Wikipedia’s definition of leadership is inaccurate when I think of my neighbor Juana. She is an older woman, without many studies, a struggling housewife who has always worked cleaning houses. Juana is an affable person who dedicates time to you when you cross her, and who helps you without expecting anything in return. Juana does not quite fit the standards of managerial skill. However… Juana, my neighbor, is a born leader. And it’s not just me, it’s said by hundreds of neighbors with dogs that who appreciate her influence and pull in the commendable fight we had with the city council for the change of municipal ordinance of tenure and animal protection.
We’ve all met an informal leader once. He is a person who possesses certain characteristics that give him the ability to influence other people. Someone who communicates by connecting in a deep way and for some reason leaves you with greater ability to get where you wanted to go. In addition, they are people who are not recognized by the formal power structure, nor do they receive any extra compensation or benefit for such influential work.
Juana lives outside of this, but you, reader, know that we live in an especially difficult time for organizations. No one has prepared us for so many changes or for the speed at which it is necessary to manage them. In addition, it is difficult to approach a change knowing that we drag a collective history of constant failures when managing them (nearly 70% failure rate). It is clear that our problems have changed and the solutions we used before to solve them are no longer useful to us now.
our problems in managing change have also changed, and the solutions we used before are no longer working.
Now the changes are so profound that we usually call them “transformations” and the environment is so complex and volatile that the rigidity of our organizational structures can cost us survival. We need to adapt to each situation and business moment in an agile way, maintaining something we cannot renounce, while our economic system remains what it is, high productivity and efficiency.
Ultimately we need to change form as quickly as a liquid does for a coil. That’s why many digital businesses, already born in this environment, have structured themselves as “liquid organizations“. Flatter structures, organized by projects in which professional profiles from different disciplines collaborate, and team leaders, who change according to the project, are chosen for their specific competencies for each case.
People need to make sense of change before they change, so it is essential to start our work with good communication. In a traditional organizational change process, it is common to see how corporate information bounces off the formal leaders who issue it. Messages that do not cause too much repercussion to the bulk of our staff, at most arouse fear and distrust.
The corporate message has a poor reach, its impact is small, it is very unattractive to the employee and worst of all… it has little credibility.
Data, data and data.
Data, data and data. Researchers, who handle the data, have spent years studying and detecting that people within an organisation are recurrently sought for informal advice. Statistical analysis of this information has revealed that there is a minimum number of employees with whom as much of the organisation as possible can be influenced (“key influencers“)..
3% of the most influential people, you can influence 85% of the other employees.
Therefore, if you want to be successful, there are 3% of employees who should inevitably actively participate in the change strategy.
But… How can we detect which are the “key influencers“? How can we rigorously detect (for intuition there will always be time) which are the informal leaders of our organization? How can we identify which are the best ambassadors of change?
The company is an environment in which the collaboration between people acquires a determining character for the successful functioning of the organization. The organization chart of a company is nothing more than a vague attempt to direct the occurrence of interactions between people. Behind this necessary formalism, there is a complex structure of information flows between individuals that determines the tangled nervous system we call Collaborative Network.
From social psychology many efforts have been directed to study this type of interactions, and to promote the efficiency of the collaboration between people. But only People Analytics gives us access to coherent techniques for analyzing collaborative networks, with the aim of improving them or identifying key people in the organization.
As always the process starts by asking the right questions and ends by putting data on those questions. But before being able to ask any question it is necessary to first understand that it is a collaborative network and differentiate it from other virtual networks that exist in the company.
It is also necessary to find a series of measurable parameters (patterns of collaboration) that are useful to us to define the network, some could be these:
And finally we must be able to draw the collaborative network of our organization (or department, or area of the company to be studied), that is, obtain data on “who interacts with whom”, within the network, to finally use certain tools that facilitate us to map such data and visually represent the network.
By way of example, let us imagine a very simple collaborative network such as the one I represent below:
Of all the things we can question about a collaborative network like this, there are two questions that seem nuclear, the first would be how do the patterns of collaboration within our network vary? And for the sake of simplicity, let’s look at the variation of just one relationship pattern: the size of the network.
We can analyze how network size varies over time, but it is also very useful to see the differences for each of the different employees. The latter can be represented in a table, counting for each node of the network the number of information searches (arrows that go out) and the number of information requests (arrows that go in). We represent all this in a table like the following one:
This table will not give us the answer we want, but it will awaken our imagination to very interesting questions. For example, Tony is an employee who receives many requests for information, why does this happen? Possibly one of the informal leaders we are looking for. At the other extreme Susan, she demands too much information from others and too few people use her – is there any explanation? Susan may not be an example of an informal leader in our organization.
Detecting informal leaders with this methodology is one of its benefits, but this type of analysis provides valuable information when making important decisions in terms of people management. For example, it has a powerful utility in the analysis of performance evaluation, in promotion processes, in how to efficiently manage training or mentoring programs. If we try to discover a causal relationship between these data and performance indicators, a range of data opens up that gives us tremendously useful information on how the parameters of our collaborative network affect individual performance or any other indicator that measures the performance of our organization.
Depending on the type of network parameter and the type of result you measure, this analysis also has strong applications in processes such as the correct determination of roles and responsibilities, in remuneration. It is also useful for the management of internal rotation and professional development since you can position your employees in nodes that allow them to grow their collaboration network. Talent retention also benefits from this analysis because detecting people with a high number of requests for information can have a relationship with finding people who are really “exhausted”, on whom you could act before it is too late.
The identification of the Informal leaders of an organization is a key part of any transforming process. There are different tools to efficiently represent a network to proceed to its analysis, I can tell you about one with which I have had the good fortune to collaborate and I think it is a delight for its simplicity and the power of the information it provides, it is ONA. – Organizational Network Analysis.
ONA – ORGANIZATIONAL NETWORK ANALYSIS
ONA is a scalable cloud-based platform that allows you to visualize and analyze the informal relationships that exist within the organization, enabling the detection of employees who have a high ability to influence other employees, either positively or negatively.
Depending on the type of data analysed, we can differentiate between active ONA and passive ONA.
The implementation of Active ONA is carried out through a brief online survey, which aims to map the different types of informal interactions between employees, and then identify the informal leaders within the organization.
Once informal interactions have been captured, they are visualized in an interactive network, where employees are categorized by level of influence (central, intermediate and peripheral). These categories are based on the employee’s position in an automated ranking based on the results of the online survey.
Even with a level of participation as low as 40%, the results are able to reflect the entire informal collaborative structure of the entire organization. This is because even if an employee does not participate in the survey, it can be reflected in the analysis after being identified by a colleague.
Passive ONA provides a complementary view based on employee fingerprint analysis. This analysis has a more objective and formal character, as well as a higher level of scalability. Examples of passive data sources include tools for email communication (Gmail, Outlook), collaborative software development (Github) and project management (Jira).
When analyzing the network it is important to consider the role that each employee plays in the organization. An employee working in a technical assistance position can provide technical support to a large number of people within the organization, but this does not necessarily mean that he or she is an informal leader. Therefore, ONA’s algorithm considers all kinds of interactions when classifying employees, which reduces the impact of noise introduced by employee roles.
A large network that is difficult to analyze due to its size, but ONA enables different options to reduce the density of the network:
- Reduce network density by grouping or merging nodes
- Show subsections of networks by applying filters based on specific areas of the organization, deadlines or centrality measures.
ONA’s algorithm is able to determine whether the employee’s level of influence is aligned with expectations given their role and level of experience. For example, a senior employee in a managerial position is expected to occupy a central position in the network, while the same level of influence will be considered to be above the expectations of a junior non-managerial employee.
There is a collaborative network underneath your organization, and today technology makes you capable of understanding how it works, of being able to measure and map it at a given moment, of observing how it evolves, of understanding how it relates to the results that matter to us, and above all… of identifying which are the informal leaders, of discovering which are the perfect agents of change that will move 85% of your staff to wherever you choose.
Throwing control over something as complex as organizational change processes is difficult, but data analytics certainly configures the smartest way to achieve it.