How do you use attributes and calculated members to enrich your OLAP data analysis?

1 What are attributes and calculated members?

Attributes and calculated members are two types of metadata that you can define in your OLAP cubes or models to add more information and functionality to your data. Attributes are descriptive properties of dimensions, such as color, size, or category. Calculated members are custom expressions that you can create using existing measures, dimensions, or attributes, such as ratios, percentages, or rankings. Both attributes and calculated members can be used in your queries, filters, slicers, or calculations to enhance your analysis.

2 How do attributes and calculated members differ from dimensions and measures?

Dimensions and measures are the basic building blocks of OLAP data. Dimensions are the categories or perspectives that you use to analyze your data, such as product, customer, or time. Measures are the numerical values that you want to aggregate or analyze, such as sales, profit, or quantity. Attributes and calculated members are not independent entities, but rather extensions or modifications of dimensions and measures. Attributes are part of dimensions, and calculated members are part of measures or dimensions.

3 How do you create attributes and calculated members?

The exact steps to create attributes and calculated members may vary depending on the OLAP tool or platform that you use, but the general process is similar. You need to access the cube or model designer, where you can edit the metadata of your data source. To create an attribute, you need to select a dimension and add a new attribute or edit an existing one. You can specify the name, data type, format, and hierarchy of the attribute. To create a calculated member, you need to select a measure or dimension and add a new calculated member or edit an existing one. You can specify the name, expression, format, and scope of the calculated member. You can use the MDX (Multidimensional Expressions) language to write the expression for the calculated member.

4 How do you use attributes and calculated members in your queries?

Once you have created attributes and calculated members in your cube or model, you can use them in your queries to retrieve or manipulate data. You can use attributes and calculated members in the same way as dimensions and measures, depending on their type and scope. You can use attributes to slice, dice, drill down, or roll up your data by different levels of detail. You can use calculated members to perform calculations, comparisons, or transformations on your data. For example, you can use an attribute to filter your sales by product color, or use a calculated member to calculate the profit margin for each product.

5 How do you use attributes and calculated members in your reports?

Using attributes and calculated members in your queries is not enough to enrich your OLAP data analysis. You also need to use them in your reports to present and visualize your data in a meaningful and insightful way. You can use attributes and calculated members in your reports to create charts, tables, dashboards, or other formats that suit your needs and preferences. You can use attributes to create categories, labels, legends, or filters for your reports. You can use calculated members to create values, indicators, measures, or rankings for your reports. For example, you can use an attribute to create a pie chart of sales by product category, or use a calculated member to create a gauge of sales performance against a target.

6 What are the benefits and challenges of using attributes and calculated members?

Using attributes and calculated members can bring many advantages to your OLAP data analysis, such as enhancing the granularity and flexibility of your data, enabling more complex calculations, and improving the readability of your reports. However, these features also present some challenges, such as increasing the complexity and maintenance of your cube or model, affecting query performance, and necessitating more skills and knowledge of MDX and OLAP concepts. It is also important to consider the accuracy and consistency of your data and calculations.

7 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?