10 HR analytics tools that can optimize your workforce – TechTarget

Enterprises have long used analytics for tracking revenue, spotting growth opportunities and improving user engagement. But it is equally important to understand how to improve the satisfaction, performance, development and engagement of the people who keep the business running. HR analytics tools can provide the necessary insight.

Betsy Summers, a principal analyst at Forrester who covers human capital management, said, “Frankly, all companies should invest in understanding their people data, because what’s happening with people — their engagement, their attrition, their skills, their wellness, their productivity — are business issues that are critical for growth.”

This has become even more true in the wake of the COVID-19 pandemic, as many organizations have become aware of the poor state of their people data. HR tools may have some analytics capabilities, but they often scratch the surface of what is possible with more capable analytics tools. And many HR professionals are not confident using the analytics tools they have.

Summers said that according to a recent survey of 719 HR leaders that Forrester is still analyzing, only 37% reported being confident in their people analytics capabilities. In contrast, 81% reported confidence in their compliance practices.

Why businesses should adopt HR analytics tools

Helen Poitevin, vice president analyst at Gartner’s HR practice, named two key reasons why enterprises should adopt HR analytics tools. First, they make analytics accessible to more stakeholders in HR and the business, which helps to improve decision-making about talent. Second, they automate commonly requested analyses and reports, thereby freeing up time for HR analytics specialists to take on more complex projects to answer critical and strategic talent questions in a timely way.

Most businesses are heavily dependent on their people to create value for customers. If the right people are in the right roles, the business will create more value for customers, increase profits and be successful. That’s why talent management can provide such a competitive advantage.

But talent management is especially challenging without the ability to surface and take advantage of insights buried in people data. “Understanding how many people a business will need in the next three years, how to find those people, and how to motivate, develop and retain your workforce is a multidimensional and complex problem,” said Aaron Sorensen, partner and head of business transformation and behavioral science at Axiom Consulting Partners.

HR analytics tools help translate people data, such as demographics, skills, pay and performance into actionable insights that support business decisions about the talent needed to build a competitive advantage. And if the analytics tools are HR specific, they can capture insights from unstructured data, such as employee aspirations and experiences, and provide predictive analytics to guide talent decisions.

Sorensen said it is helpful to break down HR analytics tools into the following four broad categories of specific purposes:

  • talent acquisition, including sourcing, selection and onboarding;
  • reskilling and mobilization of talent through talent marketplaces or exchanges such as Gloat;
  • employee development through platforms such as BetterUp and LinkedIn Learning that are built on recommendation engines that personalize development;
  • engagement and retention by continuously monitoring attitudes and perceptions.

Read on for explanations of 10 prominent analytics tools — listed alphabetically by product name — most of which are general purpose, some that specialize in HR, and that singly or in some combination could provide just the capabilities you need.

HR analytics has important uses at all stages of the employee lifecycle.

1. ChartHop

ChartHop is dedicated people analytics software intended for companywide use, to facilitate broader planning and allow feedback across teams. It was designed from the ground up to make it easy to surface various kinds of analytics for common HR problems and questions. It includes modules for headcount planning, performance management, compensation and employee experience.

Key features: Ease of integration and a rich library of analytics templates to get started.

Why you should consider ChartHop: This is a good option for companies looking to make people analytics available to more employees and for exploring opportunities in improving HR analytics workflows.

2. IBM ILOG CPLEX Optimizer

CPLEX Optimizer is a more technical tool for solving complex optimization problems. ILOG started in 1987 with analytics visualization tools and, after acquiring CPLEX Optimizer in 1993, combined the platforms into a comprehensive suite for optimizing business processes. IBM acquired ILOG in 2009 and upgraded the core tools over the years with a better UI, improved data integration and support for newer cloud architectures. It is a highly technical offering, but a good one for HR departments involved in complex scheduling decisions and optimizing workforces for new markets and corporate expansions.

Key features: A strong set of tools for expressing complex optimization problems.

Why you should consider ILOG CPLEX Optimizer: This is a good choice when the HR team is tasked with a major shift in headcount such as opening a new facility, growing into a new market or launching a major product.

3. Microsoft Excel

Excel is the industry leader when it comes to surfacing analytics insights and communicating them across the company. One big advantage: Business users are already intimately familiar with the Excel user experience. The spreadsheet standard is one of the best options for translating complex data into a form that is immediately understandable to finance and planning teams. It also presents data in a form that allows experts across the company to slice and dice data in different ways. Microsoft continues to update Excel with programming tools for capturing information from various systems, generating reports automatically and sharing them across teams. There are also hundreds of third-party HR analytics templates for Excel.

Key features: Familiar and popular across business and finance teams.

Why you should consider Excel: Most companies are already using it. Also, integrating existing HR analytics workflows into Excel can simplify communication across HR, business, finance and marketing teams.

4. Microsoft Power BI

Microsoft Power BI is the clear market leader in business intelligence tools, with extensive and well-documented integrations into every major HR application and platform. It also provides numerous ways of analyzing, presenting and visualizing data. It works quite seamlessly with Excel, which, as noted, can improve communication across the business. Power BI is also supported by various third-party HR dashboards, analytics tools and consultancies.

Key features: The most popular platform for creating analytics dashboards and metrics.

Why you should consider Power BI: It is a good choice for companies that want to customize their own HR analytics or work with a consultancy that uses the platform.

5. Python

Python has become the most popular programming language for artificial intelligence and machine learning applications. As a result, a wide variety of AI development and analytics tools have grown up to support the platform. Although it may be accessible to a few geeks in the HR department, building applications in Python generally requires collaboration between the HR and development teams. It is also one of the most flexible tools for automating the complex data engineering workflows that are essential in deploying HR analytics applications at scale.

Key features: A comprehensive programming language popular with data scientists.

Why you should consider Python: It is best suited for highly customized HR analytics applications. It can also help with data engineering and data preparation tasks at various stages of the analytics workflow.

6. Qlik

Qlik is another popular BI development tool. It is easy to learn compared to more technical statistical tools but relatively powerful. The vendor has focused on improving the user experience for exploratory data analysis, which makes it easier for users of all skill levels to discover new insights and share them. Qlik also provides various tools for improving recruitment efficiency, optimizing candidate sourcing channels, improving hire quality and advancing diversity and inclusion goals.

Key features: Simplifies exploratory data analysis.

Why you should consider Qlik: It is a good option for HR teams that want to facilitate free-flowing analysis of people data for less technical staff.

7. R

R is one of the most popular programming languages and environments for statistical analysis queries. It excels at running advanced analytics on extremely large data sets. Consequently, it supports a wide variety of third-party tools and recipes for HR analytics use cases. The basic tools are available for free as open source software. Also, RStudio is a popular open source, GUI-based R development environment that can simplify programming of complex applications.

Key features: Powerful statistical and analytics programming language with popular open source tools.
Why you should consider R: This is a good choice for analytics geeks who want to make sense of large data sets.

8. IBM SPSS

SPSS is one of the oldest statistical packages on the market. The vendor, also called SPSS, was founded in 1975 to improve analytics for marketing, academic and health research. IBM acquired the company in 2009 and renamed the platform SPSS Statistics. IBM has since modernized it to support more modern data workflows. It includes extensions for coding in Python.

Key features: Rich set of analytics functions with a simple user experience for complex HR queries.

Why you should use SPSS: It is a good choice for HR teams that need help answering complex questions involving large data sets.

9. Tableau

Tableau was an early innovator in visual analytics tools that made it easier to slice and dice data from different perspectives. It has built on this lead with a rich set of capabilities for users with any level of technical expertise to understand and explore data. Tableau is a leader when it comes to visualizing complex data.  Salesforce bought the vendor in 2019, which makes Tableau a good choice for companies that have standardized on Salesforce for other aspects of the business.

Key features: Ease of integration and a rich library of analytics templates to get started.

Why you should consider Tableau: It is a good choice for teams looking for powerful visualization features and who want to explore HR data in various ways.

10. Visier

Visier is focused front and center on HR analytics. The platform is designed to make it easy to incorporate HR insights directly into existing HR workflows rather than just providing statistics. For example, it includes modules that help guide HR teams in solving problems relating to workforce planning, attrition and diversity and inclusion. It also has tools to help manage the recruiting pipeline, retain high-performing employees and measure the impact of training programs.

Key features: Provides easy integration into popular HR tools and platforms and a rich set of commonly used HR analytics templates.

Why you should consider Visier: It is a good choice for teams looking to get HR analytics into production quickly with minimal technical expertise.

Features to look for when considering HR analytics tools

All HR analytics tools come with various bells and whistles that improve analytics processes. But an HR manager’s first consideration should be how well the software integrates with their HR applications.

To choose products, the buying team may want to create a list of top HR challenges and desired business outcomes. Then analyze how various tools might help address those needs. Axiom’s Sorensen finds that most companies are going with a “best in breed” approach, using specialized tools to target specific pain points and goals. This is getting easier as the APIs for exchanging data between apps get easier to manage.

He advised caution about AI features, which are sometimes more hype than reality. “Lots of HR analytics vendors are also bad actors when it comes to AI claims,” he said. Sorensen recommended getting vendors to explain how their AI works in practice and investigate how the tools mitigate bias and explain results.

It is also helpful to think through how a tool pulls in different types of data and surfaces insight for specific analytics workflows. Summers, of Forrester, observed that because people are complex, the analytics can require combining data from a variety of sources. For example, to measure the impact of your learning investments, you will want to see how employees’ participation in certain courses intersects with performance reviews, outcomes and engagement scores.

Summers also recommended exploring how well a given tool might fit into your existing HR workflows. Low-code tools tend to be the most intuitive to use, which can improve adoption across the whole HR team. Also, some tools can notify or alert users to a change or crisis, or nudge them about events rather than just providing reports.

It is also helpful to investigate the ecosystem of tools, such as the services and other capabilities around them. Gartner’s Poitevin advised looking for prepackaged insights and dashboards with topic-relevant data storytelling. This includes metrics and trend analysis across all HR domains as well as key predictive indicators, such as attrition risk.

How much do HR analytics tools tend to cost?

Pricing of HR analytics tools can vary significantly. At one end of the spectrum, open source tools based on R and Python are free. At the other end are commercial tools that can cost hundreds of thousands of dollars a year. Many vendors price their products based on the number of employees in the organization while others use the number of HR users.

Sorensen observed that a SaaS tool with custom AI for predicting turnover could run well over $300,000 per year for a large enterprise. A highly skilled data scientist could develop a similar algorithm in Python and visualize the patterns in Power BI for little direct cost. However, such savings need to be balanced against the average cost of employing a data scientist.