The Problem with HR

Photo by Gilles Roux on Unsplash

Background

HR is the weakest link in a knowledge economy.

Many of you may know that my business consulting partner (Sally) and I do a lot of work in HR Analytics; the domain is also commonly known as People Analytics. Since founding our consulting firm a decade ago, we’ve worked with top names in Singapore and Indonesia, both in the government and private sector, to shape and optimise their HR Analytics capabilities, ranging from setting up the HR Analytics function to creating HR metrics and dashboards to developing performance management frameworks to predicting employee attrition. We are often seen as pioneers in this domain in both countries. In these 10 years of HR Analytics consulting, I have been troubled by the pace of progress within the HR community. And so I dedicate my 5th article of this series in which I call out the bad practice and bad thinking in HR when it comes to data literacy.

Unprogressive HR

Over the past decade, the sophistication of HR tools have increased dramatically, but there was no corresponding growth in the sophistication of data-driven thinking for HR practitioners. Consider the following:

1. HR’s primary approach to data collection is the use of surveys — from training feedback to employee engagement pulse checks. Despite that, many HR folks are unschooled in survey designs (e.g. crafting the right questions, removing leading questions, etc.) and survey interpretations. Digitally-driven surveys typically have <10% response rates and are skewed towards negative sentiments — so the survey results are generally not representative of the target population if taken untreated.
2. HR folks continue to work with latent variables without the ability to deconstruct them into data representation — e.g. employee engagement, employee experience. Most marketeers can define customer value but most HR folks cannot define employee value.
3. HR takes a superficial approach to skill-based talent management and development. Pivoting to a skill-based organisation requires just 3 things: (i) defining an organisation-relevant set of skills & competencies that is internationally aligned, (ii) ability to measure the level of skills & competencies of each employee, and (iii) ability to link and correlate learning assets to improving the proficiency of said skills & competencies. I’ve not seen HR folks having the ability to do all three.
4. HR plays no significant role in optimising departments and employee behaviours through the robust definition, calibration and alignment of performance metrics. Their contribution to the metrics landscape is the cascading of the balance scorecard (BSC) framework to employee performance scorecard, which many are saying is outdated in the modern economy. There is a lot of evidence that the methodology does not deliver the desired outcome and often results in unconsidered issues such as negative reinforcement and conflicting effects. I’ve done a lot of work both in my past corporate capacity as well as in my current consulting on sales performance and sales incentives, and have found that when HR is involved in the design framework, they introduce a lot of sub-optimalities due to poor thinking on employee and organisation behaviour.
5. HR is unable to quantify the impact (not outcome!) of re-organisation and are unable to provide meaningful advise on the matter. Consider the act of delayering the organisation — e.g. in Sep 2023, Citibank announced the removal of 1 layer of senior management reporting into the CEO. HR takes on the obvious task to assist with the change management, or rather pain and anxiety management instead of quantifying what organisation value and capability is lost through the re-organisation.

Re-wiring HR

HR has consistently shied away from quantifying its (economic) value contribution to the organisation, shielding itself from existential questions. HR considers itself an ‘essential’ function; it simply exist and often don’t feel threatened in re-organisation activities. Hence there is very little pressure for the function to adapt its capabilities to the new knowledge and network economy.

HR has been slow to upgrade its internal HR metrics that define the function’s success. Recruitment teams need to incorporate comprehensive accountability metrics for new hire performance. HR needs to be held accountable for defining and increasing employee value. HR needs to be responsible for aligning the organisation’s investment in re-skilling and up-skilling with extending the employees’ skills shelf-life and increasing employee lifetime value. HR needs to conduct workforce planning not based on job descriptions and headcounts but based on the carrying capacity derived from the aggregation and combination of skills. In short, HR needs to be accountable for outcome and impact, rather than just output (see my previous article that discusses the metrics continuum).

This is where HR Analytics need to be focused and prioritised; solve for the foundational problem statements — defining and increasing employee value (instead of justifying outputs on valuable employees), defining, measuring and developing organisationally-aligned skills, understanding how work gets done in a networked and collaborative knowledge economy, and then influencing and attributing employees’ contribution.

Granted that tertiary institutes have been upgrading their undergraduate and graduate programmes in Human Resource Management (HRM) to incorporate modules in Statistics, HR Metrics, Survey Design and Workforce Planning. However, these topics are covered without sufficient technical and analytical depth. Traditional tools such as Excel and Pivot Tables are utilised to teach descriptive analysis instead of modern data visualisation tools. Traditional statistics are taught instead of information theory and decision science. Many HR graduates are still woefully unprepared to enter into the knowledge economy.

Conclusion

So what now, brown cow? How should HR orientate itself in a networked knowledge economy? Increasingly, HR needs to re-cast itself as an enabler rather than a governor, and to that end, its success metrics will naturally evolve from outputs to outcomes and impact.