Data fluency connects staff through a set of norms, procedures, instruments, and terms across positions in a company. It is necessary for an organization to be data fluent and accept new technologies to work better.
FREMONT, CA- Innovation has come a long way in the field of HR. The HR leaders have a risk-averse mindset as they are on the way to digitalize HR practices. While it will be easy to search for human capital on cloud or smartest applicant tracking systems to qualify with artificial intelligence, it is also possible that good matches can be neglected while using technology.
Digital HR transformation is not just about technology. The future mostly relies on data literacy and fluency of practitioners. These technologies will be more effective when they will complement the humans associated with them.
Predictive analysis tools are used to extract information from data and later used to predict trends and behavior patterns. Decision support tools are computer-based and are designed to support decisions. These tools powered by Artificial Intelligence (AI) can be very powerful if they can easily access huge data quickly. Transformation of data into values is just one part of the process but turning values into recommendations is another.
Literacy Skills of HR Data:
According to a report generated by a poll conducted at IMPACT 2018, Bersin’s HR Conference, it was found that only 9% of the HR practitioners believe that their HR data literacy skills are appreciable.
It is true that traditional HR professionals may not have huge knowledge in data munging and regression analysis like the trained data scientists. HR pros must learn how to validate, audit and manage the inflow of data about human capital.
In another way, HR pros must-have foundation knowledge of mathematics, logic and even storytelling and visualization to grasp what is happening on the ground through hard data. If the more advanced HR tools can give the numbers or formulate graphs and spreadsheets instantly, it is not sufficient for the HR leaders to rely on them.
Data literacy basics vary from a variety of skills like reading a dashboard to differentiate between a correlation and causality, and understanding the basics of AI such as machine learning and natural language processing.
The main aim is to understand the basics around big data and data mining and how that data will be used. It is also important to develop an analytical mindset to understand and analyze everything.