This article looks at the five most used HR analytics tools. Adopting HR analytics is a big step for many people and organizations. Indeed, I often get asked: “What are the best HR analytics tools to use?”
This blog will reveal the answer. Here’s a list of the 5 best HR analytics tools to get started with analytics.
HR Analytics Tool #1 RStudio
R is the most used HR analytics tool. R is great for statistical analysis and visualization which is very suited to explore huge data sets. It enables you to analyze and clean data sets with millions of rows of data. In addition, it lets you to visualize your data and analysis, like what you see below.
You can download R here.
However, we chose RStudio as our top pick for HR analytics tools.
RStudio is an open source and enterprise-ready professional software package for R. It basically does everything that R does, but has a friendlier user interface. The interface contains a code editor, the R console, an easily accessible workspace, and history and room for plots and files. You can take a look at an example of this below.
As previously stated, R is very useful because it enables you to work with much larger datasets compared to for example Excel. Furthermore, R has a very extensive library with R packages.
These packages are easy to install and allows you to do very specific statistical analyses and create beautiful visualizations. Take for example the caret package. This package enables you to split data into training and testing sets to train algorithms using cross-validation.
Another example of an R package is ggplot, which helps you to visualize graphs. In a previous article on R Churn analytics, Lyndon showed the distribution of employee turnover for a large Canadian company as seen in the following chart.
All in all, RStudio is a more than satisfactory tool for analyzing and visualizing very large amounts of data. You can download RStudio here.
Python is another programming language, and can be used interchangeably for R. In the data science community, there’s quite a bit of buzz about which will become the data scientist’s tool of choice.
While R is better at doing statistical analyses, has a more active community in regard to the field of statistics and is better suited for visualizations, Python has an faster learning curve.
In short: if you already have experience in Python, or want to get started quickly, use Python. If doing statistical analyses will be your job for the next 5 years, use R. For more information on the difference between Python and R, check this article.
You can download Python here.
When we talk about HR analytics tools, we shouldn’t forget the basics.
Excel is where most of us started. It’s no surprise that when you manually extract data form any of your HR systems, it most likely comes in the form of a comma separated value (CSV) file. These files can easily be opened and edited using Excel.
The good thing about Excel is that it’s very intuitive to most of us HR data geeks and therefore easy to use.
For example, if you wanted to check the cleanliness of your data, you can easily transform a dataset into a table and check each column’s data range for outliers.
Related: read more about HR data cleaning
This way, if you select the age column you can easily check minimum and maximum ages. You wouldn’t expect anyone below 16 to work at your company, nor would you expect anyone over the age of 80 to work for you. These outliers can be found in one single click.
Some quick tips on how to use Excel for HR analytics purposes:
- When you work with large files, transform them into Tables. Excel is able to work much more efficiently with tables.
- Don’t use Excel formulas in large data sets. When you calculate a column using an Excel formula, transform the outcome to a numeric value. Formulas are recalculated every time you make a change in the data set. This places a significant and unnecessary burden on your computer’s memory and processing speed – and bogs down Excel.
- Categorical variables (Gender: Male, Female) are easy to check in a table. Select the table column and check for errors or inconsistencies. Can you spot the two inconsistencies in the picture?
- If you want to merge to data sets, the ‘VLOOKUP’ function is your best friend. It makes connecting two separate data sets very easy.
- Pivot tables do a great job in summarizing large quantities of data. Pivot tables and the VLOOKUP function practically enable you to do HR analytics in Excel.
#4. Power BI
Gartner’s Magic Quadrant for Business Intelligence shows Microsoft as the absolute leader. That’s why we included Microsoft’s PowerBI. It makes the aggregation, analysis and visualization of data very simple.
- With Power BI, it’s a cinch to connect to multiple source systems, like SQL data bases with people data, a live twitter feed and/or machine learning APIs. All these different data sources are then combined in Power BI. This simple aggregation process enables you to combine multiple data sources in one large database.
- The consolidated data can then be used to create a pivot table (using Power Pivot). This lets you to get quick insight in key areas of your workforce.
- The same data can then be transformed into a dashboard, using Power BI’s dashboarding capacity. An example of this dashboard can be found below.
SPSS is one of the most commonly used HR analytics tools in social sciences. Thanks to its user-friendly interface you’re able to analyze data without having extensive statistical knowledge. In addition, SPSS is often used within the field of social science. This means that a lot of HR professionals know how to use it, especially the ones with an interest in data analysis.
This is also the reason why we put SPSS on the list and not its biggest competitor, SAS. SAS is more widely used outside of the social science field. However, SAS has a steeper learning curve. In addition, SPSS shares many similarities with Excel which makes it easier to work with.
Consider SPSS an easy stepping stone for companies with less mature analytical capabilities. SPSS makes it easy to do an exploratory correlation analysis, or a quick regression analysis. For more complicated (machine learning) algorithms, R is the better candidate.
How to choose the right HR analytics tool
In order to select the most appropriate HR analytics tool, it’s crucial to know what you want to achieve. Do you want to…
- …get a grip on your data and create (HR) dashboards? Go for a tool like Power BI or Tableau (Tableau excels in data visualization, but is much more expensive to work with, money-wise). Such tools make data aggregation and data visualization quite simple.
- …get some basic insights in your company and employee data, for example by checking if departments differ significantly in terms of employee performance or engagement? Go for the simpler tool like Excel and SPSS. They require a low level of analytics skills and can already give you some vital insights in your data.
- …thoroughly analyze HR data and make predictions about the future? Go for data analysis tools like Phyton or RStudio. They provide you with the capability to do the most advanced analyses out there – all while handling huge quantities of data. Some examples are predicting employee turnover and job classification analysis.
Good luck in your search for the HR analytics tools that work best for you. If you feel like we’ve missed a great tool, let me know at Erik@AnalyticsinHR.com.