Following these eight steps is critical to unearthing culturally relevant data insights.
By Michael Switow
There is no shortage of data for HR professionals. From recruitment data to promotions, productivity, absenteeism, timesheets, expenses, and retention metrics, data that can be used to craft better workforce strategies is being created every day. Yet many HR leaders struggle to realise the power of people analytics, choosing instead to rely on instinct and experience.
“At Pepsi, I was always going with my gut against the gut of every other leader,” recalls Fermin Diez, group director of human capital and organisation development and deputy CEO at Singapore’s National Council of Social Service. “Gut to gut, I lost almost every time. Data to gut, I won every time. If you really want HR to be a strategic business partner, then you need to persuade with data.”
Yet many HR professionals are reluctant, having joined the profession because they like working with people, not statistics. In addition, a lack of quality data and analytics skills are among the biggest barriers to embracing data analysis, according to webinar surveys by the artificial intelligence talent management platform Pymetrics.
“It’s easy to become overwhelmed. How do we bring the data together in a meaningful way?” asks Pymetrics’ Global Head of Consulting Psychology Michelle Hancic.
“Many companies are still storing data across different systems,” observes PeopleStrong’s Practice Leader for the Future of Work Adrian Tan, who works with companies to drive productivity. “You have the HRIS. You have an Excel sheet here, another one-trick pony over there. There’s a lot of duplication and human error. Trying to centralise all this into a single source of truth is a key challenge I’m seeing for many businesses in Singapore right now.”
Eight Steps to Better Insights
How can HR leaders overcome data paralysis and embrace analytical solutions? Diez, who is also an adjunct professor at Singapore Management University and co-author of the recently-published textbook, “Fundamentals of HR Analytics: A Manual on Becoming HR Analytical,” offers an eight-step process for HR departments to offer better insights and more precise recommendations.
1. Define a business problem. That might sound basic, but HR leaders often start with a human resources issue, like how to reduce attrition. That’s a means to an end, not a business goal. Think instead about how to improve corporate profits or quarterly revenue.
2. Formulate a simple hypothesis before digging into the data. Looking at the numbers too soon could lead HR professionals in the wrong direction. An example of a hypothesis that can be tested is: “An incentive system increases branch profitability.”
3. Collect data. Don’t stretch the team too thin—only collect the data needed for the analysis.
4. Analyse data. Keep an eye out for insights that reveal whether the hypothesis is valid.
5. Reveal insights. By organizing the information into a single source, organisations will be able to see if there are any trends.
6. Make an HR recommendation to improve the business situation. Look to the data and see what it says about a business issue. For example, “There is a single source of talent that will drive our sales higher.”
7. Tell a story. To make an effective data-driven argument, Diez suggests “ditching all the analysis” and crafting a powerful story instead. He notes that his best presentations are short, featuring a proposed solution on slide one and the data-driven rationalisation on slide two.
Slide 1: “The answer to the business question is…”
Slide 2: “I know this because…”
Slides demonstrating the data analysis should be kept at the end of the presentation as backup in case there are questions.
8. Monitor the impact. Implement the recommendation, then measure to see if the intended effect is achieved.
Data in the Age of COVID-19
All HR departments should make sure that their employees are analytically-savvy, says Diez, especially in the age of COVID-19.
“There has never been a more urgent need in HR for understanding in real time what employees are experiencing and how to impact them positively,” he explains. “Not much of what happened before can serve as predictors to what is happening now, so pulse surveys have become the tools of choice for innovative and progressive HR departments.”
In just one quarter, the pandemic forced digitalisation and work-from-home policies that C-suite managers had been discussing for years. But whilst pulse surveys are becoming a data source of choice, the results can be distorted by cultural considerations.
“In many settings, giving negative feedback is frowned upon,” notes PeopleStrong’s Tan. “So, when you put out a pulse survey, you’ll see that 99 per cent of people are happy. You’re going to get very skewed data unless you uncover ways to ensure that biases do not come into play.”
Understanding Cultural Nuances
For companies that operate across Asia Pacific, there are a number of other cultural differences to bear in mind when analysing and comparing data sets.
“Many firms in the APAC region operate talent acquisition functions across multiple countries with vastly varying talent markets, methods of recruiting, and regulatory requirements, all of which can pose challenges for ensuring data hygiene,” says Doug Terry, Cielo’s APAC client services director.
Cultural differences can impact how candidates interact with prospective firms, as well as the definition of what is considered a good benchmark for certain metrics.
For example, “no-show” rates for interviews are likely to be higher in India than elsewhere in the region, Terry notes, as it is common for candidates there to accept multiple concurrent offers. Japanese candidates, meanwhile, are less likely to respond to recruitment advertising as compared with candidates in Australia, India, and Singapore.
Without an understanding of country nuances, factors like these will skew the inferences HR leaders draw from regional data sets.
“Stay focused on what ‘good’ looks like at a market level and keep this in mind when aggregating data across the region or globally,” Terry advises. “It’s important to ensure data gathered across different geographies compares ‘apples with apples.’ This means that the various trigger points through the recruiting life cycle are consistent; otherwise, relevance can be lost.
“Making sure your organisation develops your approach to use data smartly—rather than trying to do everything right away—will help ensure you realise real benefits from data,” he concludes. “Using data well isn’t just the flip of a switch. It’s a gradual evolution.”