Hi Jason! Love this thoughtful piece on the staffing model for people analytics teams – the exact question I’ve been wondering as I navigate various entry points for the field.
From what I learned from my friend working as a project manager for Facebook’s people analytics team, they do follow the “service-based model” with researchers, data analysts, business partners, and project managers. I am excited to see the training & solution / UX roles in the platform-based model that focus on informing the broader business about the capabilities of people analytics in addressing not only the traditional employee lifecycle questions (recruiting, retention, etc.) but also the core of human capital productivity and leadership development, etc. The integration between business performance data and HRIS data definitely requires more stakeholders to understand and endorse the significance of a strong people analytics team.
Meanwhile, I do agree with you that it’s still an early stage for most companies to grow into the platform-based model as they may not have the data infrastructure or integrity to support broader business requests, and the traditional model could be a good starting point for proof of concept. Thanks again for sharing!
Hi Robson! I appreciate your reflections on the importance of nudges during the concentrated higher ed experience, especially the value in both long-term trajectory explorations and skill-oriented small gains. I agree with you and Rocio that the modular integration of academic and real-world experiences could benefit students – not only in identifying and adjusting their career goals or understanding the market needs for employable skills, but also to dive deeper into personal motivations and purpose (nudges for introspections?). I also wonder how Humu could help with the transition from undergrad to first job through its understanding of individuals’ effective ways of working, as well as to provide nudges for a healthy amount of rest (“work-life balance”) which is not intuitive for many new grads.
Hi Counselor! Very interesting thoughts on the existing incentive metrics for drivers and how they could influence the pay gap. I agree that the compensation algorithm should be thoughtfully crafted to discourage risky driving behaviors or gender-based barriers, and I think to truly understand the data there needs to be more user research on the female driver population to understand their personal choices and preferences to provide effective support. Instead of being “gender-neutral”, I wonder if having the option of tailored support and education programs could benefit some drivers. I also recently learned that it’s very sensitive for apps to have any sort of geography-based “safety” metrics, as safety could be a subjective/relative concept that could lead to accessibility issues for certain neighborhoods / socioeconomic groups, so definitely curious how the surge rate policies could be adjusted to serve both economic and societal needs.
Hi Mo! I appreciate your reflections on the “missing” piece of data due to their offline nature, especially as the COVID situation helps us attend to the nuanced elements of in-person interactions. In terms of the physical tracker, the Hitachi example (a media lab invention) that we’re about to discuss is actually an interesting example where they use a badge with a microphone to capture in-person conversations (the directions & proximity the badges are facing each other + voice detection). It’s an opt-in program that comes with a lot of data privacy implications – I’m curious what motivates employees to agree to its use – the opportunity to see their collaboration network? Understanding their own physical movements? I also had a guest speaker in an HKS class yesterday that talked about the consideration of surveillance infrastructure to track & allocation resources to the population without smartphones in times like our current one – an aggregated measurement of proximity I guess.