HR Analytics Data: A Guide For HR Managers

Benjamin Sombi / Posted On: 16 August 2022 / Updated On: 23 September 2022 / Analytics / 154

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HR Analytics Data: A Guide For HR Managers



What Exactly Is Human Resource Analytics?

Human Resources Analytics is the study of a company's HR processes using hiring, recruitment, compensation, and other HR data. By collecting and analyzing essential HR data, businesses can develop actionable insights and enhance workforce, people, and talent management performance. The insights gained can then be utilized to impact process improvement decisions.

 

Why is HR analytics important?

HR professionals have long been data collectors, amassing and tracking personal information about employees, salary rates, and the annual number of retirements. However, to fully realize the potential of HR analytics, HR managers must become data interpreters.

 

HR analytics can help HR Managers answer questions such as: How can we make more informed decisions about where to direct our limited resources? How can we provide solutions that satisfy employees? How can we persuade the CEO to invest in people initiatives? How can we ensure that HR assists the organization in achieving its goals? How can we make use of available data to help the company grow?

 

In a year, HR collects a lot of data that can be useful if used correctly. However, interpretation is the key to analytics. You can have all the data in the world and not know what to do with it.

 

HR Analytics Data

The new oil is data. The HR department collects a lot of information. HR Managers must understand where to obtain data, manage it, and extract insights from it.

 

HR analytics data can help HR Managers gain numerous strategic insights. Employee data revealed the importance of power naps in promoting heightened cognitive functions and facilitating brainstorming at Google. That's why Google installed sleep pods in its office.


If Marriott Hotels' recruitment managers had relied on the number of CVs trickling in rather than the number of offers made to candidates, they would have missed out on an active talent sourcing medium - their Facebook page. They have the most "Careers" pages on Facebook and do an excellent job of reflecting Marriott's casual, friendly, yet luxurious vibe.

 

The bottom line is that Human Resource data is more than just hours worked or vacations taken. If managed properly, this data has the potential to tell very compelling stories about C-suite HR issues like productivity boosts, retention boosts, and leadership development.

 

Let's start with HR Data Sources.

 

HR Analytics Data Sources

The company's HR Information Systems (HRIS) contain data on the most common HR functions, such as recruitment, performance management, and talent management. Although HRIS modules vary by company, a common group of modules often contain data useful for people analytics.

 

Recruitment: The first common data source in the HRIS recruiting data gathered from the Applicant Tracking System (ATS). This system is the most frequently used input for recruiting metrics. This includes the number of applicants, their CVs, other characteristics, and information about the recruitment funnel, recruitment sources, selection, and so on.

 

Demographic information. HRIS employee records are another critical data source. Employee ID, name, gender, date of birth, residence, position, department, cost centre specifications, termination date, and so on are all included. These demographic data is frequently used as control variables in analyses. Furthermore, when data is manually combined, it is often the database that is enriched with data from other systems by matching the employee's ID as a unique identifier.

 

Performance Management. The performance management system (PMS) is a component of the HRIS that contains performance management information. Employee evaluations and performance ratings.

 

Learning Management. Another source of HR information is the learning management system (LMS). The LMS includes a course catalogue and tracks employees' progress through various programs. The LMS does not store all learning data. Finance keeps track of expenditure on external courses, whereas learning impact and effectiveness are frequently measured through surveys.

 

Benefits and compensation. Employees are compensated to keep them engaged. The HRIS also stores compensation and benefits information. These include remuneration information as well as secondary benefits.

 

Succession Management. The HRIS also includes succession planning schemes. The volume of data collected is determined by the maturity of the organization's succession planning practices. Data examples include leadership development data, managerial bench strength, and information on who is next in line for positions.

 

Talent advancement. Talent development data is an odd one out. Talent programs frequently include courses and workshops integrated into the learning management system. However, the organization's overall approach to talent development is another critical piece of information that can be retrieved from the HRIS.

 

Exit Interview. This section contains information on the reasons why employees left the organization. This information can be used in analysis aimed at lowering employee turnover.

 

Other HR analytics data

Other HR analytics data sources, in our classification, are HR data that are not commonly stored in the HRIS. This is frequently due to the difficulty of gathering data using traditional methods.

 

Learning. Our first example is learning data. Data on learning effectiveness and program evaluation is frequently stored separately from the LMS and managed by the learning department. This information is commonly saved in excel spreadsheets and survey collection tools. Integrating this data into a larger HR reporting and insights database is a top priority for organizations that are just getting started with learning analytics or trying to advance their reporting.

 

Travel. Travel data is another important source of information. The frequency with which an employee travels internationally may predict employee turnover. This type of data, however, is not stored in a traditional HRIS.

 

Mentoring. Mentoring is essential for high potentials and frequently included in talent and leadership development programs. Mentorship can help mentees be more effective, stay longer, and advance to a more senior position.

 

Employee survey results. This is more of a subcategory. Surveys are used to collect a large portion of HR data. This can range from a poll on the food quality in the cafeteria to a CEO popularity survey and the traditional engagement survey. Most businesses distribute surveys in a decentralized manner, resulting in data dispersion throughout the organization and survey fatigue. All this information in one place allows for a more in-depth analysis of employee survey data.

 

Employee Engagement Survey. The engagement survey is sometimes included in the previously mentioned employee survey data bank. However, to ensure anonymity, engagement surveys are frequently collected by a third party.

 

Absence Data. Another important HR data category is recorded absence data. Managers typically track and record sick days in a system. Some organizations also keep track of the reasons for absences. Similarly, data on holidays, maternity leave, and tardiness are collected.

 

Well-being and health data. This is an additional data source that the HRIS does not capture. Depending on the organization, records regarding (participation in) employee wellness programs may be available.

 

Data from social networks. Organizational social network data, also known as organizational network analysis (ONA), can be a valuable source of information. This could be data from network surveys, email accounts, phone records, or any other system that reports network data.

 

Business Data

The scope of business data is nearly limitless. People analytics can make use of a wide range of business data sources. Listed below are some of the important ones.

 

CRM Data. The customer data in the company's Customer Relationship Management system is massive. Customer contact moments, NPS scores for those touchpoints, lead scoring, and so on are all included. This information can be used to assess the impact of people policies on customer-facing employees.

 

Financial Data. Another vital source of business data is financial data. This can be for simple analyses of L&D spending and more complex analyses of personnel costs, ROI calculations for various interventions, and other financial analyses.

 

Production management data. Production management data can also be a source of information. Production management systems plan, track and manage data such as scheduling, number of service calls, delivery rate, and delivery speed, among other things. These data can also be used to assess the impact of people policies on employees involved in the manufacturing or service delivery processes.

 

Data on sales. Sales data, like the previous one, is another outcome measurement. Sales per store, for example, can be used as outcome data to measure the effectiveness of various HR policies, such as learning program effectiveness.

 

As you can see, numerous data sources can be used for people analytics. Remember that each organization's HR and business data is structured differently.

 

HR Analytics Data Management

As an HR Manager, you know the massive amount of data you collect from employees. The amount of data you collect can sometimes overwhelm all the abovementioned sources.

 

A game plan is required to avoid drowning in this sea of documents. That is what employee data management is all about: having a plan for how you collect data from employees, organize that data, and retain that data to ensure compliance with legal regulations.

 

Physical and electronic file management are typically combined in data management. While physical document storage can be more convenient in some ways, the trend is toward digital storage, which has many advantages. Digital data management is almost always more secure, efficient, sustainable, and accessible. However, physical files remain popular, and the transition to digital can be time-consuming.

 

 

How to start Employee Data Management

The best way to begin with employee data management is to perform a data audit. Consider the first time you collected employee data and walk through each time data is collected. You'll want to make your process as simple as possible for you and your subordinates.

 

Step 1: Document Your Current Procedure

Start with the first time you collect employee data (recruiting) and work your way through the employee lifecycle. The first step is to document how you currently collect and store data. Note whether the data is physically or digitally stored and the type of data stored.

 

Step 2: Determine Which Data Should Be Stored Where

After you've noted which data you have, determine whether any changes are required. Do you have the same type of data spread across multiple systems? Do you store sensitive data alongside non-sensitive data? Do you have any information that is difficult to obtain?

 

Step 3: Improve Your Data Collection Methodology

There are two takeaways here. Regarding data management, the rule is to fix any problems at the source. So, if you find any incorrect or missing data, change how you collect the data to avoid repeating the errors. Second, for administrative tasks, automation is always preferable. Use all the technological tools to make data collection and storage as simple as possible for you and the employee.

 

Step 4: Perform Regular Audits

You should audit your employee data at least once a year, especially for government documents. Internal (you do it yourself) and external (you pay someone to do it) audits are rotated every year. The longer you wait to audit your data, the more work you will have to do for your next audit.

 

Good, clean, secure data won't make a difference if the people making Human Resource decisions aren't enlightened by the resulting insights.

 

First, visualize the data.Secondly, create custom reports.

 

Finally, databases must interact with powerful predictive analytics machines to supplement the human perspective and eliminate bias from HR operations.This is the future of human resources and the big change we are all demanding.

 

Benjamin Sombi is a Data Scientist, Entrepreneur, & Business Analytics Manager at Industrial Psychology Consultants (Pvt) Ltd a management and human resources consulting firm.


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