How to effectively employ data-driven HR decisions with HR analytics
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The years 2020 and 2021 have caused organizations globally to rethink their HR strategies. While 2020 had HR professionals grappling with a COVID-induced overhaul of work policies and remote operations management, 2021 saw around 47 million people quitting their jobs, testing HR teams’ abilities to engage existing resources while seeking new ones amid the Great Resignation.
During this period of extreme transitions, the HR function has evolved to rely on data and analytics – ranging from employee and organization information to data around how HR dilemmas have historically been addressed. There is also increased reliance on technology and AI-powered automation to turn data into valuable insights throughout the HR process.
According to Fortune Business Insights, the global human resource technology market is projected 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. Additionally, a Mercer report found that 88% of companies globally use some form of AI in the form of intelligent chatbots, candidate engagement systems, recommendation engines and more.
The growing dependency on data-powered insights can be accorded to the need to efficiently make HR decisions that consider both employee happiness and business growth. However, to successfully employ data-driven HR decisions, businesses must understand steps critical to the process of turning data and analytics into valuable insights. Outlined below are some of these key considerations.
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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-led 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 can be easily analyzed or calculated. For example, employee name, age, types 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 of these data types are equally relevant to HR. For example, if an HR professional wants to calculate their company’s median age and demographic, they can look at their structured data such as employee age, address and race. Similarly, if they want to assess the need to make more diversity-forward hiring decisions, they can look at their demographic data and text-based feedback in company reviews and surveys. Furthermore, if there is an opening for a role, HR professionals can ascertain the need to search for candidates outside of their organization by mapping the skill sets of existing employees, and looking at upskilling initiatives and time needed to fill the position.
Between an organization’s employee data to surveys sent out to understand how employees perceive their employers, HR teams stand to benefit from many data types. But while the different types of data hold the promise of actionable insights, the HR teams cannot begin to make sense of the data without robust data management tools.
Collecting and managing relevant data
HR data intrinsically comprises sensitive information. Everything from an employee’s background and medical history to salary and growth trajectory should be treated with confidentiality and 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.
Irrespective of whether the organization uses in-house or third-party resources, its ability to make decisions on data hinges on how the data is sourced and curated. It depends on how organizations distinguish between volunteered information and information collected from resources that employees aren’t aware 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 dictated by the laws and regulations of its areas of origin. However, proactively creating data standards for HR teams can help not only at a process level, but also generate an employee-first culture.
Turning data into decisions with HR analytics
Once organizations have data collection and management processes in place, the final and most critical step is to understand the data well enough to base decisions on it. This is where HR data analytics comes in.
At its core, HR analytics is a formulaic or algorithm-based approach to deciphering everything from resource planning, recruiting and performance management to compensation, succession planning and retention. HR analytics empowers HR teams to use data to strategically map out the story of an organization.
While organizations often think HR analytics must employ 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-analysis tools, such as spreadsheets, to manage, track and analyze employee engagement, cost per hire and turnover rate metrics. However, to truly make data-driven analysis in HR scalable, investing in sophisticated AI-based tools is important.
Some areas data analytics can add immediate value to are gauging employee satisfaction, understanding employee learning needs and prioritizing company culture feedback. HR teams can use a mix of structured and unstructured data, including historical data, to understand burnout, salary dissatisfaction, team morale and demand for diversity or sustainable practices.
HR teams stand to readily benefit from data and analytics-powered decisions, but this can only be possible with a clear understanding of the types of data that deliver insights, how to manage the data and which of these can be effectively analyzed with investments in impactful technologies.
For an HR future powered by data, successful integration of humans and machines is key. This will be particularly critical for ensuring data ethics and preventing biases that can be introduced by both undertrained AI models and humans.
Above all, to successfully incorporate data analytics into the fabric of an organization’s HR system is to foster a data-first culture. This data-driven approach helps organizations shift from an operational HR discipline toward a more strategic one.
Sameer Maskey is CEO at Fusemachines and an AI professor at Columbia University.
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