Organizations under the HNA collaboration microscope

The purpose of organizational structures is to optimize efficient collaboration**. Those who need to talk to each other are put together, those who don’t are separated from each. In a static world, once this optimization problem has been solved, that is the end of the story. Organizations support the most efficient and effective collaboration for the business purpose. However, in a dynamic world, purposes change, and so does the optimal organization (for even more dynamics situations and more radical thinking, see appendix*).

When an organizational structure has been detected to be suboptimal, this triggers (in this ideal company) an organizational transformation, where the wanted new organization is optimizing the new need for collaboration.

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However, on Day 1, the new organization is just an org chart, a blue print of the wanted position, an ambition.

It doesn’t mean that any of the collaboration habits within the former siloes have changed. The hard work only begins now. For every organizational transformation the following questions are vital along this way:

-????????? How does the collaboration reality look like?

-????????? Are we making progress with adapting the collaboration towards the wanted position of the blueprint?

-????????? How do we measure the progress of the organizational transformation?

In the past these questions often were answered in a “normative” way: The organizational chart represents reality; progress is according to the implementation plan of the transformation and working of the action plan is the measurement. In essence this means that organizations often have no clue whether the collaboration really has changed and there is a lot of un-measured wishful thinking included.

But now with Human Network Analysis (HNA, #HNA #humannetworkanalysis) applied to collaboration data we indeed have a tool to answer these questions data driven. Because this is what Human Network Analysis is looking at: Not only the attribute data of a person (e.g. how many meetings), but the interaction between the employees. On a statistical level Human Network Analysis identifies siloes and interaction between employee groups, that is HNA on collaboration data is taking a snapshot of the collaboration. Creating an order of sequence of the network graphs then creates a movie of the collaboration evolution and answering the vital questions mentioned above.

With a recent organizational transformation in Ericsson, these considerations had a very practical use case.

We have marked the 3 former entities with different colors and we follow the evolution over several months. We see an increase in the interaction in total (which is at least partly due to the fact that 07/2022 was a vacation month). We also see an increase in the interaction between different colors, the former siloes are getting closer together and have more collaboration across the former borders. Still we see that each color holds its terrain (most of the collaboration is within, not across).

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For the first time we can see an organizational transformation happening almost in real time under the microscope of Human Network Analysis. For the first time we can observe reality.

With these technical capabilities at hand and the descriptive challenge under control, the more fundamental business questions move on stage and into the focus: How should the wanted position look like? 3 blocks of condensed matter of different color represents very much the siloes, we wanted to get away from. On the other hand, a realization of 3 ideal gases, where the colors are completely mixed is also not efficient. Somewhere in the spectrum should be the solution. Where, this is the million dollar question of organizational management and the business leaders.

What is the purpose of our organization and what type of collaboration is supporting best this purpose. ?As so often here the breakthrough doesn’t come from the answers but from asking the right questions. Good and bad are not pre-defined, they need to be negotiated by the business leaders.

Of course, the following is playing tricks on you with statistics and representation, but you can take it as a metaphor. The network graphs might become very busy and hard to interpret. Hard to see the trees through all the woodwork. HNA metrics can help to simplify the picture (in are very different way, as conventional attribute analysis can do it). Closeness centrality*** is one of these HNA metrics and it is a good measurement of the intensity of the collaboration. Below you see twice the same results shown, only the scale is different. Whether you think it is a huge increase in collaboration (left) or consider not much has changed to collaboration (right) is very much up for discussion and what the business expectations are.

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Appendix 1:

In a more radical approach one might consider that organizations, at least the rigid line organizations are a left-over of the past. Instead of optimizing an outdated concept, which then still will not be adequate for the challenge, one should propose structures that are more adequate to the very dynamic world we are living in. The Matrix approach and the work in projects is a very accepted and established way of “breaking” the line organization. A more advance concept are “Teal Organizations” introduced by Frederic Laloux in “Reinventing Organizations” (Would be an interesting research question to look at an teal organization with the help of HNA).

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Appendix 2:

I am using collaboration in the broad sense of all interactions between employees for the business purpose. I acknowledge that there might be different definitions of collaboration. However, in this broad sense I can relate to the data of meetings, without knowing something about the character of the meeting. Classifying the various types of collaboration would be an interesting topic for future research.

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Appendix 3:

In a?connected?graph,?closeness centrality?(or?closeness) of a node is a measure of?centrality?in a?network, calculated as the reciprocal of the sum of the length of the?shortest paths?between the node and all other nodes in the graph. Thus, the more central a node is, the?closer?it is to all other nodes (Wikipedia: Closeness centrality)