“It’s the inherent benefits of a unified data asset, coupled with reduced costs and increased employee satisfaction that will drive the emergence of a new role: The HR Product Manager.” - John Sumser

The HR Product Manager in
The Emerging HR Data Department

A shepherd for a dizzying array of tools and operational complexity in a landscape dotted with legal and cultural landmines.

To better understand this topic please read, The Emerging HR Data Department, (1) The HR Product Manager in The Emerging HR Data Department, (2) The HR Product Manager in The Emerging HR Data Department – Conclusion, (3) and Building the Computational HR Flywheel in The Emerging HR Data Department (4).

Digital transformation has failed to deliver on either the digital or transformation parts of the promise. The reasons are many. In HR, the problem is largely caused by the way that functions are siloed and separated. Managing the value of the overall people-data asset, combined with the desire to deliver a uniform level of experience quality, will drive the emergence of a single conversational interface.

It’s the inherent benefits of a unified data asset, coupled with reduced costs and increased employee satisfaction that will drive the emergence of a new role: The HR Product Manager.

Currently, HR is managed as a series of loosely federated spheres of influence. Employees attempting to interact are often faced with extensive coordination in order to access services. One of the main roles the HR Generalist currently serves is as a human switchboard for the maze of HR services.

The HR Product Manager role will evolve from two longstanding threads: technology implementation and agile management processes. In agile development environments, the Product Manager is the person who, with dotted line relationships to everyone, is responsible for moving products forward. As HR increasingly becomes productized (you have to do this to deliver on the experience idea), the role of people who shepherd new ideas into the workforce takes shape.

HR Product Management will occupy a staff level function in the HR Department. The job entails gathering data, and insight from across functions in order to deliver new products, personalization, and/or experience. The primary function of the job is to turn digital transformation into a series of cross-silo, achievable projects while expanding the momentum of the projects that emerge from increasing measurement and analysis. These functions will be the drivers behind the evolution of employee experiences that deliver the entirety of HR to the employee.

The Consequences of ‘Columbusing’

Columbusing is urban slang for ‘discovering something that’s existed forever.’ The term is a reference to the fact that Columbus is credited with the discovery of a place that had been inhabited for thousands of years before his arrival. It’s a pejorative term that indicates a failure to understand history and the value of what’s come before.

Columbusing at the intersection of HR and Technology is increasingly common in the development of intelligent tools. It takes the form of applications that are developed without ever taking a look at the existing solution set. Bias and bias reduction come to mind. With decades of EEOC compliance, monitoring, and audits, HR Departments have learned how to manage bias at the regulatory level. Precious few of the new tools build on prior learning.

It’s similar in the various tools that measure culture. There are a solid 75 years of science and practice in the areas of cultural measurement and organizational effectiveness. Yet, few if any new intelligent tools vendors build on these historical foundations. You can also see the consequences of ‘columbusing’ in newly minted assessment tools.

It is unusual for HRTech vendors to open up their internal scientific processes to peer review even though the value they deliver is of the kind of science that needs public peer review.

The lack of transparency and review is another outcome of radically decentralized research and development (funding small projects or companies and saddling them with revenue demands). When venture financing dominates an industry’s R&D investment, lots of projects get started in places where earlier work is available. It’s unusual for founders to see this.

It’s worth doing research into the underlying science before installing a tool that is divorced from it. You risk the penalties associated with reinventing the wheel.

Employee Experience Demands Data Integration with Operations

Executed well, HR is mostly invisible to employees. The current emphasis on employee experience in HR is a shallow reflection of a much larger problem. The overall experience of the workplace, which is beyond the control of HR, is a critical component of an ever-engaging relationship with the workforce.

Operations is where real data about employee performance, sentiment, experience, accomplishment, and satisfaction is held. HR has always had a derivative view, expressed in secondhand reports of performance in reporting systems designed for merit plan distribution. Access to data from operations promises to unlock the longstanding promise that HR has a contribution to make in the area of employee performance.

While new performance management systems attempt to channel feedback about workplace value from a variety of sources into a channel for recognition and improvement, the measures are all second-hand. Operations data offers real-time performance appraisal among other things. Rather than measuring the equivalent of individualized surveys, real work data gives an objective context to the conversation.

There are all kinds of operational data from the entirety of digital communications to keystroke contents and rhythms. Contemporary Organizational Network Analysis tools are the beginning. Actual workplace data is the next step.

AI and the Focus on Business Outcomes

John Sumser is the Principal Analyst for HRExaminer.

There are techniques (like Structured Equation Modeling) that already allow a disciplined data team to develop reasonable arguments about the impact of HR on the bottom line. As the department gets accustomed to using data from the rest of the business, the ability to see the relationship between HR and organizational health and accomplishment will continue to grow.

It appears that there will be a growing emphasis on the way that the business delivers value to stakeholders other than the shareholders. I recall an announcement from the Business Roundtable that said that business should focus on investing in employees, protect the environment, and deal ethically with their suppliers. (The idea that shareholder value was the most important objective is only 25 years old and was promulgated by this same group.)

The pressure to understand investment and return in the workforce will drive deeper research in academia and in organizations. It’s reasonable to imagine a fairly different landscape for results reporting that evolves over the next five years. Measures of employee equity, impact of diversity, suitability of tools for future adaptation will all be experimented with. Models of organizational process, measures of organizational intelligence, and work process effectiveness (from a human capital utilization perspective) are all predictable elements of this trend.

Modeling the Organizational Culture

There is no element of the intelligent tools revolution that is more guilty of ‘columbusing’ than the various tools that claim to understand, measure, and influence culture. It’s as if none of the vendors were aware of the work of Peter Senge, Edgar Schein, or the other major thinkers on the topic of organizational culture and anthropology in general. Instead, they present shallow conceptions of culture that are likely to produce errant output. Already, we hear concerns from CEOs about whether ‘sentiment analysis’ produces anything that’s useful.

We understand precious little about the deep function of our organizations. Much of what is currently considered a measure of culture are wisps of sentiment and response to very immediate stimulus. Engagement surveys and other assessments of employee sentiment help to understand the current mood of the workforce. Culture and its implications are something much deeper.

If current tools measure something like organizational weather, culture is the terrain and geological structure. While the weather varies with the seasons, the culture itself is a slower changing thing with rhythms of its own. It’s going to take effort and investment to use the various network analysis tools to see the topography clearly and understand what is below the surface.

A comprehensive picture of how the organizational actually functions requires some sort of structural framework as a point of departure. The existing work of academics like Senge and Schein provide a point of departure. In the absence of a conceptual framework, the early going will include some humorous disasters.

I’ll pick this up tomorrow with complexity science, group intelligence, and the limits of specialized AI.

To better understand this topic please read, The Emerging HR Data Department, (1) The HR Product Manager in The Emerging HR Data Department, (2) The HR Product Manager in The Emerging HR Data Department – Conclusion, (3) and Building the Computational HR Flywheel in The Emerging HR Data Department (4).