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This article lists 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 give you the answer to this question. Here’s a list of the 5 best HR analytics tools to use.
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 a 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 a 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 how clean your data is, you can easily transform a dataset into a table and check each column’s data range for outliers.
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:
If you want to learn more about how to use Excel to analyze HR data, check the HR analyst course from the HR Analytics Academy. That course will teach you the basics of HR data analytics in Excel.
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 databases 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 HR analyst course from the HR Analytics Academy dives into how to aggregate data from multiple Excel sheets, visualize this data and create HR dashboards and reports using PowerBI
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.
In order to select the most appropriate HR analytics software tool, it’s crucial to know what you want to achieve. Do you want to…
This article provides an overview of the most commonly used HR analytics software. Good luck in your search for the HR analytics tools that work best for you. If you want to learn more about how to work with Excel and PowerBI to analyze data and create HR dashboards, check out this HR analyst course.
If you want to learn more about HR analytics in general, check out the other courses in here.
This article was originally posted at https://www.analyticsinhr.com/blog/hr-analytics-tools/
Erik van Vulpen is an expert in connecting HR processes to business results through qualitative and quantitative methods. He is globally recognized as a thought leader in the People Analytics and Digital HR space. Erik is a regular speaker at conferences and trained dozens of HR leadership teams to embed innovative and data-driven HR practices in their organization. He is also an instructor for the AIHR Academy, as well as one of its founders. Contact Erik at firstname.lastname@example.org or connect with him on LinkedIn.
This article was written by one of the consultants at IPC
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