How do you leverage dimension hierarchies to enable self-service BI and data discovery?

1 Why dimension hierarchies matter

Dimension hierarchies are essential for enabling self-service BI and data discovery because they allow users to explore data at different levels of granularity and drill down or roll up to find insights and patterns. Dimension hierarchies also simplify the data model and reduce the number of joins and queries required to access the data. By defining consistent and meaningful dimension hierarchies, you can improve the data quality, usability, and performance of your BI solution.

2 How to design dimension hierarchies

When designing dimension hierarchies, it is important to identify key dimensions and attributes that are relevant for analysis and reporting needs. Grouping attributes into logical levels of detail and aggregation, based on the business hierarchy or the natural order of the data, is essential. Too many or too few levels can affect usability and performance. A good rule of thumb is to have between 3 and 7 levels per dimension hierarchy. Additionally, use descriptive and unique names for each level and attribute, avoiding codes or abbreviations that are not intuitive for users. Finally, validation and testing of dimension hierarchies should be done with users and stakeholders, allowing for adjustments as needed.

3 How to implement dimension hierarchies

Different technologies and platforms can be used to implement dimension hierarchies in a data warehouse or BI tool. Common methods are star and snowflake schemas, which both use surrogate keys to link dimension tables to fact tables. Star schemas have a single table for each dimension, while snowflake schemas have multiple tables for each dimension. Multidimensional cubes or analytical models can also be used as a data structure that pre-aggregates and organizes data into dimensions and measures for fast and flexible querying and analysis. Data virtualization or federation layers are another option, connecting to different data sources and providing a unified view of the data without physically storing it. Tools such as SQL Server Analysis Services (SSAS), Oracle Essbase, Power BI, Denodo, Informatica, or Data Virtuality can be used to create and manage these structures.

4 How to use dimension hierarchies

Once you have designed and implemented your dimension hierarchies, you can use them to enable self-service BI and data discovery for your users. For example, you can create interactive dashboards and reports that allow users to slice and dice data by different dimensions and levels. Additionally, with natural language queries, visualizations, or advanced analytics, users can perform ad-hoc analysis and exploration of data. Furthermore, your dimension hierarchies can be used as input variables or output features for predictive models and scenarios that are built using machine learning, statistical analysis, or simulation techniques. Tools such as Power BI, Tableau, Qlik Sense, Power BI Q&A, Tableau Ask Data, Qlik Insight Advisor, Azure Machine Learning, R or Python can be used to achieve these outcomes.

5 How to optimize dimension hierarchies

Dimension hierarchies are useful for self-service BI and data discovery, but they must be designed, implemented, and maintained properly for optimal performance and usability. You should review and update your dimension hierarchies regularly to stay current with data and business needs. Additionally, you can apply best practices for data modeling, indexing, partitioning, and compression to improve data access and processing speed. It's also important to implement security and governance policies to control access and usage of your dimension hierarchies by different users and roles. Tools such as DQS, SSMS, SSDT, Azure Data Studio, SQL Server Security, AAD, or Power BI Admin Center can help you optimize your dimension hierarchies.

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?