How to effectively leverage data-driven HR decisions with HR analytics

The years 2020 and 2021 have caused organizations worldwide to rethink their HR strategies. As HR professionals grappled with a COVID-induced overhaul of remote work policies and management in 2020, 2021 saw approximately 47 million people quit their jobs, testing HR teams’ capabilities to leverage existing resources and seek new ones. during the Great Resignation.

During this period of extreme transition, the HR function has evolved to rely on data and analytics – ranging from information about employees and organizations to data on how HR dilemmas have been addressed in the past. There is also a greater reliance on technology and AI-driven automation to turn data across the HR process into valuable insights.

According to Fortune Business Insights, the global human resource technology market is expected to grow from $24 billion in 2021 to $36 billion in 2028, and companies are likely to prioritize investments in artificial intelligence (AI) to optimize business processes and reduce costs. to lower. In addition, a Mercer report found that 88% of companies worldwide are using some form of AI in the form of intelligent chatbots, candidate engagement systems, recommendation engines and more.

The growing reliance on data-driven insights can be attributed to the need to efficiently make HR decisions that consider both employee happiness and business growth. However, to successfully make data-driven HR decisions, companies need to understand the critical steps to turn data and analytics into valuable insights. Below are some of these important considerations.

Types of HR Data

There is an abundance of data and data sources in today’s digital world, and the first step to making smart data-driven decisions is understanding the types of data that are relevant to HR.

HR professionals deal with both structured and unstructured data. Structured data is information that can be translated into a spreadsheet-like program and easily analyzed or calculated. For example, employee name, age, type and number of skills, gender, and race are all categorized as structured data.

Unstructured data refers to information stored in its most raw format. This data usually consists of textual documents. For example, employee performance evaluations, mental health surveys, or company reviews on third-party websites.

Both data types are equally relevant to HR. For example, if an HR professional wants to calculate the median age and demographics of their company, they can look at their structured data, such as the employee’s age, address, and race. Likewise, if they want to assess the need to make more diversity-focused hiring decisions, they can view their demographics and text-based feedback in company reviews and surveys. In addition, when a vacancy arises, HR professionals can determine the need to seek candidates outside of their organization by mapping the skillset of existing employees and looking at upskilling initiatives and time needed to fill the position.

Between an organization’s employee data and surveys sent to understand how employees view their employers, HR teams can benefit from many types of data. But while the different types of data hold the promise of actionable insights, HR teams cannot understand the data without robust data management tools.

Collecting and managing relevant data

HR data contains intrinsically sensitive information. Everything from an employee’s background and medical history to salary and growth trajectory must be treated confidentially and with the highest degree of ethics.

Often, depending on the size of the organization, HR teams outsource the collection of certain types of data, such as mental health surveys or third-party data providers on company reviews.

Regardless of whether the organization uses internal or external resources, the ability to make decisions about data depends on how the data is obtained and managed. It depends on how organizations differentiate between voluntary information and information collected from sources that employees do not know they are being monitored or tracked, such as chat groups, emails, social media, external forums, etc.

How an organization stores, collects and manages its HR information is also often determined by the laws and regulations of its regions of origin. However, proactively creating data standards for HR teams can help not only at the process level, but also an employee-centric culture.

Turn data into decisions with HR analytics

Once organizations have established data collection and management processes, the last and most critical step is to understand the data well enough to base decisions on. This is where HR data analytics comes in.

At its core, HR analytics is a formula or algorithm-based approach to decipher everything from resource planning, hiring and performance management to compensation, succession planning and retention. HR analytics empowers HR teams to use data to strategically map an organization’s story.

While organizations often think that HR analytics should use AI and machine learning-based algorithms, simple spreadsheets and manual analysis processes can also be a good first step. In fact, according to Deloitte, 91% of companies use basic data analytics tools like spreadsheets to manage, track and analyze employee engagement, cost per hire, and employee turnover. However, to create truly data-driven analytics in scalable HR, investing in advanced AI-based tools is important.

Some areas where data analytics can add immediate value include measuring employee satisfaction, understanding employee learning needs, and prioritizing feedback about company culture. HR teams can use a mix of structured and unstructured data, including historical data, to understand burnout, pay dissatisfaction, team morale, and demand for diversity or sustainable practices.

Conclusion

HR teams can easily benefit from data-driven and analytics-driven decisions, but this can only be possible with a clear understanding of the types of data that drive insights, how the data can be managed, and which of these can be effectively analyzed with investments in high-impact technologies.

For a data-driven HR future, successful human-machine integration is essential. In particular, this will be critical for ensuring data ethics and avoiding biases that can be introduced by both under-trained AI models and humans.

Successfully integrating data analytics into the structure of an organization’s HR system is primarily about fostering a data-first culture. This data-driven approach helps organizations move from an operational HR discipline to a more strategic one.

Sameer Maskey is CEO at Fusemachines and an AI professor at Columbia University.