Dimensions
Dimensions are qualitiative data fields that describe or categorize or group data. They provide the context in which the metrics or measures are to be interpreted. Dimensions are typically text based fields e.g.,
Product Category: The category or type of product (e.g., electronics, clothing, etc.).
Customer Name: The name of a customer.
Month/ Year: The months or years.
Region: The geographical location (e.g., country, state, city).
Salesperson: The name of the person handling a sale.
Store/Branch: The location or branch of the store where the sale occurred.
Deriving Dimensions out of Numeric Fields
Numerical fields cannot be directly used as dimensions. However, when they are converted into discrete categories or groups which in turn can be used to segment the data, the discrete categories or groups can be used as dimensions.
In ViewZen Analytics, the following operations are supported on numeric fields, enabling them to be used as dimensions:
Adding Data Transformation Step in the Data Pipeline - A data transformation step can be added in the pipeline to convert the data to categories or groups. This grouping is static and is represented as a text column in the dataset. This approach is ideal when the data volumes are large and the groups do not normally change very frequently or have pre-defined standard ranges. E.g., credit score ranges.
Adding an expression in the Dataset - An expression can be attached to a dataset to create the groupings. This grouping is static but can be changed, and the data type of the expression is text. This approach is ideal for relatively small data volumes and situations where group definitions may need to be adjusted over time. E.g., classification of sales revenue into Low, Medium and High Value. The definition of low medium and high values may require to be readjusted over time.
Context-based groupings - Contexts can be created. Contexts can be mapped to the relevant numeric fields. While creating the visual, the context can be used for dimension. Although the context is static, multiple contexts can be mapped to a data field. This approach is ideal for classifying numbers into different buckets, especially when data volumes are relatively small and the number of buckets is limited. For example, receivables data may need to be categorized into four or seven aging buckets.
Tip
In ViewZen Analytics, a numeric field can be directly used as a dimension, allowing standard (equal-sized) bins to be created as categories. This approach is ideal when continuous numeric data needs to be segmented into evenly sized groups.
Deriving Dimensions out of Date Fields
In ViewZen Analytics, date fields can be directly used as dimensions by extracting meaningful attributes such as year, month, week, or day. Dates serve as a crucial dimension for time-series analysis, trend identification, and period-based comparisons.
Common date based dimensions
Year – Extracts the year from the date (e.g., 2024, 2025).
Month – Extracts the month as a name.
Week – Groups data in intervals of 7.
Day – Extracts the data as date.
Financial Year & Financial Quarter – Custom groupings based on a company's financial calendar.
Visual Components using Dimensions
The following visual components in ViewZen Analytics use Dimensions
Pivots
Both for row and column
Mutiple Dimensions are supported in both rows and columns of the Pivot chart
Grid / Table
Only for aggregate table
Multiple dimensions can be used. Each dimension provides a separte column grouping
Charts
Dimensions are supported only on the x-axis
Only two dimensions on the X-axis are currently supported
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