This is already the sixth edition of ‘Most Trending Articles’!
Once again, we’ve selected the best (HR) analytics articles of this month. I put HR between parenthesis because one this month’s articles is about more general analytics. However, I am sure you will appreciate this article as it provides some very valuable lessons to the people working in the HR analytics field.
#5 Building and Scaling a People Analytics Practice with Limited Resources: 10 Guidelines for Success
Many of us who implement people analytics have to work with limited means. Craig Starbuck’s article offers 10 great tips for building and scaling a people analytics practice.
His tips, which are relevant for both starting and more experienced analytics leaders, emphasize the importance of key elements like a shared vision, quality of data (analysis) and process automation. It even includes a practical list of ratios to include in an analysis!
All in all, a great overview of practical tips on how to build and scale people analytics. Read the full article here.
#4 What constitutes best practice in people analytics?
While we are still on the topic of practical tips: David Green has some great ones for you. His article lists 16 best practices in the implementation of people analytics.
Examples of these practices include a focus on the business, an involved CHRO and leveraging resources from outside HR. According to David, analytics is a long-term investment with great potential in terms of helping the employer save cost and helping the employee.
The final best practice David mentions is to not forget the ‘H’ in HR analytics. People analytics is about people and it’s thus important not to forget the human touch. Because: Data + Judgement = Problem-Solving.
This month’s number 4 and 5 go together very well and offer a comprehensive overview of the elements to think about when developing and scaling people analytics capabilities.
Read the full article here.
Related: 11 Key HR Metrics
#3 What teaching People Analytics has taught me about teaching People Analytics (Part 2)
Sam Hill published his second part of “What teaching People Analytics has taught me about teaching People Analytics” this month.
His article offers some great insight in the common traps people fall into when they start with people analytics. For example, there’s not one ultimate people analytics goal. Different organizations strive for different targets and maturity levels and it is important to acknowledge this.
There’s also a question that any teacher of people analytics should never answer! Want to know which question?
“What are the top 10 KPIs used by organizations?” Sam calls this metrics rubbernecking. What if these top 10 KPIs are used because they are the easiest to calculate? Or what if they relate to a challenge that is not relevant for your company at all?
The take-home message is to focus on the KPIs that are relevant to your business and develop those. These and other tips can be found in Sam’s article.
#2 The 30 best HR Analytics articles of 2016
David Green’s articles are hard to miss in the people analytics field. This month two of his articles are featured.
This article lists the 30 best HR Analytics articles of 2016 and is a great recap of last year’s progress in the field.
Articles include the “Six must-have competencies in a world-class analytics team”, “Still Under Construction: The State of HR Analytics” (a report we summarized on our website), and Patrick Coolen’s “The 10 Golden Rules of HR Analytics”.
To check out the full list, click here.
Related: A comprehensive list of 21 Employee Performance Metrics
#1 The Best Data Scientists Get Out and Talk to People
This month’s top article is written by Thomas C. Redman and published in the Harvard Business review. The article focusses on data scientists and offers a few valuable lessons.
A data scientist’s job involves processing vast amounts of data. In order to be good at his/her job, a data scientist should get out of the building.
The world is filled with “soft data” that does not show up on your screen. This soft data is needed to interpret the hard data that the data scientist works with. An example from the oil processing industry:
“Where the oil is thick, it is hard to pump out of the ground. To make this process easier, companies heat the oil first with steam. Steam is expensive and must be used according to strict ecological guidelines, so putting the right amount in is critical. A good data scientist can take many factors into account — the underlying geology, the current temperature of the oil, the well’s production history — to optimize the amount of steam.
But a great data scientist would also spend some time in the oil field. There, they would notice that the probe used to estimate current temperature is sometimes lowered into the well clean, while at other times it is covered with mud. As it happens, mud is a great insulator, leading to a “too-low” temperature and, in turn, too much steam. Having verified this through a simple experiment, the great data scientist will tackle the root of the issue, namely the lack of a work instruction advising the technician to insert a clean probe.”
This example applies one-on-one to HR. Does your data scientist know the organization’s basic HR practices? Does he/she know how performance is rated, or how employees are assessed for their future potential?
Knowing how these processes work helps the data scientist to work with and interpret data the right way. Read Thomas’ full article here.