Recently I had the opportunity to present a session at NHRD Pune Chapter (https://www.linkedin.com/pulse/nhrd-pune-chapter-presents-workshop-hr-analytics-nhrdn-pune-chapter)
As part of a day-long HR Analytics workshop. Apart from Feedback Consulting, other speakers included experts from technology firms TCS, IBM and research firms KPMG and Cerebrus Consultants indicating how Analytics as a field is at the interaction of research and technology.
I presented on the following topic of “How a data led approach can be instituted to streamline HR operations”
As a practitioner of Employee and Customer Experience over the years, I tried to simplify some statistical approaches researchers use and their applications in the real world. Some highlights from the session.
- Normal Distribution – The simple function that represents the distribution of many random variables as a symmetrical “bell-shaped”graph; yes the very dreaded Bell Curve which firms are abandoning in haste now. The challenge is Normal Distributions works best in larger sample sizes with minimum 30 employees, force-fitting the curve in small team sizes is probably the major reason for employee resentment and manager dilemma to identifying great performers and more importantly identifying relatively poor performers.
- Correlation – Correlation refers to any of a broad class of statistical relationships involving dependence; however it does not imply causation or intensity of the relationship
- Regression – Regression on the other hand actually is a statistical method to estimate the relationship and extent to which a dependent variable say employee engagement is a function of independent variables i.e. salary, work environment, growth and role of manager etc. this helps us identify high impact drivers and drivers which are hygienic factors and look at immediate actionizing
- Cluster Analysis – Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). This can be used to classify employees into various groups on
- Attitude
- Demographics
- Employees as salary focused, work culture focused or career focused
These were a few examples of how statistics can be used in HR analytics but what is very critical to follow is the underlying business needs should be defined up front, the framework i.e. conceptual thinking on what is the premise and the relevance of this in your organization context comes next and the tools serve a purpose of slicing and dicing the employee data.
