In the final article of this four-part series, Dr Max Blumberg compares the Scientific People Analytics (SPA) to other commonly used people analytics approaches. Which methodology will come out on top?
To briefly recap, part 1 of this series demonstrated that unlike marketing and manufacturing, people analytics has been relatively slow to adopt methodologies based on the scientific method.
Part 2 of the series introduced the concept of a Human Capital Value Profiler which:
- Demonstrates how organisations make human capital investments via people processes (see Level 4 in Figure 1)
- Links people process with business outcomes via the organisation’s workforce capabilities and key performance drivers (KPDs)
- Identifies and prioritises problematic people processes based on the extent to which they inhibit desired business outcomes
Part 3 of the series then went on to show how to modify problematic people processes and test the impact of proposed solutions using the Scientific People Analytics (SPA) methodology, which uses:
- Correlation to quantify the impact of people processes on business outcomes
- Isolation to demonstrate that these business outcomes are the result of the modified people process, rather than other possible factors (such as a change in the economy or new organisational leadership)
Scientific analytics is fairly standard in many other business functions, but is not yet widely used in people analytics.
This final article of the four-part series compares the extent to which three people analytics methodologies (reporting/visualisation, data mining and Scientific People Analytics) support executives in making human capital investment decisions in the service of achieving desired business outcomes. In particular, we will focus on the ability of each methodology to deliver two key criteria for executive decision-making: correlation and isolation.
Figure 1: The Human Capital Value Profiler
Correlation and isolation as the criteria for comparing people analytics methodologies
People analytics exists primarily to help managers make better decisions about human capital investments in people processes, with a view to achieving desired business outcomes. Better management decisions, in turn, rely on two fundamental analytical techniques: correlation and isolation.
1. Correlation: measuring the strength of relationships between metrics
Correlation measures the extent to which two metrics change together over time; it can be thought of as the strength of the relationship to metrics. In the context of people analytics as a tool for supporting management decision-making, we are particularly interested in correlations between people process, workforce capability, Key Performance Driver and business outcome metrics (see Figure 2)
For example, if the proportion by which employee performance and revenue change over time is extremely similar, we would say that employee performance and revenue are strongly correlated. If they don’t tend to vary much together, then their correlation is weak.
Example: Correlating people process metrics with ROI
In most commercial organisations, delivering adequate return on investment (ROI) to shareholders is a key business outcome metric.
If a company does not deliver adequate ROI, its shareholders usually withdraw their capital, its share price falls, and the company becomes undercapitalised resulting in either increased borrowing and debt, or layoffs and closure of facilities. In extreme cases, some companies simply collapse (as happened, for example, to Woolworths and Marconi).
Therefore, before investing in resources like technology, financial assets or human capital, executives usually ask their financial teams whether the investment is likely to yield sufficient ROI to meet shareholder expectations. In effect, they are asking about the size of the correlation between the proposed investment and the likely business outcome.
But there is an catch. While financial teams have methodologies for correlating investments in financial and tangible assets with business outcomes and ROI, they usually don’t have techniques for correlating human capital investments with business outcomes.
And because these correlations are unavailable, executives tend to view human capital investments as inherently risky compared with alternative investments where such correlations are available, like robotics and automation.
Hence rather than making investments with uncertain outcomes in cashiers, factory workers, and call centre representatives, for example, executives prefer to invest in automated supermarket checkouts, robotics and chatbots.
This is not to suggest that automation investments deliver greater ROI rather human capital investments: on the contrary, human capital investments may deliver greater ROI in the medium- to long-term. But what they do offer executives is greater certainty because correlations between investments and business outcomes can be measured.
What about correlations with other business outcomes?
Of course, ROI is not the only business outcome of interest to executives. Non-commercial organisations, for example, may also be interested in the impact of human capital investments on outcomes like patient care or public sector improvements.
Even in these non-commercial cases, the same principle applies: from an executive perspective, if their teams can’t correlate investments in an asset with the desired outcome, executives will tend to avoid investment in that asset wherever possible.
Click here to continue reading Max Blumber’s article.