Every month, Analytics in HR compiles a list of the top 5 trending articles – this month is no exception. These are the articles shared the most in September. Have you already read them?
Luk Smeyers – iNostix by Deloitte
Luk describes how some of the organizations he worked for began their HR analytics journey with a ‘needs analysis’. According to Luk, these analyses are not that useful. Instead, companies should accept that the majority of HR professionals are still in the dark about the power of people analytics. HR analytics should thus have a more strategic function. This function should focus on the key business processes in the organization, support HR processes in a data-driven way and should learn as much as it can in order to develop its own durable capabilities. This article offers some great insights for companies who are in the process of introducing HR analytics.
David Green – IBM
David Green has consistently made it into our top 5 every time since we started. This month he does it again with an interview with Anshul Sheopuri, director of the People Analytics team of IBM. In this interview, Anshul talks about reducing employee churn and the impact of new methods on people analytics. The method David and Anshul focus on is IBM’s Cognitive Computing. According to Anshul, Cognitive Computing solves an employee’s problems throughout the duration of his or her employment. The article offers an interesting view on the possibilities of getting started with analytics.
Related: 21 Employee Performance Metrics
Andrew Marritt – OrganizationView
Number 3 on our list fits perfectly with our number 2. According to Andrew, companies should not always focus on optimizing a specific workforce value, like attrition. Instead, the company should focus on optimizing expected loss/value. For example: a reduction of attrition does not offer much benefits if only low performers stay with the company longer. When expected loss/value is optimized, a company will focus on retaining only the high performers. Taking expected loss/value in consideration will help focus on factors that will eventually add most value.
Brian Sommer – TechVentive
Attrition analytics often focus on WHEN a person is at risk of leaving. According to Brian, it is however much more useful to focus on WHY and HOW an employee makes the decision to leave.
We already showed that employee attrition can cost a company millions of dollars annually. Traditional attrition analytics tools look at factors that indicate the intention of an employee to leave the company. By analyzing the employee’s behavior, these tools can quite accurately predict the chance of this employee leaving. However, these traditional attrition analytics tools could provide much more value by analyzing what drives employees to start thinking about leaving.
Predicting doesn’t have to be difficult. By putting the right strategies in place, HR can prevent attrition and steer employees’ career paths. However, the success all depends on getting the right metrics. In the meantime, Brian is looking at the firms that have sorted out this question of when, why and how employees decide to resign.
Both Brian and Andrew (#3) attempt to look deeper than the traditional churn analysis in order to find more useful information. Their articles forces people analytics practitioners to intensify their thoughts about what they really want to analyze – and how they can help the business in the best way possible. Both articles stimulate the reader to think differently about the way they analyze data!
Patrick Coolen – ABN AMRO
The number 1 article is a crowdsourced list of 10 rules of HR analytics. Patrick managed to get various experts in the people analytics field together to work on the list. This list expands on Patrick’s 2015 version of this list.
According to Patrick, the number of organizations that feel fully capable to apply HR analytics has doubled in the last year from 4% to 8%. That does sound positive, however, this also means that 92% of organizations don’t feel fully capable.
Patrick assembled 10 rules that will help businesses apply HR analytics to their organization. The rules include #1 – Go beyond statistics, #3 – Create actionable insight, and #6 – Create a clear process. By following these rules, organizations can take a smarter look at their data and transform their analytical capabilities. All 10 rules are highly relevant to anyone working in the field of HR analytics, so I definitely recommend reading this article.