Welcome to the 1-year anniversary edition of our ‘Most Trending Articles’! This month we’ve got another amazing line-up, highlighting several of the best HR analytics articles of September. Enjoy.
#5 Leading Analytics Teams in Changing Times
Coming in at #5, we have an article from Lori Bieda who talks about leading analytics teams in changing times.
It seems to be prime-time for analytics teams to shine. This is due to open source technology, availability of data and the increasing value of data. But how can it be that at the same time data analytics teams are not performing at their full potential?
First, she notes that the current demand for analytics outstrips supply – there is simply too little talent. Much of the available talent moves from one company to another, allured by increased salary or interesting career opportunities.
Additionally, management itself is not yet well versed in analytics. At the same time, analytics teams fail to communicate their message effectively.
She also notes that the promise of analytics is not yet at the same levels as the harsh reality of data. Data is still plagued by issues of cleanliness, connectivity and availability.
These three issues have to be resolved in order for data analytics team to shine in their respective companies, as we all want them to.
Read the rest of the article here.
#4 People Analytics: What makes for a high-performing employee?
Article #4 is an insightful article from Ridwan Ismeer about performance metrics. He shows how people analytics models, despite their best intentions in measuring performance, can fall flat for several reasons:
- Bias: Qualitative feedback is often prone to bias. This makes performance appraisals difficult to measure objectively;
- Incomplete pictures: Quantitative feedback is usually much easier to work with, but it often doesn’t reveal the full picture;
- Recency: Performance is dynamic and nuanced over time. A single score at the end of the year is never going to capture anything useful;
- Inconsistent Rating: What one manager considers to be “acceptable” performance, another may consider “not meeting expectations”.
It’s not all doom and gloom, however. Building good performance models is not impossible. Want to know how? You can read the rest of Ridwan’s article here.
#3 Demystifying HR analytics – it’s not what you think
There are a lot of misconceptions about HR analytics. Is it about HR? Is it about analytics? Jonathan Ferrar has given us another great article in which he demystifies what HR analytics is and isn’t about.
In his article, Jonathan explains that HR analytics is more about the business than HR and analytics respectively. It’s about making sure you have a tangible business impact, a destination. Without a destination, analytics becomes confusing, complex and unnecessary.
When a manager wants input on whether or not to fire someone, he’s not looking for an in-depth analysis on how you arrived at your recommendation. He just wants the recommendation.
As Jonathan Ferrar puts it:
“While in life it may be about the journey, rather than the destination, when it comes to analytics, it is all about the destination”.
Read his full article here.
Bonus: HR Tech World
While all the articles in this post are written in the past, there’s also something coming up in the near future.
Analytics in HR is press partner of HR Tech World Amsterdam. This event, which is also referred to as ‘the greatest HR show on earth’, features a line-up of international top speakers in the HR tech world. To attend the two day event taking place on October 24 and 25, register via this link to get a 10% reduction!
#2 HR Analytics: Using Machine Learning to predict Employee Turnover
Employee turnover is a major cost for most organizations and a key issue for HR departments around the world. HR analytics can help. However, how do you estimate people’s flight risk? Our #2 article, written by Matt Dancho, has the answer.
In his article, Matt shows us the exciting new opportunity machine learning has in predicting employee turnover. He shows you step-by-step how he was able to predict employee turnover using several new cutting-edge machine learning techniques.
“With advances in machine learning and data science, it’s possible to not only predict employee attrition but to understand the key variables that influence turnover.”
You can find the full article here.
#1 Seven rules for spinning analytics straw into golden results
This month’s must-read article comes from Richard Kelly, Subu Narayanan, and Mark Patel. They talk about the experiences they’ve gained from their work with manufacturers around the world in creating measurable, sustained impact with analytics. Although they don’t specifically talk about HR, you can recreate their success in your own analytics team by following their seven golden rules:
- Start simple, with existing data;
- Capture the right data, not just more data;
- Don’t let the long-term perfect be the enemy of the short-term good;
- Focus on outcomes, not technology;
- Look for value across activities as well as within them;
- Break out the pilot trap;
- Build your capabilities.
They note that it is easy for companies to get started and get some quick wins. However, it is much harder to scale across the company and deliver consistent bottom-line impact. What should organizations do to get there?
Read the full article here and start employing these golden rules within your own organization.