In an earlier post I summarized Josh Bersin’s 2018 forecast of disruptive technologies in HR, which I followed-up with an overview of the leadership styles implied by the technology. My experience with the technology and analytics is that many of the logical elements of human resources can now be figured out with increased ease. Or rather, it’s easy if you figured them out last year. But once we have figured out the numbers, it is the social and qualitative factors that become important.
When describing the analytics Bersin names four different types of data:
- HRMS data
- Relationship data
- Wellbeing data
- Sentiment data
The relationship data described above is a reference to Organizational Network Analytics (ONA), which uses social network theory to look at the way people interact. ONA collects data from email traffic, meeting attendance, phone calls, and geographic proximity. It takes a lot of work to get the data to sing, but we already know some of the implications from pre-existing research on social networks.
Information and opportunities flow through the social networks with partial disregard for rank, department, or a person’s commitment to the institution itself. Sometimes powerful and important people have good connections… but sometimes they do not, and sometimes there are lesser-known influencers who are the key.
Your new status in a network will be influenced by your ability to consider contradictory opinions, your curiosity about new perspectives, and your connections to people in diverse cliques. Keeping the channels open will be key to your success. But the best opportunities are to coordinate the entire network for organizational gain, rather than to rig it to favour one individual (be it yourself or someone else). Think of this as being like pay equity on steroids; once you measure and publicize how things have been organized, there will be an immediate impetus to re-orient everything towards fairness and performance.
Beyond social networks, sentiment data opens a major opportunity. Your opportunities for analysis jump dramatically once you ask people their story, their context, their emotions, and how this experience relates to their home life and how they describe themselves as people. Qualitative data has turned out to the missing puzzle piece that everyone was looking for. It’s difficult to get to because analysts need the humility to talk to people who aren’t always great at math. Some of the best insights about the subjective experience comes from journalists, novelists, philosophers, and people in the arts. You really need to show up at those kinds of dinner parties because when it’s time to design your model or your AI to mimic human behaviour, you need to know what it means to be human in the first place.
Increasingly, people analytics is a velvet-roped line up to board a greyhound bus that takes you to destinations unknown. When you get off that bus, you will find you are not being led to a four-star hotel or the hip new club. Rather, you are unloaded at a diner where a long-lost cousin shares old photos, your best friend calls you on your bull, and you re-discover that one small thing that’s truly important to you. The truth doesn’t feel good because it’s cool, the truth makes you feel right because it helps you become authentic.
The deeper you go into the data, the more you realize that people are vulnerable, complex, and hiding great potential. They want to talk, and it’s your job to listen.