How to get from raw data to actionable insight – Orgvue Knowledge Base

This article introduces key terms used to describe organizational data, explaining how ‘Raw data’ (Nodes, Properties and Values) is used to generate ‘aggregate’ Measures which can be ‘sliced and diced’ by Dimensions to provide actionable insight.

Orgvue’s template packs for organizational analysis contain typical organizational measures to get your analysis started and can be customized with the dimensions which are relevant for your organization.

Nodes are the smallest ‘thing’ we have information about. In the example below, each Node is an employee, seen in the rows of a Worksheet slide in Orgvue.

Properties are the information we have about Nodes. In the example below there are six Properties, shown as columns in the worksheet.

In addition to your source data properties, Orgvue datasets include Generated Properties to accelerate your analysis. For example, ‘Span’ is automatically calculated through employee IDs and Manager IDs, and the value for this property updates as structural changes are modelled.

Values describe a Node. These are found the intersection of Nodes and Positions.

Nodes, Properties and Values are individual-level, raw data.

Measures are generated by performing calculations using properties. These are typically simple operations such as adding values to create a (sum) total or creating averages and percentages. Examples include:

Sum: e.g., total workforce size; total compensation costs
Average: e.g., average span of control, average bonus value
Percentage: e.g., percentage of female employees.

Measures are reported as aggregate data; they are always based on aggregations – groups – of Nodes. Often, every node is included in the aggregation to provide a headline total for the whole organization. However sub-group comparisons are needed to understand differences across the organization.

Dimensions are properties used to create subgroups so we can look at Measures across different parts of the organization. For example, different functional departments, geographic locations, or business units. ‘Slicing and dicing’ Measures by Dimensions enables sub-group comparisons so we can identify where action needs to be focused.

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