The deluge of data can be overwhelming for HR leaders who are launching into people analytics. Where does one begin, and how does one integrate data into their strategy?
Stéphane Michaud, senior director at Human Link Asia, Mitsubishi’s HR services subsidiary, was interviewed by HR Tech News regarding the science of data collection and analysis.
Michaud, who served as a panelist at the HR Tech Summit Singapore in April, implements a fundamental methodology. His recommendation is to construct your data from the ground up.
“One of the things that is frequently overlooked is the necessity of commencing at the beginning.” What inquiries would you like addressed? What are the obstacles you are currently encountering in your business? he stated.
HR directors will be able to prioritize the data sets to construct and the analytics tools to deploy by identifying workforce challenges early on. “What data and tools will assist you in addressing these inquiries?”
Michaud stated that HR analytics teams frequently initiate their efforts by “looking backwards” in search of the “nail to hit.” In other words, the act of initiating a high-level strategy and seeking a solution without first identifying the actual issue.
He stated, “The appropriate approach is scientific and evidence-based. Begin by determining the destination, the metrics required to achieve it, and the cost-effective method for monitoring them on a regular basis.”
Strategy and data
The analytics expert suggests that one conduct a thorough examination of correlations, which involves contrasting data sets and events.Measuring employee engagement serves as an ideal illustration. “We frequently employ engagement surveys to report observations and describe the data,” Michaud stated.
“If you examine the engagement survey results, you may observe that pay is the lowest-ranked motivator among employees. Consequently, you may conclude that it is necessary to adjust the compensation.”
“However, when you examine the correlations with engagement, it is not even among the top three.” Therefore, engagement will not be significantly influenced by pay. There are additional factors, such as career development and advancement.
In his decision-making process, Michaud employs evidence: “I have developed predictive models to determine the most effective factors that would influence engagement.”
However, Michaud also cautions against data saturation in the midst of everyone’s fixation on big data, which is the desire to measure everything.
“In my opinion, the frequency of reports must not be so high that they become an inconvenience to read,” he stated. “When you are prepared to make decisions, you require your reports.”
“A quarterly report is acceptable if you are prepared to make decisions on a quarterly basis.” You are not interested in reporting on every single detail. You are required to provide a report on the critical inquiries that will enable you to make informed decisions.
Michaud succinctly defines the analytics practice, a reference to his own agile, no-nonsense work ethic.
“In the context of data analytics,” he stated, “it is imperative that we pose the appropriate inquiries, acquire the appropriate data, and proceed with urgency.”