You Have Many Sources of HR Data You Could Be Using

The Data

According to Deloitte’s study, only 8% of companies think they have usable data.  But we know many HR managers underestimate how much data they have and how possible it is to extract, integrate, and analyse it.

Your Existing HR Data Sources

Your organisation might be readier than you think to employ advanced HR analytics. Most companies with 100 or more staff already have multiple sources of people and HR data that can be used for predictive HR analytics. This data has often been used just once for its original purpose, but it can be integrated with other HR data to create scientifically-supported insights into your people and their performance.

Many HR managers we talk to delay exploring HR analytics because they think they don’t have enough useful data and that collecting more will take significant time and resources. However, in this article we list just some of the HR information already available today in most organisations. This data might be in different forms and places but that is likely not a big problem. See our article on how easily these disparate data sources can be integrated nowadays for analysis.

Here are the main HR data sources that are very likely available in your organisation right now and how you could use them in HR analytics.

HR Demographics Data

Most employers collect and store lots of employee demographic data including: gender, age, ethnicity, occupation, seniority, tenure, salary levels, marital, and family status.
This information is mostly used in operational HR reports or used to categorise people. In fact, demographic data has the potential to be used in both advanced and predictive analytics (see our article for the definition of levels of HR analytics capability). In one major training evaluation project, we used tenure, seniority, and age to help predict which women would benefit most from a diversity program. Demographic data can also help to predict employee engagement, performance, and turnover amongst many other outcomes of interest for HR managers and the business.

Psychometric Assessment Data: Selection Assessment

When selecting amongst candidates, it has become increasingly common for companies to use psychometric assessments (e.g., mental ability tests, personality questionnaires, competency-based interviews etc). However, few organisations take advantage of the data collected once the recruitment process is over. Yet, this data can tell you so much more if combined with other data sources and properly analysed. This data could help answer questions like:

  • ‘Which factors are the best predictors of employee performance?’
  • ‘How well does test performance predict real world performance?’
  • ‘Which traits and abilities best define good leaders in our firm?’
  • ‘What can we learn by looking at who we didn’t hire?’

Importantly, we can also help you select the assessment tools that are most predictive for your firm and save time and money on those that aren’t. To make life easier, we have tools and techniques for extracting assessment data from your reports and databases to help answer all these questions and more.

Psychometric Assessment Data: Development Assessment

Similarly, most companies use psychometric assessment measures to help identify development needs or to support training programs and improve teamwork.
This data can be used in the same way as selection assessment data but can also be aggregated to help understand broader training needs or organisational strengths and weaknesses. There might also be clues in this data about employee engagement, culture issues, and the leadership styles that are more or less effective in your company.

Training Program Data

This is the area of HR is where we hear the most doubt and uncertainty from HR Managers. They know training is necessary for the personal and professional development of their people, but what training will benefit whom and how much time and money should be invested for what return?
In fact, HR departments usually store or can readily access detailed information to answer the ‘return on investment’ question and many more. Training program attendance, content, costs, and feedback are typically all accessible. This information, when combined with other data and measures, can provide insights into the design and cost/benefit analysis for specific training programs as well as insights for the organisation more broadly.
Most Learning and Development Managers know about Kirkpatrick’s model to evaluate the effectiveness of training but find it too resource-intensive to move beyond collecting participant reactions (Level 1). HR analytics using existing data or a pre- and post- training assessment often make it possible for many companies to move up at least one or two Kirkpatrick levels in their training evaluation. See our article about just this topic.

Performance KPI Data

Almost every company uses key performance indicators (KPIs) in some way to measure the performance of people and the business. KPIs are really the most important data source for HR analytics because they are often the outcome variables that HR and leaders are trying to predict and improve.
Once companies can determine which factors predict higher performance, then they can apply these insights to recruitment processes, talent management and retention, training programs and so much more.
A significant advantage of KPI data is that it is collected regularly – every 6 or 12 months year after year. This increases the likelihood that we can combine it with other HR variables and use it for longitudinal or trend analyses.

Employee Turnover Data

Employee turnover is another important outcome variable that HR managers want to be able to predict. Employees leave their company for many different reasons – the research shows these reasons are mainly: they don’t enjoy their work; they don’t get to use their strengths enough; and they aren’t developing skills to grow their career. Knowing who might leave and the reasons is of great interest to HR departments who want to retain their most valuable employees and intervene before they get to the point of leaving.
Most companies likely already possess the necessary information to detect the most important local drivers behind regrettable and non-regrettable employee turnover. Our HR analytics methods and tools are excellent at analysing a multitude of information sources that seem unrelated at first but can detect and highlight many turnover-related issues.

360-Degree Survey Data

360-degree surveys can be a goldmine of useful information if your firm has had enough employees complete them. Depending on the 360-degree survey you use and the other available data you have, it might be possible to generate insights like: which leadership styles relate to the best business results, employee engagement scores, and employee turnover; the gender or cultural differences in leadership style and leadership expectations that exist in your firm; how leadership and culture differ between departments; how effectively bosses evaluate the people that work for them. Any of these insights would be hugely beneficial for improving any number of recruitment, talent, and training programs.

Engagement Survey Data

Most mid- to large-sized companies conduct a regular survey of employee engagement. While engagement is another example of a key outcome that HR leaders want to predict and improve with HR analytics, it can also be used to predict other interesting outcomes in the business like financial performance, turnover, and productivity.

Social Media Data

Many companies today have social media accounts. These might be public channels to communicate with their existing and potential customers and receive feedback on their products or services. They might also use social media tools within the firm to connect and communicate with staff. For all the wrong reasons, companies now realise how much valuable data can be extracted from social media for marketing purposes. But social data – collected and used ethically – can be a vital data source for HR analytics. Identifying talent, mapping communication networks, analysing employee sentiment and employer brand, and predicting conflict, turnover, or success are just some examples.

Using Your HR Data in HR Analytics

We find that HR managers consistently underestimate how much they could do with the data they already have. But, as you can see, your organisation very likely already has multiple sources of HR data that we can help you identify and integrate right away. Applying our HR knowledge with some nifty data-science tools can then provide you with powerful insights and predictions about people and performance sooner than you thought possible.