Today’s edition of our blog post series written by the speakers giving talks at the upcoming 2ML event, covers how ABN AMRO applies Machine Learning in their Human Resource Management department. Auke IJsselstein, the HR Analytics Lead at ABN AMRO, provides a summary of his talk for us. To discover the insights from his full speech, we invite you to attend his presentation session at 2ML Madrid Machine Learning on May 11, 2017.
Exciting times for the Human Resource Management department at ABN AMRO! This month, the HR Analytics department, has launched their all new and improved proposition into the organization. For four years they have been servicing the HR organization and Senior Management teams with their products HR Analytics and Strategic Workforce Planning. The HR Analytics Team views the fact that their department has doubled in size this year as a huge compliment for their work and also as a sign that the organization strongly believes in the power of data analytics and working in a fact-based manner. During these four years of experience ABN AMRO has learned a great deal, sometimes the hard way, as they admit. With the new integral approach to Fact-Based Human Resource management, the HR Analytics Team feels that they can make good use of their lessons learned and, having optimized their products, processes and tooling, are able to focus fully on gaining impact on their business goals.
As from 2013, ABN AMRO has been performing predictive analyses within the HR field. The main focus is to provide management with relevant insights to make better decisions regarding how to optimize their workforce to be able to reach the bank’s goals. In the early days, they did this mainly through using classical forms of analysis such as multivariate regression algorithms. As from 2015, the HR Analytics Team was introduced to the BigML tooling by iNostix (now iNostix by Deloitte). They started using Machine Learning techniques such as Decision Trees, Random Forest and Clustering next to the more traditional analysis methods. This gives them more flexibility and knowledge to match analysis methods better to the business questions and the characteristics of data at hand.
The aim is, of course, to be able to predict certain business and HR performance outcomes, but even more important for the HR Analytics Team is to better understand the drivers of these predicted outcomes. Most of their research focuses on explaining the human factors in reaching company goals such as client satisfaction and financial performance. For example, when they find that cultural aspects (e.g. communication, collaboration or leadership style) have a direct impact on customer satisfaction, they are able to give advice where and how to focus culture and training interventions.
Another trend they are witnessing and exploring is that in the near future, performing (basic) analyses will no longer be solely reserved for analysts. With the development of analytical tools that combine complex algorithms with user friendly interfaces and comprehensive visualization possibilities, analysis slowly becomes reachable for non-expert users. The Human Resource Management department at ABN AMRO are not there yet, but they are already offering on-the-spot analyses sitting at the table with senior managers. Of course this comes with risks and requires a good preparation, but they believe this is the way analysis will evolve, insights becoming available for larger groups of people.
At ABN AMRO Bank, they could not have achieved their current level of maturity in the field of People Analytics without their partners from iNostix by Deloitte, who helped them with building new capabilities and performing analyses, and BigML, who introduced ABN AMRO into the world of Machine Learning providing the ML platform. Therefore, Auke IJsselstein, Lead HR Analytics at ABN AMRO, is proud to present at the 2ML conference their views on fact-based HR, their proposition to transform the HR organization with hard-learned lessons, and examples of how they were able to bring valuable insights to senior leaders through Machine Learning and data mining.
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