The 10 golden rules of HR analytics

Patrick Coolen

Is HR lost without analytics? And with analytics I do not mean reporting on FTE, headcount, employee engagement, or for instance providing the number of talents in your organisation. With analytics, I mean applying statistical methods, like regression analyses, on data sets that combine HR and business data. The latter allows you to really determine the impact of HR elements such as, engagement on, for instance, client satisfaction, products sold or market share.

This type of analytics will (re-)claim that strategic seat at the business table HR has been talking about for the last ten years. So not surprisingly, the mission of my department is to make HR more fact based and to inject decision making with sophisticated data analytics. To achieve this, we have been focusing for the last year and a half on HR analytics and strategic workforce planning. And we are pleased with the progress and the appetite for our services within our organisation. In this article, I am happy to share our lessons learned in the field of HR analytics.

1. Combine strategic workforce planning and analytics: We strongly believe that strategic workforce planning and HR analytics should go hand in hand. For us, strategic workforce planning has to make sure that HR is executing the right activities to optimize our workforce in order to support business goals. HR analytics can provide insights that can help you focus on the relevant aspects of the workforce. For example, analytical research may show that a specific leadership competence, like program management skills, has no positive effect on business goals. The same research may indicate that being an inspirational leader is a main driver for business success. In this case, your organisation should reconsider shifting learning and development efforts towards creating inspirational leaders. So HR analytics helps you to focus on the right things within strategic workforce planning. A second example where HR analytics can support strategic workforce planning is in evaluating the effectiveness of specific HR activities, like onboarding, talent development, or leadership programs. Analytical research may show that new employees are two times faster in meeting their job requirements after they participated in an onboarding program. In both examples, HR analytics can provide valuable insight that can be used in strategic workforce planning. For this reason, we decided to position strategic workforce planning and HR analytics in one department.
2. Combine analytics and intuition: Be aware that data analytics by itself is not enough to make the right decisions. Experience, intuition, and analytics will have to work together to get the best results and insights. So make sure you have business experts, HR domain experts, and analytical experts in the same room to look at the models and interpret the research results. Data analyses inspire intuition and vice versa.
3. Make analytics business relevant: All of our statistical research focuses on a real business problem or opportunity. Furthermore, we want every HR analytics project to be signed off by a member of the management group and by an HR director. By doing so we make sure that the possible outcomes of our research have real strategic value for our business. As supported by CEB research, our strategy is to stay close to our business needs and business application and improve on our technical and statistical skills in parallel.
4. Involve legal and compliance: We do not start any project without approval from legal and compliance. We take the royal approach when it comes to collecting and processing employee data. Also, in the first stage of setting up our department we asked advice from legal, compliance and IT security. You can imagine the importance of IT security when you are transferring data between systems.
5. Think of the skills you need: Many people think that statistical skills are most important when applying analytics. They are right about the importance but it is definitely not the only important skill. If you are only focusing on statistical skills, you might end up with good statistical analyses on irrelevant business questions with insufficient data that is communicated poorly to the business. So that is why business knowledge, HR knowledge, communication, and consulting skills, IT/data architecture knowledge and data visualization skills are also vital to do analytics. We did not have the data analytical skills in our organisation and that is why we aligned with an external data analytics expert right from the beginning. An extra argument to work with an external partner is that they are allowed to collect and process more data on an employee level then we are allowed internally. And this simply results in better data analyses. My team members are providing HR consulting, and IT/ Architecture knowledge and visualization skills. To have access to vital business knowledge, we align with HR business partners and directly with subject matter experts in the business.
6. Business subject matter experts (SME’s) are key: Talking about SME, they are key in almost every step of the process. The SME’s are those persons who work on a daily basis with the business data you want to use. They have in-depth knowledge to co-design the logical models in the first phase. They can possibly explain missing values or data inconsistencies during the data exploration phase. And last but not least, they can really help you interpret the results of the analyses before you bring them to senior management.
7. Start small and learn: If you have a dataset you can start with analytics tomorrow. If you have two datasets you can connect them tomorrow (depending on complexity), and start with analytics the day after. Just use SPSS, SAS, R, or you can ask an external vendor to do the analyses for you. You do not have to wait until you have all the data you ideally want for the research. Just start with what you have and run a second project once you have new data available to run more sophisticated models. You will experience a steeper learning curve when you start small and learn from there.
8. Be realistic: Analytics is hard work and it takes a while before you agree on the research question and model, before you receive the right data, before you connect the datasets and before you checked data quality and cleaned the data. These steps take approximately 75% of the whole research project. Once this is done you can relatively quickly run the models and interpret the results. A wise thing to do is not to give a final delivery date before you received all the datasets. And do plan some extra time for analyses after receiving the first results. You probably want to do some extra analyses before you finalize your results and go to the business.
9. Make results actionable: Maybe the most important thing to do is to make your results actionable. Your relevant research results should have an impact on policies or future interventions. Knowing the results is not enough; your organisation must be willing to act on them. It is not always easy to translate results into action. Our department supports HR business partners and the business in doing so.
10. Preach & Teach: Once you have the results, start sharing them! Not only with your customer but also with relevant HR experts within recruitment, talent development, reward, learning and so on. Preach the benefits of analytics to everyone who wants to hear it. This will increase the awareness within the HR community and beyond on what you can do with analytics. And finally provide your HR community with regular workshops to explain the principles of HR analytics.

Reprinted with the permission of Patrick Coolen, Manager HR Metrics and Analytics at ABN AMRO Bank N.V, Amsterdam, The Netherlands.


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