This article is the first in a four-part series explaining how to implement Scientific People Analytics (SPA). SPA is the application of the scientific method to people analytics as opposed to the non-scientific approaches currently used by most companies today.
Part 1 of the series provides a rationale for using SPA. Part 2 explains why SPA is useful or supporting human capital decisions made by organisational managers. Part 3 presents a methodology for deploying Scientific People Analytics, and Part 4 compares Scientific People Analytics to non-scientific people analytics approaches.
Scientific decision making in organisations
Because the social and health risks associated with the release of a new drug are significant, pharmaceutical companies are legally required to deploy a process – known as a clinical trial – to demonstrate, for example, that the proposed drug is fit for purpose and that it does not result in unmanageable side effects.
Clinical trials, in turn, are based on a based on the scientific method, a process which uses experimental design and what business people might refer to as a form of analytics to establish causality beyond reasonable doubt (Figure 1).
Figure 1: Proving that Drug A leads to the desired outcome
But the scientific method is not confined only to manufacturing and R&D functions in business: marketing, procurement and finance functions have benefited from its use for at least the last 20 years.
For example, prior to investing in a campaign, analytical marketers use A/B testing – a technique based on the scientific method – to demonstrate which marketing campaign will deliver the greatest return on investment (Figure 2).
It is not in the interests of technology companies to promote high-level analytics techniques as these would harm their sales. Instead, it is in the interest of technology companies to promote people analytics as a technology-led and technology-intensive discipline.
Figure 2: Proving that Campaign A leads to the desired outcome (below)
Yet when it comes to people analytics, only a few companies – like Unilever for example – use the scientific method to guide their investments in people programmes (see Figure 3).
Figure 3: Proving that a compensation programme is leading to a desired outcome
This is remarkable because many companies that are using the scientific method to improve their marketing, procurement and R&D decision making choose not to use it when it comes to guiding their people investments. This being despite the fact that they spend more on people than they do on marketing, procurement and R&D combined.
Instead, these companies focus entirely on low-level people analytics techniques like HR reporting, visualisation and dashboards to deliver their people analytics results.
Why are these companies not using the scientific method?
There are at least two possible reasons. First, many lower level people analytics approaches can be learned at post-conference workshops or 1-week courses. In contrast, the scientific method requires a significantly higher educational investment, typically a postgraduate research qualification. It may be that some HR functions, unlike their marketing and R&D counterparts, are not willing to make this investment.
The phrase ‘low-level analytics delivers low-level returns’ has never been truer.
Second, the scientific method is not widely publicised because it is not in the interests of technology companies to promote it in their marketing messages. This is because the scientific method requires significantly less technology to be effective than lower-level analytic approaches, which rely on vast quantities of data and technology upon which to display it (rather than analyse it).
It is therefore not in the interests of technology companies to promote high-level analytics techniques as these would harm their sales. Instead, it is in the interest of technology companies to promote people analytics as a technology-led and technology-intensive discipline.
Low-level analytics versus high-level analytics
There is, of course, a place for lower-level analytics approaches like dashboards, visualisation and HR reporting in people analytics; in fact, they are essential for statutory reporting and problem identification.
However, unlike the scientific method, they cannot establish causal links between people processes and desired business outcomes, they cannot be used to identify those people processes that require modification in order for the business to achieve its business objectives.
The result is that companies that focus purely on low-level people analytics are most likely wasting a significant proportion of their human capital investments. Yet these companies have no idea how much they are wasting nor how to address the issue.
The phrase ‘low-level analytics delivers low-level returns’ has never been truer and is undoubtedly leading to Gartner’s Trough of Disillusionment in the people analytics industry.
We call the application of the scientific method ‘Scientific People Analytics’ and the next article in the series shows why Scientific People Analytics is particularly well suited to supporting human capital decisions made by business managers.