HR is not yet using people analytics to improve business outcomes, argues workforce analytics advisor Max Blumberg. How can we address this? With better education.
While people analytics use has increased dramatically over the past three years, its focus – for the most part – still lies in tactical operational activities such as employee retention and recruitment. If HR wishes to contribute more to the corporate strategic agenda, people analytics will need to up its game in the eyes of senior general management.
This article proposes a curriculum for HR and people analytics practitioners, as well as senior general managers, wishing to implement strategic people analytics practices.
It’s important to note that the content below is not something that can be simply learned in a one-week analytics course or by reading further upon the subject. To transform people analytics into a practice that generates true strategic value, HR professionals need to recognize their knowledge gap in this domain and invest in the appropriate training for their people.
The senior general management perspective
The most useful people analytics deliverables for senior general managers are those that help to maximize business outcomes by strategically guiding their human capital decisions.
In commercial organizations, these outcomes include, for example, total return to shareholders, profitability, and revenue. In most industry sectors, business outcomes are driven by a set of key performance drivers (KPDs), such as productivity, innovation, quantity, and customers.
Senior general managers (SGMs) achieve these outcomes by optimizing investments across a range of competing resources including tangible assets (e.g. machinery and robotics), financial capital (e.g. cash and bonds), and intellectual capital (e.g. human capital and customer capital).
A number of reviews suggest that current people analytics practices fall well short of providing these deliverables. For example, Marler & Boudreau (2017) report that less than one-third of Fortune 500 companies use people analytics to measure business outcomes.
That is, rather than delivering strategic investment insights into KPDs and business outcomes, most people analytics projects tend to focus on tactical improvements to workforce capability improvements (such as engagement and retention), and people processes (such as recruitment, learning management, and succession planning) as illustrated in figure 1.
This is not to suggest that tactical workforce capabilities are unimportant. The problem, however, is that people analytics tends to focus on tactical workforce activities that exclude strategic investment advice required by SGMs.
How can people analytics better support SGMs?
To develop strategic people analytics practices, people analytics practitioners will need to learn more about SGM strategic investment activities, and SGMs will need to learn more about the special characteristics of investing in human capital.
The following curriculum recently delivered to senior MBA students at Leeds University Business School, may be useful for both people analytics practitioners and SGMs.
This curriculum, broken down into four key parts below, is designed to supplement rather than replace existing people analytics educational programs. It has been outlined here to give HR an idea of the key areas that are incorporated into training to evolve a business’s strategic people analytics offering.
1. The role of assets and capital in generating value
The curriculum opens by examining how an organization’s combination of resources, such as human capital, AI, and finance, are deployed to achieve its desired objectives. This includes a review of organizational theory and, in particular, the resource-based view, which determines the unique combination of resources and assets required to achieve sustainable competitive advantage and business outcomes.
2. Value optimization from a human capital perspective
Armed with techniques for identifying the resources required to deliver business outcomes, analysts next need to consider how best to optimize investments between resources and within each resource:
a) Allocating investments between resources
While allocating investments between resources is more likely to fall to SGMs, they will rely on people analysts to help them understand how to optimize investments between human capital and automated systems such as AI.
Useful tools for this approach are strategic planning, production possibility frontiers, and optimization modeling.
It is likely that some combinations on the curve will generate more profit than others. Optimization modeling can be used to determine the most profitable points.
There is a variability of returns achieved from human capital investments compared with investments in automation and robotics. The message here is that if people analytics practitioners cannot find ways to modify peoples processes so as to reduce the variability of human capital ROI, SGMs will generally prefer automation because outcomes are easier to forecast.
b) Investment allocation within an asset class
Once the level of investment for human capital has been established, analysts must then determine how to allocate investments across the competing people processes.
To this end, here are five human capital investment strategies proposed by Phillip’s (2005) to consider:
- Avoiding human capital investment by using individuals with pre-existing competencies such as contractors or ‘poaching’ from competitors
- Minimal investment by minimizing wages, benefits, and training, and how to cope with the resulting high levels of attrition
- Benchmarking in the sense of copying the human capital investments of competitors
- Investing over the top, which is when firms overinvest in human capital in order to improve retention, thereby effectively reducing ROI on human capital investments
- Increasing the level of human capital investment while ROI is a positive and halting investment when ROI begins to decline.
The ROI approach is what should lie at the heart of people analytics.
3. Linkage analysis
The next key understanding for developing a strategic people analytics function is linking business outcomes and KPDs to yield a Value Profiling model shown in figure 3.
Value profiling (adapted from Cantrell et al, 2006) demonstrates the causal chain linking people processes to business outcomes. Three important people analytics principles emerge from this model:
- The end goal of people analytics projects should be to improve KPDs (and therefore business outcomes) rather than the current practice of focusing on workforce capabilities.
- The Value Profiling model shows that managers can only intervene directly at the level of people processes. They cannot make direct changes to workforce capabilities, KPDs, and business outcomes because all of these can only be indirectly affected by changes to people processes. The outcome of any people analytics initiative should therefore be to guide changes to people processes in order to improve KPDs and business outcomes.
- To remedy a problematic business outcome, analysts must work backward through the Profiler starting with the problematic business outcome and identifying the problematic KPDs, workforce capabilities, and people processes that are causing it.
4. Which analytical technique is best?
The curriculum closes with a review and comparison of various people analytics techniques.
Operational reports typically provide a snapshot of current workforce data using tables, visualizations, organizational network analysis diagrams, and dashboards. They require significant technology investments to store pre-existing data; but while this data is useful for statutory and tactical ad hoc reporting, it is seldom sufficient for guiding strategic SGM investment decisions. Typically, data for guiding strategic decisions must be specially generated and collected.
Data mining uses statistical and machine learning techniques to look for patterns in data available to the organization. This usually includes people data and may be supplemented by additional internal data (such as ERP data) or external data (such as ‘big data’).
For example, data mining may reveal a relationship between employee turnover, qualifications, tenure, and travel time to work. As with operational reporting, however, it is unlikely that organizations will “happen” to have data useful for guiding strategic decisions.
Data mining can, however, be useful for generating hypotheses for use in scientific people analytics.
Scientific people analytics
Scientific people analytics differs from operational reporting and data mining in that instead of starting with pre-existing data, it starts with a specific strategic business outcome problem and then works backward through the Value Profiling chain using statistical modeling to identify and modify problematic people processes which underpin it.
The key advantage is that scientific people analytics is more efficient and more effective than reporting and data mining at solving specific strategic business problems.
The key disadvantage of scientific people analytics is that whereas other techniques can typically be learned over a series of short courses, scientific people analytics requires postgraduate education in areas such as statistical modeling, research methods, and business domain knowledge. The returns, however, are usually worth it.
Taking people analytics to the next level
Many SGMs question returns on people analytics initiatives because not only do these projects seldom measure their own ROI, but they also fail to deliver guidance for helping SGMs with strategic investment decisions.
To address this, HR needs to better educate its people analytics teams in the areas of investment and strategy outlined above. This will help to ensure that HR does indeed contribute usefully to organizational strategy.
The post \"Why HR needs to up its game in Strategic #PeopleAnalytics\" was first published by Max Blumberg here https://www.linkedin.com/pulse/why-hr-needs-up-its-game-strategic-people-analytics-max-blumberg/
About Max Blumberg
People Analytics for Sustainability | Organizational Design & Development | AI | Sales Force Transformation | Digital Transformation | Corporate Finance