28 HR Analytics Terms Every HR Professional Must Know Part-II
In our second post on a glossary of HR analytics, we continue with a few more terms.
#1. Demand and supply forecasting
As the name suggests, demand forecasting means estimating the number of people required with the right skill set to accomplish certain upcoming tasks. Supply forecasting on the other hand implies estimating the number of people who will be available to accomplish a certain future task.
#2. Cost to hire analytics
Needless to say that all external hires come at a cost. Sometimes this cost can be hidden. For example, the cost of sifting through hundreds of resumes to find the perfect candidate or it can be as simple as cost of an advertisement for job vacancy. This is referred to as cost to hire analysis. It makes use of notable hiring data to evaluate and identify the various cost heads; and then suggests means to reduce or contain certain large cost heads.
#3. Joining probability analysis
HR professionals often have to deal with situations such as a candidate accepts an offer but backs out from joining the organization. Joining probability analysis includes building a probability score for all candidates, as an indicator of how likely are they to join. It successfully identifies profiles of candidates who are more likely to join, based on an analysis of their character and traits. A tool like this helps avoid the loss incurred when a candidate backs out from joining after receiving an offer.
#4. Hiring channel mix modeling
HR professionals make use of several channels while hiring candidates. These include employee referrals, recruitment consultancies, social media and so on. Hiring Channel Mix Modeling analyses previous hiring and identifies all channels that have led to hiring for an organization and the interplay between them. It can also go a step further and pinpoint the channels that have been the most fruitful in hiring.
#5. Attrition analytics
In order to understand attrition analytics, first of all we need to understand what is flight risk. Flight risk refers to the risk associated when an employee is looking to switch jobs. The HR must devise methods to retain talent as long as possible. Attrition analytics helps reduce flight risk by adopting a predictive approach and identify problem areas in advance.
Clustering refers to a type of machine learning that makes predictions by crowding data. The following example shows 1,000 data points divided in three clusters. Take a look at the image below. Machine learning makes it possible to make estimations of the different clusters. Additionally, when a new point of data is introduced, the algorithm is able to predict in which cluster it is more likely to belong.
#7. Training data vs test data
When you have a data set, you can definitely choose to develop an algorithm. But how would you know that the predictions you made were accurate? In order to find out about that, you would need another different set of data which is known as test data set.
Both training data and test data are created by splitting one full data set. The first part of this set is usually reserved for training purpose. This can be used for creating the next set of predictive algorithm. The second set of data is the test data. This data will be used, once the algorithm is created, in order to test, how accurate the predictions of the algorithm. Take a look at the image below for a better understanding:
Overfitting refers to a modeling error which occurs when a function is too closely for into a limited set of data. However, in HR analytics, it is related to machine learning. Machine learning is a complex technique and it can provide very detailed analyses. Since a lot of detailing is involved, it is at a risk of ‘overfitting’. This implies that anyone can create an algorithm that has the ability to predict the data almost perfectly.
Bagging is another form of meta-algorithm that stands for bootstrap aggregation. Bagging refers to a particular technique in which multiple training sets are independently sampled, based upon the original data set. Bagging helps in reducing the effects of outliers in the algorithm, and thus the algorithm’s variance as well. This technique is mostly used for the decision tree model.
C4.5 is a decision tree algorithm as well as a well-known data mining algorithm. With every new branch, C4.5 uses the criterion of information gain versus default gain ratio per attribute and then selects the best feature to riven its branch on.
#11. Linear regression
Linear regression analysis refers to a statistical method, that estimates the relationship between a dependent variable and one or multiple independent variables. Regression analysis implements the least squares methods in order to estimate the best fitting curve on the data. This curve can be used to predict various outcomes.
#12. Data cleaning
Data cleaning is a well-versed topic within HR analytics. It is the process of going through the data, fixing inconsistencies and gathering missing data and preparing it for analysis. Since HR data is oftentimes regarded as ‘dirty’, this system of data cleaning is implemented in order to ensure that the data is perfectly alright.
#13. Business intelligence
Business intelligence refers to, making an effective use of data and information to well-informed and sound business decisions. It comprises various elements such as data analysis, data mining and reporting. Business intelligence in HR can help make the right decisions, and letting go decision that are backed by data.
#14. Workforce analytics
Workforce analytics is a great combination of software and methodology that makes use of statistical models to worker-related data, allowing enterprise leaders to optimize human resource management. Workforce analytics can help leaders to develop and improve recruiting methods; make general and specific hiring decisions and of course keep the best workers within the company.
Click here to read – Part I