5 Critical Skillsets for HR Analytics

Increasingly, companies are applying analytics and data science procedures to new areas of their business. Human Resources (HR) management, with its central role in managing the People in a business, is one such area. HR Analytics is a fact-based approach to managing people. A fact-based approach helps organizations validate their assumptions about how best to manage their people. This makes good business sense: on average, companies spend 70% of their budget on personnel expenses.

Using data and statistical methods, HR may look to examine people-oriented questions, such as:

  • Can we better understand employee absenteeism rates at a labor-intensive business, such as retail, food service or industrial manufacturing? Can we predict it?
  • Do our compensation realities reflect fair and balanced job classification policies? Asked differently, which factors are most predictive of compensation: ones we want to reward (i.e. education level, on-the-job performance) or ones we need to ignore (i.e. gender, age or race)?
  • What is our real employee churn rate? Can we identify employees headed out the door and take preventive steps?
  • Are our service response times keeping pace with spikes in customer demand?

These questions, and many more, can be answered with datasets, data science and statistics.  But how?  Analytics involves skill sets that go beyond those considered “traditional.” Knowledge of recruitment, hiring, firing and compensation are key to understanding HR processes. However, HR professionals often struggle to answer these questions in a data-driven manner, because they lack the diverse skills required to perform advanced analytics. These skills include statistical and data analytical techniques, data aggregation, and mathematical modelling. Finding the right data can be another challenge. Data analytics requires data, and that data is likely to reside in several different systems. IT professionals play a critical role. Finally, communication to the business is a key skill. HR Analytics projects may produce analysis and models that contradict conventional wisdom.  Action on these insights requires the team to communicate the what, why and how’s of Data Science.

To be successful, HR Analytics projects require five distinct skillsets to be successful in creating value for an organization.

  • Without Business input, HR Analytics projects may answer questions with no value added to the organization.
  • Without Marketing input, insights from HR Analytics will fail to be adopted by the business.
  • Without HR input, the team will struggle to recognize relevant data and interpret the outcomes.
  • Without Data Analytics input, analysis will be “stuck in first gear” – producing basic descriptive statistics (i.e. Averages and Totals), but never advancing to diagnostic (i.e. root cause) or predictive (i.e. Machine Learning) models.
  • Without IT input, the team struggles to acquire relevant data in a usable format.

HR leaders must engage all the required perspectives and skillsets to be successful with analytics. Business, marketing, HR and IT are common perspectives found in most organizations. But Data Analytics professionals, able to cleanse data, identify candidate predictive models and evaluate model output, are typically lacking.  We encourage HR professionals, interested in learning more about The Power of Data, to reach out to our Data Analytics Advisory Services team. Our goal is to help you understand the data science process, identify business opportunities, and potentially offer analytics services that fill in the missing pieces for your puzzle.

Want to learn more about the world of HR Analytics?  We are speaking at this year’s CBIA Human Resources Conference on the topic. We hope to see you there!

Berry_Brian-240About Brian: Brian Berry leads the Microsoft Business Intelligence and Data Analytics practice at BlumShapiro. He has over 15 years of experience with information technology (IT), software design and consulting. Brian specializes in identifying business intelligence (BI) and data management solutions for upper mid-market manufacturing, distribution and retail firms in New England. He focuses on technologies which drive value in analytics: data integration, self-service BI, cloud computing and predictive analytics

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