We take data about communication, collaboration, and reporting structures, and analyse it using graph theory to help you detect and address key business issues. In this short primer, we illustrate a journey from your org chart to some of the maps we create, and explain how they can help you reach your goals.
Your org chart is a map of reporting relationships. Perhaps you visualise it with boxes for roles and connecting lines for reporting relationships. The boxes may have other attributes: the name of an employee or colour-coding to denote team or geography.
An org chart is a type of graph. We visualise it differently, but with the same principles: each node is a role or employee, each line (or 'edge') is a reporting relationship. We can enrich the graph to convey additional data, such as colouring nodes to denote team or geography.
We can also change the meaning of the edges. You use MS Teams for communication and collaboration. Let's make edges illustrate the count of MS Teams common to each manager/employee pair. A thicker edge means both manager and employee share many MS Teams; a thin edge means none, or just one.
We might see interesting patterns about the collaboration infrastructure: reporting relationships with no MS Teams in common, others with many. Since membership doesn't guarantee usage, let's enrich our graph by counting how many MS Teams each manager/employee pair have both messaged, then illustrated this with a colour scale: edges start out dark grey (for none), and become red and finally yellow as the count rises.
More patterns emerge: reporting lines with extensive, but underutilised, infrastructure; others with a few, well-trafficked MS Teams. For organisations seeking to drive new ways of working, this picture shows where more support is required, and where to build on (and learn from) success.
Until now, we've used reporting lines to shape our graph - but we know real collaboration cuts across organisational boundaries. Let's enrich our graph again, by depicting all detected relationships and messaging levels. This is now a complete map of employees, and how they are connected by MS Teams.
The precise shape of this visualisation isn't important, but its characteristics (and underlying data) yield useful insights. Less connected employees hover on the periphery - is this appropriate for their roles? Clusters of employees appear - do these align with teams, or geography, or some emergent principle? How does the shape of the network align with your strategy?
We can use measures to make a visualisation better highlight some of these insights. Let's use the 'betweenness' value for each employee to highlight those who occupy key points in the network (like important road junctions in a city). For a change programme seeking ambassadors, this can identify candidates that may not be obvious from the org chart.
The data that underpins the graph can draw its own pictures. For each pairing of departments we may ask: how many MS Teams are common? What percentage of potential relationships have been detected via common memberships? Tabular data can quickly illustrate the answers, enabling targeted action.
Now let's add a final dimension: time. Imagine using a graph to identify opportunities for change, enacting your targeted interventions, and later re-mapping the same space to visualise and quantify progress. Our maps help you reach your chosen destination, and prove it.