On November 13, Orange Silicon Valley hosted an offsite event that focused on corporate digital transformation and people sciences at the University of California, Berkeley. Professor Gregory LaBlanc, who specializes in economics, data science, and finance, gave an overview of recent trends and research impacting digital corporate transformation and the future of work spaces. The event also emphasized the importance of data analytics in human resources use cases.
The event opened with an eye on people’s lives in a data-driven culture. Data has made it easier to analyze problems and make decisions, leading to a higher profile and increased demand for data science.
LaBlanc began by citing sports data and analytics as surprisingly interesting topics for HR professionals, because everything is transparent and easy to follow and identify. With a given data set, one can look at a player and predict what their future performance could look like through digital and predictive analytics. Additionally, sports analytics provides a means to identify under-priced players and recruit them, or (alternatively) overpriced players to be sold.
Data science, which is an interdisciplinary field that uses scientific methods, processes, and systems to extract insights from data, is another popular topic in HR. According to LaBlanc, this subject is all about training and scoring. Data scientists go through a constant trial-and-error process to identify niche methods that correspond to specific fields for tracking data. Those scientists can enter data into a regression table, figuring out which models are better and easier to measure and analyze.
“Data science in HR has the potential to produce high or low stakes, depending on our decisions, so HR teams have to prioritize data science resources where decision making is sensitive,” LaBlanc said.
In the HR field, data science is useful for the hiring process, as it can help an HR team identity which potential hires have the most long-term value. For example, a data science team could conduct research on candidates and identify a hiring preference based on the highest profit outcomes. The decisions HR departments wish to make can mean either high or low stakes for the data science work. HR teams have to prioritize data science resources when decision making is sensitive. Using data was before much more expensive, and leveraging the data to make decisions was difficult.
In addition to informing hiring decisions, data analytics can also be used to surface actionable insights from current employees. Through network analysis, it is possible to figure out what people within a company are doing throughout the day, what websites they spend time on, and from that, test ways to increase productivity.
Following the data science presentation, the event convened a panel of experts from the digital HR realm: Alper Tekin from Udacity, Steven Forth from TeamFit, Jessi Roesch from Degreed, and Emercan Dogan and Ryan Zervakos from LinkedIn.
The panelists focused chiefly on the consumer-facing side of data science and data science for HR tracking. There was a comparison made between browser history and an HR profile on employees. For retailers, loyalty cards and accounts track not only what a customer purchases, but also enable data-gathering that can be used to provide tailored recommendations based on search history. HR professionals are now starting to ask if a similar concept could be implemented to understand employee goals and behaviors.
The idea would be to track and record the employee life cycle, including key experiences in the workplace, soft and hard skills, the value of work ethic and productivity in a company, and more. HR teams could use data analytics to look into employees’ track records and improve hiring processes as a result.
Forth, who co-founded TeamFit, works to help individuals and organizations to find the hidden potential of employees, and he sees HR hiring teams encountering certain roadblocks. “One problem with current hiring and recruiting processes is that there are often too many files to make highly selective decisions,” Forth said. “Data scientists could store this information in one place and extract insights. Moreover, those insights can be leveraged to identify institutional biases give HR departments the ability to identify toxic employees ahead of time.”
Overall, this would create a way to extract data from employees and use it for recruitment, retention, and more. This could also provide network analysis, making it easier to identity what individual people do within an organization. Additionally this would enable HR to track what people are spending their time on, and from that information, one can figure out ways to enhance employee experience and by extension productivity in the workplace.
The conversation shifted to skill assessment in the work place, and how this plays into analytics, hiring platforms, and matching people with appropriate roles.
According to Forth, “it is important for people to uncover the full potential of their skills. The social culture is around talking about and sharing skills, but the more difficult problem is the tension between the top down models and models that bubble down from the bottom. The challenge is how to take the skills that bubble up from the bottom if an HR recruiter doesn’t have this information. It will be hard to make the connection, and the skills will not resonate if there is not much data recorded. Corporate HR wants to add order to this structure, this is why it is important to invest in people analytics.”
According to Dogan, “the hope is to see a market change where the source of data given to HR is how people talk about skills.” One example from the skill sets he provided is self-selecting skills on LinkedIn. This process starts with a user putting a skill on their profile, and it is an opportunity to get endorsed by peers. However, the two speakers from LinkedIn felt that there is a problem with normalizing LinkedIn skills.
“LinkedIn was meant to be a type of social platform used to showcase skills and connect with professionals in different fields. Unfortunately, there are a lot of people only utilizing it when they are looking for a job,” said Ryan Zervakos who works in the LinkedIn Learning division of the company. He believes that LinkedIn should prioritize finding talent from within, while providing a digital training record online.
A new form of HR recruiting would ideally be able to connect potential hires to positions that fit their personal skills and individual goals. This system could provide a better user experience and recommendations. HR teams want to know “how can I do a better job at giving employees a better user experience in a way that doesn’t require them to do a lot of work,” according to Roesch.
All of the panelists agreed that HR privacy in recruiting is an important topic. Even though it would be helpful to have a digital space for employee track records, this could be considered an invasion of privacy. The panel ended with some open-ended questions: Who should own this data: the employee or HR? Is it fair or safe for the employee to have this record follow them throughout their career? What if this is detrimental to whether they get a position or not? Participants at this event were asked to consider these questions and think about how the issues raised could affect their careers moving forward.