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Implementing HR Analytics in the Organization

HR analytics is the analysis of employee-related data using tools and metrics. The objective of HR analytics to understand, quantify, manage, and innovate the role of human resources in the execution of strategy and value creation. It comprises of collecting data from internal and external sources, pre-processing, storing, and finally analyzing it to get deep insights about people on whose skills and competence an organization’s performance really depends on. HR analytics insights allow human resource professionals to make better-informed decisions related to employees, such as recruitment, training, performance evaluation, compensation, or education.

Implementing the system has several benefits. It facilitates smarter recruitment and people management. It also helps with forecasting employee turnover, by analyzing various stages and events in an employee lifecycle. Analytics with machine learning takes employee engagement to greater heights. HR professionals can use the system for workforce planning, thus augmenting identifying current and future skill gaps is another application for predictive analytics.

Key Steps to implementing HR analytics:

#1 Establish KPI’s -figure out the business questions that need answering

It’s important to choose and monitor HR metrics that are important to the goals of your business.  If you are new to HR analytics, make a start by tackling the more standard business questions, such as measuring absence and sickness leave, performance issues, training spend.  

#2 Identify the data needed and where to source that data

To ensure your analysis runs without a glitch, you will need to first identify which data is needed and then gather all of the data from the different sources so it can be merged in a single location.  That way you can extract the information quickly and accurately. 

#3 Decide on a custom or off-the-shelf tool
Based on your KPI’s and other deliverables, you can decide to create your customized tools. Several off-the-shelf solutions as available as well. There are many HR platforms available on the market, such as Gust, Zoho People Plus, or Namely. These products have multiple features for recruitment, onboarding, performance management, payroll and benefits management, employee engagement, etc. The market is brimming with AI-based solutions as well.

#4 Create a team

If you choose to create a customized tool for as to best fit your requirements, building a team of human resources and tech-specialists will become imperative. Positions that need to be filled will be like data engineers, HR Managers, data warehouse developers, data analysts, ETL developers, data scientists and so on.

#5 Data infrastructure set up - ETL and a data warehouse

This step is about getting the infrastructure that you put together in place to extract data from different systems. Data from all sources will then be transmitted to a single storage system for analysis and reporting. The infrastructure for this task includes a data integration tool and a data warehouse.

#6 Shape-up a predictive model

The main idea here is to develop a model that provides the most accurate predictions for a given query or concern. Data scientists check if the data warehouse has sufficient data required for solving a specific problem. If not, they initiate additional data collection. After having prepared data for machine learning, the specialists start model training. The models are tested and evaluated on their accuracy, and the best model is deployed into a software.

#7 Creating a front-end (UI)

With all the back-end information at hand now, a front-end user interface is required to present information in the most usable manner – by generating custom reports and manipulating data. A checklist of nice-to-have features might include charts, ad hoc reports, custom visuals, static or dynamic dashboards, etc.

#8 Train employees on how to use the system

Now that the system is ready, the final and most important step is to train the people who will use it. Without proper training, the whole exercise will become redundant because people may not be able to use the interface for correct data extraction, which makes sense.

  • Posted Date: 24-MAR-2020