Last August, we hosted a mini webinar for HR professionals seeking to understand how leveraging HR data helps in making better people decisions. The featured speaker is Katerina Bohle Carbonell, an Instructor at Northwestern University in the United States who has conducted various HR analytic projects for governmental agencies in Europe and the Middle East.
HR
is not any more pure support and back-office role in companies. The role of HR has evolved into more active responsibilities like shaping business strategies and proactively developing the workforce. But HR teams can’t do this successfully without having the necessary data to support and influence people-related decisions.
The webinar provides foundational information on HR analytics and how these facts and figures guide HR specialists to make decisions that are in the best interest of the employees.
Here are the highlights of this event.
What is HR analytics?
HR analytics is a unique approach to HR functions and decision-making using collated data. The outcome of HR analytics is entirely dependent on the data quality gotten from HR metrics. Like any other form of analytics, all procedures are the same, and the only difference is the change in domain.
The purpose of HR analytics is to target the HR audience and not the Data Scientists. It helps HR professionals gain clarity on the best and worst practices that could be carried out while on the job. HR analytics is now an integral part of people operations; hence it's necessary to understand the concept and put it into practice.
In summary, HR analytics is all about:
- Making better employee management decisions
- Moving beyond descriptive to prescriptive HR analytics
- Finding links between employees’ actions and business outcome
- Segmenting workforce
How to do an HR analytics project
Adopting HR analytics into your project system requires precision, as it deals with the future of the company. To learn how to do an analytics project that yields positive results only, we have divided these procedures into two sections.
Determine what HR Analytics Maturity Level you are on
The HR maturity level gives you a clearer view of how much progress your HR team has made using HR analytics or how much progress your HR team needs to make before seeing the effects.
Here are 5 levels of HR analytics:
Level 0 – Gut Feelings
At level one, HR practitioners are solely relying on anecdotal evidence. In other words, no data is being utilized and the HR manager depends on their intuition based on experience and observations.
Level 1 – Operational Reporting
At this level, the HR team collects all the necessary data but does nothing about it. There is no automation or further interpretation of the collected data. There are also no processes around the collected data and all operations are done ad-hoc.
Level 2 – Advanced Reporting
The reporting structure at companies on this level is getting more standardized. However, the data collection is still done ad-hoc, without any automation or process around it.
Level 3 – Advanced & Automatic Reporting
At level 3, data collection is automatic and happens in the background, making it easier to track any recorded data. Data is more descriptive at this level, and dashboards are put to good use.
Level 4 – Predictive Analysis
Companies run automatic data collection and analysis processes at this level. Data analysis is descriptive and prescriptive. They leverage historical data to predict outcomes such as attrition or performance rate by using dashboards to monitor.
Understand HR Analytics Project Cycle
The HR Analytic Project Cycle is a result-driven order on how things should work when embarking on an HR analytics project. To complete this cycle, you’ll need to go through 6 stages.
Step 1: Strategical
You map out a plan at this stage. Try to get stakeholder requirements and figure out the important questions relevant to the goals of your company. This is the foundation that fuels your quest for data collection and ensures you’re looking for the correct data needed to solve company issues.
Skipping this stage means you’ll spend more time looking for the wrong data. The strategical work never ends. It keeps on going in the background.
Step 2: Identify data source
Look at the tools your company uses and the possible results you could get using these tools – this is only applicable when using operational or behavioural data.
Another approach is conducting surveys; determining who to take these surveys and questions to include in them.
Step 3: Collect data
You must make sure that third-party integrations are set up properly. So, no data gets missing.
Step 4: Clean and transform data
Data never comes in a clean format. Just as with any other analytic project, you’ll be spending a long time cleaning and transforming the data. You will also need assistance from someone vast in statistical methods and data analytics to avoid errors. These actions are necessary for making sure the data is in the proper format of use.
Step 5 & 6: Communicate results to stakeholders and Influence strategy & decision-making
Step 5 and 6 involve communicating results to stakeholders and how these results will be used in making the next big decision. The ease of these steps is dependent on Step 1.
Example of an HR analytics project
For a more realistic and practical view of how HR analytics works. Here’s an example of an HR analytics project.
STEP 1: Identify the problem
BUSINESS PROBLEM: REDUCE ABSENTEEISM (Lack of Employee Engagement)
STEP 2: Identify the data source
Data Source: Operational Data
After identifying the problem you wish to address, you need to gather data. The data to be collected must be specific to the affected employees. Not all your employees have reduced engagement levels, so resist the urge to generalize. It is wrong and might cost you your time.
Here is employee-specific data to collect in the case of absenteeism: Absenteeism data for each business unit or each day
STEP 3: Test where absenteeism is a real problem – Where is it abnormally high?
You also need to figure out the intensity of the problem. So, you have to collect benchmark data of absenteeism data for different age groups, educational levels, etc. With this benchmark, you can determine if absenteeism is higher or lower in your company. If it is higher than the benchmark, there is a problem; if not, it is of little concern.
Check out the entire HR Analytics Talk by watching the full event below:
👩💻 About Katerina Bohle Carbonell:
Katerina Bohle Carbonell has conducted various HR analytic projects for governmental agencies in Europe and the Middle East. She has been teaching executive-level HR courses, such as leadership development, since 2012. She is a contributor to the Future of Work webinar series offered by HSTalks and appeared in several podcasts to talk about remote working and team processes.
If you'd like to learn about her upcoming HR Analytics course, contact her at katerina@netnigma.com
