Confluence of HR Technology and Analytical Design
Big data analytics is the buzzword in today’s corporate circles. Organizations are implementing it in various domains of their operations. They are also increasingly creating large quantities of data and they require tools to analyse it and add value to their businesses. Like other organizational functions, HR is also seeing a confluence of technology and analytical design. The recently concluded SHRM HR Technology Conference 2018 saw an active debate on the topic of ‘Confluence of HR Technology and Analytical Design’. The debate centered on the advent of technological innovations like artificial intelligence, IoT and Big Data analytics and the evolution of HR analytics in this environment.
The discussion was moderated by Neeraj Tandon, Practice Lead – Workforce Analytics and Planning for Willis Towers Watson Asia Pacific. The panelists included MVS Murthy, Chief – People Office and Workplace Solutions, Future Group; Arjun Pratap, Founder and CEO, Edge Networks Pvt Ltd, and Siddharth Gupta, CEO and Founder, Bluedolph.in.
Murthy opened the forum by discussing how HR as a fraternity is relatively new to big data analytics. He opined that it has been largely unexposed to big data tools in the past, relying on traditional methods. However, the time has come to go beyond dashboards and metrics and harness the power of big data through the confluence of analytical design and HR technology. There are several factors which attribute to the growing relevance of analytics design in HR. The forum discussed some of the key points, including:
1. There is a requirement for high quality data to make HR decisions.
Gupta explained that while organizations are generating employee data from such sources as attendance and performance trackers, most of it lies untapped. Marrying analytical design to HR processes will help unlock the potential of this data.
He also emphasized the need for accuracy in data. As organizations try to outsmart competition and stay ahead of the curve, their daily operations require accurate data for correct decision making. Integrity is an important element for enterprise data. Using analytics helps organizations get actionable intelligence through their data. He mentioned how even existing SQL databases often require clean-ups to make them readable. Google’s location data may have discrepancies in it every now and then, but companies like Uber and Ola have enabled their businesses with the help of machine learning experts who solved the discrepancies in the Google data and made it accurate.
HR analytics has evolved from being just a method of measurement to becoming a process of leveraging data sources and predicting the likely outcome of various scenarios. Arjun Pratap provided an example how employee attrition can be predicted using advanced tools. During attrition modelling for a global clinical research organization, Edge Networks achieved 90% accuracy in predicting attrition. The workforce demand and supply gap in that industry is quite high, so an attrition model with 90% accuracy is quite valuable.
Going forward, HR analytics may be enabled by predictive models in solving problems like employee burnout. For example, analytics will let an organization know which employees are overworked so that it may reward them with a family holiday, which will help boost employee retention. Similarly, it can be used to measure HR effectiveness, and is helping companies make better HR decisions.
2. The flow of unstructured data within HR is rising.
HR data has evolved to become big data in terms of its complexity, diversity of sources, and its relevance to business, and it requires big data analytics to operate efficiently.
Analytics are capable of handling large volumes of data and present it in a visually easy to understand format, says Pratap. GPUs today are able to process faster than ever, which means that large volumes of data can be churned much quicker. Technology has enabled multiple systems to speak to each other, which allows companies to look at employees holistically instead of in silos through a more complete system of information. Because data is not seen two dimensionally anymore, we get faster and more holistic insights assimilated from multiple sources.
Most of the data comes from disparate sources and is unstructured. It is streaming constantly, and organizations need to quickly classify them into meaningful categories to gain insights for decision making. The volume of data streamed by simple IoT devices today is 10 times more than what these devices were generating a year ago. Even data from employees using devices such as fitness bands can give meaningful insights to companies.
3. Deploying HR analytics has functional benefits.
Organizations have already started deploying analytics and AI tools to increase their efficiency within HR, and these tools are helping companies sift through resumes as well as employee information, says Pratap. They can categorise unstructured data so that it can be used for improving employee engagement and talent acquisition processes. Tandon added that advance algorithms like Natural Language Processing (NLP) can decodify unstructured voice or text data and make sense of it. Similarly, engagement with employees can also be enhanced using external data from websites like Glassdoor and LinkedIn.
AI algorithms are powering the analytical tools that aid HR decision making. Pratap hailed predictive analytics as a tool that can provide accuracy in predicting events and bring improvement in such HR areas as workforce retention and talent acquisition. For example, by predicting the demand for a resource in the next month, organizations can plan in advance and hire the right talent where they will need them, which will reduce costs. Companies can better manage their workforce because supply and demand are managed seamlessly within the organization itself.
Learning and development is another field where HR analytics is useful. Companies already build content aggregation platforms for their employees’ training. AI based analytics can add value by designing intuitive learning paths according to the demands of a job and the skill gap identified in the employees.
HR analytics can take employee engagement and productivity to the next level, according to a study conducted by Murthy, which identified five parameters that aid HR functions in improving their decision making using analytics. These parameters are: awareness of HR analytics; knowledge of how it can aid their decision making; understanding of how to implement it across the HR function; knowing the various domains where it can be applied; and the use of representation techniques like visualization or storytelling to present the data. Murthy’s research corroborated the understanding that these tools help in people and policy related decisions, and stressed the need for good technical support enabling quick transformation to HR analytics.
Machine learning is the backbone through which Big Data cleans up unstructured data, which results in accuracy along with speed. HR analytics provides both momentum and accuracy to HR processes. In fact, organizations have traditionally been predicting patterns in attrition and hiring, but on smaller data sets using offline statistical tools. Today, with advanced tools, companies are advancing their own methodologies to solve business problems with higher accuracy and speed.
A confluence between HR tech and analytical design is taking place. There is excitement which is evident from the fact that HR analytics is one of the most debated topics of any HR conference in India. In order to make it an accepted norm among employees, corporate leaders need to be convinced that HR analytics is a transparent and safe method that adds value to their organizational experience. The way to win the heart of employees is to let them know that the data is theirs and AI can help them ease issues and lead better lives.
By Swati Thakur
Source: Society for Human Resource Management