This article lists the nine 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 article will give you the answer to this question. Here’s a list of the nine best HR analytics tools to use.
HR Analytics Tools
R is the most used HR analytics tool. R is great for statistical analysis and visualization and is well-suited to explore massive data sets. It enables you to analyze and clean data sets with millions of rows of data. It also lets you visualize your data and analysis, like what you see below.
An example of descriptive statistics output generated by R
You can download R here.
The most often used Integrated Development Environment, or IDE, for R is RStudio. An IDE is software that provides additional facilities for software development and data analysis. This makes the software more userfriendly.
Simply put, RStudio does everything that R does but more and better. The RStudio interface contains a code editor, the R console, an easily accessible workspace, a history log, and room for plots and files. The picture below shows all these elements.
As previously stated, R is 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 allow you to run virtually all 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 using ggplot.
All in all, R is an excellent tool for analyzing and visualizing vast amounts of data. You can download RStudio here.
Python is another programming language and can be used interchangeably with R. In the data science community, there’s quite some buzz about which of the two will become the data scientist’s tool of choice.
R is better at doing statistical analyses, has a more active community when it comes to statistics, and is better suited for visualizations. Python, however, offers only slightly less functionalities but is easier to learn.
Often used IDEs are PyCharm and Spyder. These tools are to Python what RStudio is to R. Both are open-source IDEs that provide data scientists all the tools they need to use Python. Spyder, short for Scientific Python Development Editor, is specifically made for data science. It includes an advanced editor, an interactive console, documentation viewer, and a whole suite of development tools that also include visualization options.
Spyder, a data science IDE for Python
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 five 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. Whenever you manually extract data from any of your HR systems, it most likely comes out 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 quickly 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 quickly check the 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. You can find these outliers in a single click.
Some quick tips on how to use Excel for HR analytics purposes:
- If you want to run advanced analyses, load the Analysis ToolPak in Excel. This package enables you to do advanced analytics, including correlation and linear regression.
- When you work with large files, transform them into Tables. Excel can work much more efficiently when data is structured in a table.
- 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 recalculate 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 discrepancies in columns in this 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.
If you want to learn more about how to use Excel to analyze HR data, check the HR analyst course from the AIHR Academy. That course will teach you the basics of HR data 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 Power BI. It makes the aggregation, analysis, and visualization of data very simple.
- Using Power BI, it’s straightforward to connect to multiple source systems, like SQL databases with people data, a live twitter feed, or machine learning APIs. All these different data sources then integrate in Power BI. This simple aggregation process enables you to combine multiple data sources in one large database suited for reporting or analysis.
- The consolidated data can then be used to create a pivot table (using Power Pivot). This lets you get a quick insight into 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 is added below.
Tableau is very similar to Power BI in that it enables the aggregation and visualization of various data sources. Founded in 2003 as a commercial outlet for research produced at Stanford University, the software has taken the visualization world by storm.
Tableau is arguably the best business intelligence tool (BI tool) out there when it comes to visualization. It has been recognized in the Gartner Magic Quadrant for seven consecutive years between 2012 and 2019.
The image below shows an employee referral dashboard. The dashboard shows how long different recruiters take to resolve referrals. By sharing this visualization with recruiters, average response time to these referrals dropped significantly (see below, Q1, Q2, and Q3).
The disadvantage of Tableau is that it is more expensive compared to competitor Power BI.
Visier is a data aggregation service build to answer questions about the workforce. It connects to different HR systems and connects them into one HR BI tool.
Compared to Tableau, Visier brands itself more as an actionable people analytics insights platform that shows trends in workforce data. It also enables you to answer questions about what drives performance and productivity and different other HR outcomes.
Visier offers a solution that comes close to out-of-the-box people analytics. It has functionalities that use algorithms to predict exits, promotions, internal movement, and so on.
Similar to Visier, Qlik aggregates data. The main difference is that Visier focuses on actionable insights on workforce data, whereas Qlik is a more general data aggregation tool with extensive dashboarding capacity. This means that Qlik is very suitable for more general data aggregation, warehousing, and dashboarding.
Just like Power BI and Tableau, Qlik has been a leader in the Gartner Magic Quadrant for several years.
Whereas Power BI, Tableau, and Qlik are mostly used to aggregate data, SPSS is used to actually analyze data.
SPSS is one of the most commonly used HR analytics tools in the social sciences. Thanks to its user-friendly interface, you’re able to analyze data without having extensive statistical knowledge. Because SPSS is often used in the social sciences, a lot of HR professionals know how to use it – especially the ones interested in data analysis.
This is also the reason why we put SPSS on the list and not its biggest competitor, SAS. SAS has more users outside of the social science field. However, SAS has a steeper learning curve. SPSS also shares many similarities with Excel, which makes it easier to work with.
9. CPLEX Optimizer
A whole different set of analytics tools are made for optimization. These are often used to do prescriptive analytics. Prescriptive analyses are dedicated to finding the best course of action in a given situation. Because prescriptive analytics are quite advanced, they are still scarcely used. However, these prescriptive tools can potentially provide great value to the business.
An example of such a tool is CPLEX Optimizer. CPLEX Optimizer enables you to find the best solutions from among billions of alternative business decisions. It does this by providing flexible and high-performance data modeling.
This may sound a bit abstract. Say you have a number of stores and warehouses. What is the best way to supply these stores through your current warehouses? And would it be a good choice to open another warehouse at a designated location? Using CPLEX you can model these challenges, get input on whether or not you should open an additional warehouse – and what the optimal distance would be for this location compared to your existing shops. Other applications include scheduling and workforce planning.
The video below makes it very concrete and show you how this happens.
A decision tutorial in CPLEX Optimizer
How to choose the right HR analytics tool
Now you know the different tools, you still need to choose the right one for your analytics project. When doing analytics, you may want to use multiple tools. For example, when you combine and analyze vast amounts of data you will use different tools then when you want your output to be displayed on a dashboard.
To select the most appropriate HR analytics software 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, Tableau, or Qlik. These 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 a simpler tool like Excel or SPSS. They require a low level of analytics skills and can already give you some vital insights into your data. Visier offers more comprehensive analytical capabilities but is less suitable for a quick, ad-hoc analysis as it requires more effort to get up and running.
- …thoroughly analyze HR data and make predictions? Go for data analysis tools like Phyton or R. They provide you with the capability to do the most advanced analyses out there – all while handling vast quantities of data. Some examples are predicting employee turnover and job classification analysis. CPLEX Optimizer would be another possibility. This tool is more prescriptive as it helps you to make the best decisions using existing data and your decision parameters as input.
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 Power BI to analyze data and create HR dashboards, check out our HR analyst course.
If you want to learn more about HR analytics in general, check out the other courses in the AIHR Academy.