What is HR data?

While some HR data can be considered people data, it refers to any data that HR teams are responsible for looking after and storing. HR data can relate to:

• Talent development
• Onboarding
• Offboarding
• Interview data
• Employee survey data
• Learning and development data
• Succession planning data
• Absence data
• Job architecture data
• Financial data
• Demographic data

Structured vs unstructured HR data

While HR teams may have access to a wealth of data, it’s not always immediately actionable if it is unstructured. Examples of unstructured data include:

• Emails
• Videos
• Webpages

In contrast, structured data is organised and easier to analyse. Examples include:

• Spreadsheets
• SQL databases
• Times and dates
• Phone numbers
• National Insurance number

Using AI to organise unstructured HR data

Structured data can be stored in a management system without needing to be extracted first using specialist tools.

As we highlighted in our guide to AI in HR, unstructured data can be processed by AI tools, which clean and organise it, allowing datasets to be extracted and stored in a management system. This helps businesses organise and securely store employee and customer data. AI tools are also valuable for analysing large and complex datasets, as we explore in the next section on big data.

What is big data in analytics and why is it useful to HR?

HR teams often analyse data to make informed recruitment decisions or set recruitment budgets. This can include gathering information on both current employees and potential candidates, a process known as talent analytics. It involves working with big data, which refers to vast and complex data pools collected from a wide range of sources. Due to the size and complexity of these datasets, traditional tools are often insufficient for analysis. Instead, HR teams may rely on machine learning tools or AI to effectively process and interpret the data.

Differences between HR data and people data

Some types of HR data fall under the category of people data. People data is information collected on employees or customers, and when analysed, it can help businesses optimise and inform areas such as:

• Workflows
• Project management tools
• Learning and development
• Performance goals

We found that people analytics often requires a team of professionals trained in data analytics, who might be part of the business’s HR team. For more advanced analytics such as prescriptive analytics (which works with predictions to suggest outcomes), employers may need to hire experience data analysts. 

In contrast, HR data encompasses a broader range of data types, which can include financial data related to the business. This might involve recruitment budgets or the cost per hire.

Related: What is data literacy and how it can benefit employees

How can data analytics in HR help with business transformation?

HR analytics can play a key role in business transformation by saving time and money through the ability to predict change and the outcomes of new strategies or digital packages. This makes it useful to businesses whose industries are going through major or frequent changes.

It can also help leaders navigate volatile economic climates and a complex employment market. Our Hiring Lab findings show that the UK’s labour market is still facing inactivity levels higher than they were during the pandemic, requiring businesses to explore transformative solutions.

Workforce transformation can be useful for businesses looking to:

• Update their remote or hybrid working practices
• Upskill their employees in a ‘digital-first’ industry
• Identify skills gaps
• Conduct needs assessments
• Introduce their teams to an agile approach

Predictive data analytics in particular can provide employers with the ability to predict the outcomes of their workplace transformation measures, ensuring that they are successful. 

Diversity, equity and inclusion (DEI) analytics

Another area where HR data can be useful, is in informing DEI strategy. Some DEI metrics that employers can analyse and track include:

• Diversity metrics: these look at how well represented different demographics like age, gender or sexuality are across different business departments.
• Equity metrics: these metrics look at how fair a business is to its employees regardless of background or identity.
• Inclusion metrics: these provide insights into whether diverse perspectives and ideas are valued across an organisation and its departments. 

Other employee metrics such as employee engagement, employee satisfaction and employee turnover can also offer valuable information on whether a company is meeting its DEI targets. For example, if a business is experiencing high turnover rates particularly among employees from a certain background or demographic, it may suggest that there are issues with fairness or inclusivity worth investigating.

As we explored in our article on authentic diversity and inclusion initiatives, DEI efforts can lead to numerous benefits, including increased employee engagement, job satisfaction and collaboration. Therefore, it is often well worth employers exploring DEI analytics as part of their HR data strategy.

Related: What is cultural bias?

Leveraging all the potential of HR data

HR teams may only be using a fraction of the data they could be using to inform decisions. One overlooked form of HR data is psychometric testing results, often gathered during the interview process. Information that can be gathered from this testing include: 

• Working style information
• Personality profile
• Skill set including hard and soft skills
• Professional development opportunities

By analysing this data, employers can identify learning and development opportunities, identifying skills gaps and new learning styles. They can also use this to identify whether it would be advisable to hire someone who is a ‘culture add’ rather than a ‘culture fit’. An example could be hiring someone with a different personality to the team in order to add fresh perspectives. 

Other underutilised data includes offboarding or exit interview data. This information can be useful for informing both learning and development as well as retention strategies. Analysing the reasons why employees leave can offer insights into how to improve employee satisfaction and reduce turnover.

Industry best practices for using HR data 

When using HR data, it is useful to gain familiarity with some industry best practices. These include:

• Making sure that data is stored safely and in accordance with UK data protection regulations and UK GDPR
• Identifying realistic goals for the business which employees can achieve without risk to their psychological or physical health
• Ensuring that employees are comfortable with how their data is used and how often, e.g. some employees may find real-time performance analysis stressful in the long-term
• Determining whether any data being analysed is useful for achieving business goals
• Checking how fresh the data is, as stale data can provide redundant predictions
• Ensuring that advanced data analytics such as prescriptive analysis is conducted by experienced and qualified data analysts

Businesses across the UK may not be taking full advantage of the potential of HR data. There are many ways to use this valuable resource: hiring experienced data analysts to work with predictive analytics, using psychometric testing results to inform learning and development and applying HR data to shape diversity and inclusion strategies. When using HR data, it is important to follow best practices, particularly in terms of data protection regulations.