The Relationship Lens: Smart Network Analytics with Machine Learning

See your team like never before

Managers and leaders are used to gathering and analysing a lot of data — we are told more data we have the more useful it is and that data analytics is a powerful modus for understanding your employees.

However, what happens when all the surveys, reviews, and profiles tell conflicting stories and recommend contradictory or unrelated actions and next steps? Yes, it is possible for all the data to provide leaders with the same consistent message on their teams, but those of us who have led teams and organizations know that it rarely, if ever, happens. People analytics is often a rabbit hole of information — It is fascinating, yet overwhelming, with lots of conflicting, disparate pieces of information often with little connection to the immediate issues you face in your teams.

This is where the Relationship Lens comes into focus. Understanding how employees interact with each other, and how/where they invest their time and effort to build trust and strong relationships helps leaders understand why some of the reviews and surveys have conflicting results, and how to truly fix any gaps in a targeted manner.


An organization completed their annual anonymous Employee Engagement survey and found that their low engagement scores are primarily due to majority of the employees (72%) ranking mentorship as a huge gap in the organization. However, when the leader turned to the recently completed 360º reviews, she found that nearly all of the managers were lauded for being great mentors by their direct reports. Furthermore, the personality traits of the managers aligned with the recommended behavioral profiles for strong mentorship capabilities. As most leaders are wont to do in such situations, the leader determined the anonymous survey results to be an outlier and continued to operate the team as before, with no significant changes to promote mentorship.

RESULT: Over next 12 months, that department had the highest attrition rate in the company, with continued low employee engagement and productivity levels. Cost per employee also increased sharply due to greater recruitment and on-boarding costs.


A quick Relationship Analysis of the department showed the leader that while her managers were great mentors (consistent with the 360º reviews and behavioral profiles), they were only mentoring a small portion of the broad organization, who in turn were not in mentorship positions to others. The social networks within the team drove majority of the trust and mentorship relationships, and the team had socially self-aligned based on affiliations to prior legacy companies which had been acquired a few years ago. This self-alignment had created a major gap in the team for people who had more recently joined the growing team, as they continued to see a lack of career advancement and mentorship for employees not from the self-selected groups.


  • Create department-wide regular social rituals that engage all the employees, and helps forge a ‘department identity’ (vs. ‘legacy company identity’)
  • Set up a formal mentorship tracking process for managers to identify which manager mentors the widest group of individuals — use this as metric for incentive pay bump.
  • Empower more junior managers in the team to take on mentorship-like roles with new employees through ‘on-boarding buddy’ program with major task to integrate new employees into the social events and networks within the teams.


Attrition rate DROPS within 6 months, with higher productivity and 15% lower employee costs. More than 80% of the employees now actively mentored by senior management, and department now has the 4th highest employee engagement scores in the company.

Viewing your teams through the relationship lens is not a difficult task, however, is one that requires the use of specific tools and experts who can help leaders interprete vast amounts of data and the related metrics and maps. OrgAnalytix is at the forefront of this analysis, and is building sophisticated machine learning and AI tools to help leaders utilize their disparate employee data more effectively.

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