People are the face, heart, and hands of a company. In people analytics, we analyze data to reveal actionable insights that provide evidence for decisions regarding employees, work, and business objectives. This talk will cover the use of data science for people analytics projects such as workforce planning, improving employee engagement, and retaining talent.

Speaker bio:
Elizabeth Esarove is a data scientist in People Analytics at AT&T. In her role, Elizabeth is part of a larger team focused on embedding data and analytics into the root of decision-making and transforming insights into actionable solutions that improve employee outcomes and drive business value.

Timestamps:
*Q&A timestamps listed further below
3:42 - Start of session
5:14 - What is People Analytics
6:26 - Opportunities for Data Science in People Analytics
7:10 - Using Predictive Models to Reduce Attrition
11:10 - Segmenting Your Population
18:55 - Communicating with Leaders
20:11 - Time Series Forecasting for Workforce Changes
24:41 - Analyzing Employee Survey Comments

Helpful Resources Below:
*more follow-up to come with a Q&A blog post in the works 😉

People Analytics Books
Mentioned today:
📒 Handbook of Regression Modeling in People Analytics: with examples in R, Python and Julia by Keith McNulty https://lnkd.in/eBFgniFG
📒 Excellence in People Analytics: How to Use Workforce Data to Create Business Value by Jonathan Ferrar and David Green https://a.co/d/bJrMRuW

People analytics books shared in a previous data science hangout:
📒 Predictive HR Analytics: Mastering the HR Metric: https://a.co/d/5Hx05mw
📒 Inclusalytics - How Diversity, Equity and Inclusion Leaders Use Data to Drive Their Work: https://lnkd.in/g48tdrMu

Other links shared by Liz:
Time Series Models
📒 Forecasting: Principles and Practice by Rob Hyndman and George Athanasopoulos https://otexts.com/fpp3/
📒 Text Analytics
Text Mining with R by Julia Silge & David Robinson https://lnkd.in/emawveZd

Additional resources shared:
📒 R Gov Conference: https://lnkd.in/ePfN7jru (David Meza is presenting on the RStudio (Posit) Ecosystem as a Critical Part of NASA Analytics Capabilities)
📒 People analytics for getting to the moon | Data Science Hangout with David Meza, NASA: https://lnkd.in/eDirbgCF
📒 For LATAM and Spanish Speaking people, Sergio Garcia Mora shared the R4HR community which has developed lots of free access content: https://data-4hr.com/
📒 John Kelly IV shared the Human Resources Science LinkedIn Group: https://lnkd.in/eEMpYAfk
📒 Adrian M. Pérez shared the People Analytics Handbook: https://lnkd.in/ecsWy-dA
📒 Data Science Hangout: pos.it/dsh
📒 All upcoming #Posit community events: pos.it/community-events

Q&A Timestamps:
*the following timestamps are approximate.
16:00 - What are the most important people analytics KPIs @ AT&T? Can you share how your team/HR acts on these predictions (for optimal policy) both experimentally and ethically? do you implement new policy in smaller groups?
23:00 - How have you validated the predictive models? Looking backwards, how precise were they?
25:00 - Do you work with your HRBPs to segment your population?
25:00 - What languages are you using to build your predictive models?
31:00 - Do you include demographic information (gender, race, age) in your models?
31:00 - Are your surveys anonymous?
32:00 - How would you get the ROI from HR attrition modeling?
34:00 - Are most data scientists from a Psychometrics background?
35:00 - Is there a kind of "critical mass" to apply People Analytics? (just for big companies?)
36:00 - Looking at positive / negative comments, do you quote verbatim comments in your reports? (e.g. "here is one of the very positive / very negative comments we received")
37:00 - Do you use something like Snowflake to store and model your data? And do you deploy these models automatically or manually update them?
38:00 - R user here. How do you balance between people-ops focused analytics tools from outside vendors (often very expensive, but helpful) with custom in-house analytics (often time-consuming)?
41:00 - How much of your work is driven by HR leadership, by HR business leaders, or by the HR analytics team pushing modeling and insights to those groups?
42:00 - What was your journey into learning data science and getting into people analytics?
44:00 - Do you have a role in education business units? to improve their questions, etc.?
45:00 - What is the HR tech stack at AT&T? Does your team have a data engineer solely for people data since they're more sensitive?
47:00 - How do you present your results? (an application, report, power point) and how important is it to learn other languages (javascript, css, sql)?
If you were to start a people analytics team in a company (+1000), how do you start?
50:00 - Do you use an internal tool for surveys? Do you use thresholds to maintain anonymity?
53:00 - Does AT&T have remote workers? If so, does people analytics segment on remote vs hybrid vs on-site?