What is HR analytics?
HR analytics is the application of statistics, modelling, and analysis of employee-related data to help improve and achieve business goals and outcomes. HR analytics enables HR professionals to make data-driven decisions to attract, manage, and retain employees, resulting in an improved bottom line and a greater return on investment.
Why should I use HR Analytics?
A core responsibility of HR leaders is aligning HR initiatives to an organisation’s strategic goals. But how do HR leaders know they are implementing the right initiatives?
Before the data revolution, traditional HR leaders would rely on years of experience and gut instinct to drive their decisions. However, it has been well documented through research, that companies who use data to drive their decisions are achieving a distinct competitive advantage. Therefore, prudent HR Leaders are combining their years of experience with facts which are revealed through data contained within their organisation to inform their strategy. They are mining data from available sources across their organisation and applying rigorous analytical methods to this data to gain valuable insights which guide them on the correct initiatives to implement.
Data sources that are proving to be hugely beneficial in revealing profitable insights include;
- Employee databases
- Employee surveys
- Attendance records
- Salary and promotion history
- Employee work history data
- Demographic data
- Recruitment process data
- Exit Interview data
- Focus groups data
What does the HR data analytics process look like?
HR Analytics comes under the umbrella of data science which covers a lot of disciplines and has many applications in Business, Science and the world in general. The data science process follows a definite systematic process which is outlined below.
Frame the problem
The first step of the data analytics process is to define or frame the problem that needs to be addressed. Generally, when we look at a business problem, we must translate it to a data question (or a number of questions around a central theme) that needs to be answered.
As Retention Bridge Consulting are experts in Employee Retention, we will use this area as an example.
For a company who feels they may have a problem surrounding employee retention, below is a series of questions that they may want answered;
- What is our current turnover rate?
- Is our turnover rate higher than the industry average?
- Who is leaving our company?
- Why are they leaving our company?
- How is our retention problem impacting our business strategy and goals?
- How much are our retention problems costing the organisation?
- What areas need the most attention?
- What initiatives can we implement to improve the situation?
The next step is deciding on what data is required to answer the question/questions. Then they need to identify whether or not the data is already available, and if so, what parts of the data are useful. If not all the data required is available, they need to identify what additional data they require and where or how to get it.
The data preparation part of the process is where most time on a project is spent. It is generally estimated that 80% of a data science project is spent at this stage. Data preparation involves data wrangling (also referred to as data mining) and it is the process of cleaning and unifying messy and complex data sets for easy access and analysis. This process typically includes manually converting/mapping data from one raw form into another format to allow for more convenient consumption and organisation of the data.
Real, raw data is rarely usable out of the box. There are errors in data collection, corrupt records, missing values and many other challenges that have to be managed. Data first needs to be cleaned and converted into a form that can be useful and further analysed.
This part of the process is extremely important in terms of the quality of the data. The data used in the project must be statistically significant and be prepared and cleaned to a very high standard. There’s a commonly used saying in data science – “garbage in, garbage out”. If your data is not of very high quality you may as well not undertake the project as it would be a waste of time, money, resources and will produce completely inaccurate insights that are worthless.
Exploratory data analysis
Once the data has been cleaned and prepared into a useable format, the next step is to understand at a high level, the information contained within the data sets. It is the process of performing initial investigations on data to discover patterns, to spot anomalies, to test hypothesis and to check assumptions with the help of summary statistics and graphical representations. For example, it may highlight obvious trends or correlations in the data. It may also reveal high-level characteristics and may reveal which are more significant than others.
Perform in-depth analysis
This step is usually the meat of your project, where you apply all the cutting-edge tools and techniques of data analysis to unearth high-value insights and predictions. This can include machine learning, statistical models or algorithms amongst others.
Communicate results of the analysis
The final part of the data analytics process is the communication of the results. All the analysis and technical results are of little value unless they can be converted into valuable insights and communicated in a way that’s comprehensible and compelling. Data storytelling is a critical skill in the data analytics process. Effective communication of the data analytics results informs what should be done next to solve the initial problem.
What type of analytics can be applied to HR data and what can they tell you?
There are four major types of analytics that can be applied to HR data.
Descriptive Analytics – What happened in the past?
The first step in solving most problems is figuring out what’s going on. Descriptive analytics is the first step and is the basic type of analytics you’re most likely use to in figuring things out. It describes what already happened, or what’s currently happening in a company at a basic level. It’s taking historical data and transforming and summarising it into something that is useful and understandable by managers, and other stakeholders.
It is very important to note that descriptive analytics tells you “WHAT” has happened, it does not tell you “WHY” something has happened?
Diagnostic Analytics – Why it happened?
Diagnostics analytics reveals the underlying cause of the events presented by descriptive data. If you know the cause, you know where to focus your efforts to mitigate the problem. The information can be used to create a plan for improvement based and also bring to light any challenges standing in the way of reaching desired milestones.
Predictive Analytics – What will happen in the future?
Where descriptive analytics look backward, predictive analytics work to look ahead. It focuses on what might happen in the future, based on the details of past events. This may be a forecast of which employees are likely to resign within the next 90 days. Predictive data is gained through data modelling, machine learning and artificial intelligence using historical data. Models are built on patterns that were found within the descriptive analytics.
If you know what’s is likely to happen, you can prepare for it in advance. Much like if you know that it’s going to rain tomorrow, you can pack an umbrella in your brief case tonight. Similarly, if you know which employees are flight risks in the next 90 days, you can mitigate the problem before it’s too late.
Prescriptive Analytics – What should we do about it?
Here’s where things can get really powerful. Prescriptive analytics suggest decision options and actions you can (should) take, based on the predictions. Organisations can use the principles of analytics to help them identify problems and brainstorm solutions using data as the driving factor. Unlike purely human decisions that are often subject to illogical biases, the decisions recommended by prescriptive analytics are based on data, and therefore, more reliable. In the predictive analytics stage, the organisation considers all of the options and decisions are made.
How can I use HR Analytics to help achieve organisational business goals?
First you need to develop your HR Analytics Strategy by deciding which business problems you would like to solve. HR Analytics can be applied across many areas including but not limited to Employee Retention, Employee Performance, Recruiting, Employee Development, Workforce Planning, Employee Engagement and Compensation and Benefits. One of the most important steps to take when applying analytics in HR is that you only research data that is relevant to the goals of the business. Whatever data you are going to analyse needs to be of strategic value to your organisation or it will be a waste of time, money and resources.
You also need to determine your existing analytics capabilities in terms of skills, tools and data sources. Questions that need to be asked include “Do we have the skills required skills inhouse?” and “Do we have all the tools required to successfully execute a HR Analytics project? Your goals determine which data sources from different departments you’ll need to help your decision-making. Then you need to identify ways to expand and leverage data sources so you can reach the goals. Once you have identified your strategic goal and have in place the skills, tools and relevant data sources it is simply a case of following the data analytics process making sure to incorporate the 4 types of analytics into your projects.