2020-10-15 HR Examiner article The Emerging HR Data Department stock photo img cc0 by AdobeStock 245562438 544x313px.jpg

A new era in HR management will evolve over the next two to five years. What we’re talking about here is the emergence of a new set of analytics-driven data-based elements of the HR department. Read more in, The Emerging HR Data Department.


The HR Data Department


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).


Over the next two to five years HR’s most important asset will be its data. AI and intelligent software tools are swarming the HR Technology stack. Because these tools are being introduced without much in the way of customer acceptance testing, they’re likely to spawn a new era in HR management. Let’s take a look at what I expect to see in more detail.


What we’re talking about here is the emergence of a new set of analytics-driven data-based elements of the HR department. Not only will intelligent tools change the scope and focus of the work of HR (as it will for the rest of the organization) ever-increasing volumes of data will push some parts of HR into roles that look very much like operations. HR will accomplish things that will improve workforce agility and productivity and move beyond a cost center. As both the keepers of the data and an integral part of the functions that data depends on, HR has an opportunity to move into a more strategic role.


As I mentioned earlier, the most important asset in the emerging HR Department is its data. This data can be used to make an important difference to the organization, including better automation, insight, productivity, and organizational safety while monitoring and intervening in a variety of settings.


While service delivery and execution is the principal job of the HR Department, it will evolve quickly to become a deep source of actionable insight into the company’s workforce and its adaptation to market circumstances. The transformation to data-centric operations will be driven, in part, by the move to a standard conversational interface with HR. Today’s chatbot overpopulation problem will be resolved with a single, department-wide utility. And that is what drives the completion of digital transformation in HR.


Elements of The Emerging HR Data Function


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  • At the heart of the HR Department is its data. The data comes from three places: within HR, within the company, and outside the company. This section includes the responsibility for the cleanliness and quality of data, Each segment is exploding in volume and relevance.

  • Internal HR Data is the primary asset of the HR Department. It is the stuff that is collected, processed, analyzed, and disseminated by the company’s HR Technology stack.

  • External data is the foundation of new ways of seeing the company. From the sorts of employment branding feedback found on Glassdoor to predictive sourcing tools like EngageTalent, we’ve just started to understand how data from outside the company can shape internal experiences and operations.

  • Data from Operations and the Rest of the Business is where one gets the ability to see the actual work of the company. In order for HR to effectively show its impact on critical business operations, it needs data from the rest of the company.


For maximum utility, this data will be stored in a Data Lake. Data Lakes offer the capacity to store data in disparate forms in order to uncover their utility in later stage examination. Ddata lakes and data warehouses are both widely used for storing big data, but they don’t have the same function. A data lake is a vast pool of raw data, the purpose for which is not yet defined. A data warehouse is a repository for structured, filtered data that has already been processed for a specific purpose.


Uses of Data (Internal HR Processing)


Not all uses of data are the same. And some uses will change significantly.


  • Reporting: Once a measurement becomes routinely consumed, it is a report. Reports look at and visualize movement, activity, progress, or variance in a particular area. Many reports begin their lives as analytics.

  • Analytics involves a deep examination of a particular organizational dynamic. When an analytic becomes routine, it is a report.
    • Predictive analytics is a subset. This is the use of statistical tools to forecast outcomes in specific areas.

  • Data Science Lab: Most organizations over a certain size will have at least some of the functionality of a data science laboratory. The data science lab will allow the organization to model and understand the functioning and behavior of their organizations. Current offerings like Visier will inevitably morph to include model development by their clients.

  • AI Lab: AI comes to the organization in overt and covert ways. The primary function of the AI lab is to understand and manage the various forms of intelligence as they spread. The management and maintenance of intelligent tools happen here.


Interrogation of Data


There is a difference between the way that data is managed and developed and the way that it is delivered. The distinctions can be a simple format in the case of reporting.


2017-04-21 HRExaminer photo img sumser john bio pic IMG 3046 black and white full 200px.jpg

John Sumser is the Principal Analyst for HRExaminer.

  • Operational (History): Reporting is always backward-looking. The delivery of reports is a summary of what happened.

  • Real-time Surveillance: The surveillance category contains any real or near-real-time look at the behavior of the workforce and can range from trouble spotting to pulse engagement surveys.

  • Discovery and Automation: With intelligent tools, the HR team can begin to ask scenario-based questions (What if) and start to explore patterns for clues to enhance organizational performance.




Intelligent tools and data confer new and important responsibilities on the HR Department. These new missions are all data-centric. The main themes are:


  • Security: According to the Sierra-Cedar HR Systems Survey, 40% of HR Departments are responsible for the security of Personal Identifying Information (PII). That’s where HR’s responsibility begins. The degree to which HR produces proprietary and sensitive information expands with each new experiment in Data Science or AI. HR’s focus on the company’s overall security will focus on the human element as tools like KeenCorp deliver. HR is going to be responsible for continuing to protect the company in new ways.

  • Safety is related to security. The 21st Century organization uses its data to explore, predict, and report safety and well-being issues. Group trust, discrimination, bullying, harassment, and other behaviors can be detected in data and will be approached as health and safety concerns rather than compliance issues. HR will intervene before a victim reports much in the same way that industrial plants detect machine maintenance and potential failures. Privacy is also a safety issue and there will be acknowledgment and discussion that the ways we collect and use data have real consequences on people who are the subject of the data. We will see more governance issues involving who has access and how the data is used.

  • Ethics isn’t a rulemaking function. Ethics is a set of evolving questions that guide the company through complex decision making. External scrutiny, in the form of social media, reviews, journalism, customer reviews, news, and political visibility will continue to expand. Transparency becomes a constant because the only way to do things right consistently is to assume that you are doing them in public. As a result, ethics becomes a constant conversation that learns to take account of current circumstances and adjust.




  • Experiences: While it never rises to the level of the employee’s complete experience of work, much of HR’s work involves the delivery of experiences. As conversational tools become HR’s interface with the workforce, the department will be responsible for understanding and improving the employee experience of HR. This ranges from routing interactions about benefits and payroll to more complex involvement with performance management.

  • Personalization: Compensation, benefits, career path, performance evaluation, promotion, discipline, termination, and all of the other HR interactions are extremely personal. The more that data can be used to reduce the friction implicit in a relationship where employees feel vulnerable, the more HR can become the glue for the organization.

  • Products: While HR’s use of intelligent tools, data science, and people analytics is evolving, it will produce formal prioritization and budgeting processes to tackle what will be a huge backlog of projects. The best model for this sort of output is the product release process currently used in software development. HR will be developing services that have many characteristics of other subscription processes.


The HR Data Department will have a number of other elements. I’ll pick up next week with The HR Product Manager. The HR Product Manager role will evolve from two longstanding threads: technology implementation and agile management processes. See you next week.


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).

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