Today, order to interpret the data, or

Today, data science is so advanced that can even detect our sentiments in the emails that we are writing. According to Tom Davenport and D.J. Patil (2012)1, working as a data scientist is now considered “the sexiest job in the 21st century”. Big data is making a difference in many use cases that we did not expect few years ago, particularly in human resources. It can help companies and HR teams to respond to questions on individual level – Why this person is a top performer? What is the outcome of training result? How this person contributes to profit margin? Why certain management practices succeed and others fail? and so on.HR teams use different software (like Taleo, Jobvite, Lever, etc.) to capture a huge amount of data about the employees and candidates. Nevertheless, the biggest challenge is to create different models in order to interpret the data, or link the data with other external sources like LinkedIn, Facebook, Twitter, etc., or use this data for strategic decisions. A study by the Massachusetts Institute of Technology (MIT) and University of Pennsylvania (2011)2 found that companies with mature analytics functions in general produce 5–6 percent higher financial returnsHR and leadership team should drive the process to build a people analytics team and replace the legacy processes. Nevertheless, going there involve a change in mindset and removing some organizational barriers. According to a Harvard Business Review Research Report (2014)3 asking companies for the “three biggest obstacles to achieving better use of data, metrics, and predictive analysis by HR and talent management professionals in your organization”, those top obstacles include:Adopting HR analytics framework is considered slow, despite many tools, exponential increase in available data, and many decades of research. In their article “Human capital analytics: why are we not there?” (2017)4, Boudreau and Cascio are exploring why many organizations are struggling to move from operational reporting to predictive analytics. They found that HR analytics is more effective if analytics tools are applied to “user experience itself”.  In order for HR data to be helpful in strategic decisions, “HR needs to pay more attention to the product features that successfully push the analytics messages forward and to the pull factors that cause pivotal users to demand, understand, and use those analytics”.The scope of this paper is to understand why companies should aim to reach the predictive analytics level of maturity. In addition, we will have a more practical approach by applying this to a framework proposal for Ingenico ePayments case, one of the biggest payment service providers, worldwide.