD, Actions and name sets are not exposed to Power BI Desktop. To access these, you can connect the cubes containing these details for your reports
- For live connection in SSAD MD, you can’t create a report-level measure
- Consider all the pros and cons before you decide to proceed with Power BI multidimensional mode for your visualization requirements
- Regularly update and process the cubes to ensure data accuracy.
Frequently Asked Questions (FAQs)
Both Tabular and Multidimensional models serve as data sources for Power BI reports, but they differ in their architecture and capabilities:
The key components of a Power BI multidimensional model comprise the following:
• Cubes
• Dimensions
• Dimension attributes
• Measures
• Measure groups
• Hierarchies
• KPIs
• Display Folders
To design effective hierarchies in a multidimensional model, you need to follow some of the tips highlighted below:
• Ensure you have followed the meaningful and standard naming conventions in your hierarchy levels and members
• Keep the hierarchy depth concise and easy to understand
• Validate that each level of hierarchy has a distinct parent-child relationship
• Wherever possible, sort the hierarchy levels in a logical order to provide a meaningful visualization
• Apply formatting to enhance the visualization and readability of the hierarchies
• Periodically review the hierarchy structure to make it efficient and impactful
• Apply data filters if applicable to provide specific information
Yes, you can refresh data in Power BI reports connected to a Multidimensional model. Some of the options are highlighted below:
• For Live Connection, the data refresh happens automatically when you open the report
• For others, you can set up data refresh schedules or manually refresh on demand/ad-hoc basis as the need arises.
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