Yesterday, I was at the 5th Icongroup People & Workforce Analytics Seminar in Brussels. Since the seminar is about HR Analytics (also known as People Analytics or data-driven HR), and consequently also about research and evidence, my colleague Gido van Puijenbroek opened the event with a small test: is it possible to sit on an inflated balloon? I won’t reveal the answer, but I promise it is safe to test it at home (or in a room with 100 people). Next to the balloon challenge, there is a number of less trivial lessons that I would like to share.
HR Analytics is a win for both the organisation and the employee
One of the main reasons that I love working in HR Analytics is that I can help organisations perform better by helping the individual employees in the organisation. Yesterday I noticed a lot of people other than HR Analytics practioners agree with me. In addition, I heard excellent cases that prove this point. There are two cases that I want to dive deeper into.
Charline Cleraux and Annelies Bartlema shared their story about the massive redeployment process that happened within ING Belgium. They considerably contributed to making that process a success by using HR Analytics. Even more impressive: there was not a single complaint about the process from within the organisation! They supported by creating an assessment based matching profile for each employee. Subsequently the profile supported the matching committee to make better funded matching decision between new jobs and incumbent employees. Optimizing this process served both the organisation and employees, since job fit was optimized, and the process was less prone to all kinds of biases.
Jan Billekens from Saint-Gobain found out, using an algorithm, that there are employees within the workforce that might be considered as talents, but for one or another reason are not in Saint-Gobain’s talent program. After this discovery all ‘hidden gems’ were reconsidered and if the recommendation from the algorithm could be echoed, the person was moved into the talent pool. Since the population is not static, the organisation goes through this cycle twice a year now. Obviously, this is in the interest of employees (potential optimization), but it also supports the organisation as it can now unlock all value from its human capital.
Employee experience should be the same as Customer Experience
Two tech companies, Salesforce and Cisco, showed the strength of giving employees the same experience as customers, thereby driving value creation through employee experience.
Louise O’Dwyer showed how Salesforce uses its own applications to capture the Voice of the Employee and how they use these data to create algorithms that support employees and managers in doing their job faster and better. For instance new hires are made aware of instruction and learning opportunities that have greatly benefitted previous new hires that were similar to them. Managers get push notifications informing them what a new hire should be able to do after a certain period and what interventions they could consider to improve the learning curve. All these things lead to 90% of the employees being engaged and promoting the organisation to clients and potential hires.
Amanda Diston of Cisco showed us the impact of the proposition they make to their (future) employees. Making this proposition and sticking to it, increases employee engagement. How do they know if the organisation sticks to its promises? As an example, employees assess their leaders every quarter, leaders are provided with this input (data) and are supposed to follow up on the feedback.
Use data as a catalyst for data quality
As Oliver Kasper of Swarovski said: “Everybody loves good data quality, but nobody loves working on data quality”. Consequently, it is required to create a rinsing mechanism.
Suggestion 1: the best way to find out what needs to be improved and actually getting data quality up, is by simply using the data. As Peter Hartman of Getinge told us, using the data you have is always better than just another guess. Moreover, by using the data and making the state of the data visible, even if the data is not even remotely correct, a feedback loop can be ignited.
Suggestion 2 came from Giles Slinger: after each data run/project, do send out a data error/quality report, so the data quality can be improved.
These practices help towards a (more) swift delivery of the next project, as there is probably less data cleansing to do and it increases the chance of getting killer insights.
Like the previous lesson this one is an evergreen. Multiple speakers, including ABN AMRO’s Stijn de Vries, put emphasis on the importance of selecting HR Analytics projects that solve true business problems and have strong stakeholder commitment. It was even encouraged to be picky when taking on projects. After all, time can only be spend once and the more impact generated the stronger data driven HR is embedded in the organisation in the long run.
Make yourself redundant ASAP!
The last but not the least lesson I would like to share is about redundancy. Stefaan Rodts of Aegon revealed his ultimate goal is to get released from his HR Analytics Director position within a couple of years. Not for any bad reason, but because his aim is to make the HR function data driven. In his view most HR employees should ultimately have a data and structured problem-solving mindset and they should be able to interpret reports and insights from analyses. In such a perfect world each company would still need data scientists, BI specialist, researchers, but HR would work directly with them rather than via or with an HR Analytics Translator.
All in all, day 1 was very interesting and exciting and I’m looking forward to day 2 of the event!
Also read: Icongroup People & Workforce Analytics Seminar 2018 – Day 2