In the previous piece, we got familiar with a relatively new term in people analytics in David Dadoun’s Nordic People Analytics Summit – relational analytics. David explained in short that it deals with analysing people networks in the organisation and helps identify key employees.
As it’s a pretty new term and it sparked our interest, we decided to look closer at what relational analytics presents, the need for it, it’s benefits, use cases and so on.
First, let’s see what produced the need to go into more in-depth analytics besides the standard attribute data about employees such as their age, gender, education, tenure and salary.
Humans are social beings – and people analytics needs to reflect that
People are social beings and we continue our inherent social interaction in our workplace as well. We form complex networks and relationships at our workplaces that charts and excel sheets can’t capture. Therefore it’s necessary to look closely at a different type of data that can give a more accurate and realistic understanding of our employees.
Paul Leonardi and Noshir Contractor give a comprehensive description of relational analytics in and why it’s the next logical level in people analytics.
Their research is backed up by the Deloitte study, stating that 70% of organizations consider people analytics to be a high priority, but only 9% of companies believe they have a good understanding of which talent dimensions drive performance in their organizations.
They argue that the lack of results and modest progress in people analytics stems from it being too narrow and focused only on individuals and not organizational relationships. According to the pair of researchers, relational analytics studies people’s interaction and if it’s incorporated into companies’ people analytics strategies, they can better identify employees who are capable of helping them achieve their corporate goals related to innovation, influence, or efficiency.
They highlight that there is a real value of analytics that extends to understanding the interplay among employees in the organization. The authors also refer to this approach as “the science of human social networks”.
People analytics vs relational analytics
As we’ve seen previously in David’s presentation, Paul Leonardi and Noshir Contractor relate that traditional people analytics relies on employee attribute data: Traits (facts about individuals that don’t change, such as ethnicity, gender, and work history.) and Stats (facts about individuals that do change, such as age, education level, company tenure, value of received bonuses, commute distance, and days absent.)
The attribute analytics of these two types of data is necessary for all companies, but it’s not sufficient to understand all that we need about the people. Therefore, the next stride should be towards relational analytics and relational data that capture the communication between people in an organisation, state Leonardi and Contractor.
As the research duo asserts:
“Decades of research convincingly show that the relationships employees have with one another—together with their individual attributes—can explain their workplace performance. The key is finding “structural signatures”: patterns in the data that correlate to some form of good (or bad) performance.”
The authors provide a detailed rundown of the six structural signatures they’ve identified and that present the pillars of relational analytics:
- Ideation: Which employees come up with the best ideas
- Influence: Which employees can influence the behaviour of their colleagues
- Efficiency: Which teams/employees complete projects on time
- Innovation: Which teams/employees drive innovation
- Silos: Which functions within the organisation are siloed
- Vulnerability: Which employees can the organisation not afford to lose
This relational data can be captured through the everyday digital activity of employees: emails sent, posts on Facebook’s workplace, messages on Slack, projects assignments in Trello or Asana, etc. Leonardi and Contractor state that this communication information can be used to construct views of employee, team, and organizational networks.
The researchers also discovered that companies, because of lack of information systems for capturing relational data, typically rely on surveys to learn who they interact with. However, survey answers may vary in accuracy and take a lot of time to conduct. Also, not all behaviour and communication activities are equal. And in surveys, people may list connections they think they’re supposed to interact with, rather than those they actually do interact with.
To solve these manual collection challenges, Leonardi and Contractor suggest leveraging machine learning algorithms and simulation models to have a better understanding of employees networks.
Benefits of relational analytics
Analysing these signatures help companies to identify their key players and high potential employees they can’t afford to lose based on their ability to influence stakeholders and drive innovation, and pinpoint where silos exist in their organizations.
This knowledge is crucial for HR leaders to understand which employees they must retain and how to break down the silos around them to increase their influence.
Companies that have implemented relational analytics are able to identify types of communication behaviour that drives better relationships, and hence, superior business performance. Moreover, they have improved their retention rate by analysing interactions between employees and whether they build critical contacts with colleagues and their managers.
How to make relational analytics work for you
Employee interactions and relationships are dynamic – people and projects are constantly changing. So doing timely relational analysis based on digital communications data is critical to driving the value for HR leaders.
Also, the analytics should be performed in collaboration and close to the decision-makers and managers, and equip them with visual insights via easy-to-use dashboards to make well-informed decisions about their employees.
One challenge that the experts have identified with collecting relational data is privacy. They have indicated that employees may see the monitoring of their communications is an invasion of privacy. Therefore, companies need to define clear HR policies and draft employee agreements that transparently describe the need, depth and benefit of analysing their digital exhaust
Attribution analytics will take companies only so far. But leveraging relational analytics can give more depth to the evidence-based insights to identify key employees, drive innovation and create a more productive organisation.