Innovations in data science are enablingÂ the transformation of HR.
By Jeff Mike, James Guszcza, and Kathi Enderes
Underneath buzzwords like âdisruptionâ and âdigitalÂ transformationâ lie some important truths for HRÂ leadership. There is no denying that powerful technologiesÂ aimed at individual consumers have changed the game.Â The best of these technologies deliver compelling,Â personalized experiences to customers through digitalÂ platforms, smartphones, and increasingly, augmented andÂ virtual reality. As a result, they have created a demand forÂ similar personalization of work experiences and workplaceÂ applications.
These consumer and workforce technologies generateÂ a tidal wave of data. This data is so valuable that theÂ American Bankers Association has started to refer to it asÂ âa currency of the modern economy.â Of course, one ofÂ the first implications of this influx of data is the protectionÂ of it and the privacy of the individuals who generateÂ it. By now, the public debate over data privacy and theÂ implementation of standards like the EUâs General DataÂ Protection Regulation (GDPR) have made data security aÂ priority for HR, IT, and corporate executives around theÂ globe.
All this data raises another question: How can it be usedÂ to generate value? The answer lies in data science. TheÂ standard benchmarking and employee surveys HR hasÂ relied upon for decades help measure HR activity andÂ understand the workforce, but only provide a snapshot ofÂ whatâs going on at a given time. At worst, this approachÂ provides out-of-context, static information for managingÂ the workforce and the business in a dynamic environment.Â Sensing whatâs going on in the workforce in real timeÂ is crucial for change. Fortunately, data science offersÂ the promise of predicting and shaping behavior in thisÂ environment, enhancing the productivity and health ofÂ the workforce while driving key business and financialÂ outcomes.
HR leadership must consistently look for innovationsÂ to realize the potential of data science, with tools thatÂ enable productivity by sensing the external and internalÂ factors that are occurring in their workforce. AccordingÂ to Bersinâs High-Impact Human Resources research,Â the highest-performing HR teams are âpioneering andÂ personalized.â Pioneering indicates that high performersÂ are carving their own paths into the future based on theirÂ organizationâs particular market, strategy, maturity, andÂ culture. Personalized means that high-performing HRÂ is treating the workforce like customersâsensing andÂ segmenting the behaviors and mind-sets of the workforce,Â then designing offerings for maximum personalizationÂ and impact.
A key finding of the research report is that high-impactÂ HR teams are partnering with other business units toÂ understand whatâs taking place, whatâs driving employeeÂ engagement, and which tools and processes are enablingÂ success. For example, HR can partner with IT and financeÂ to create a more consistent employee experience withÂ organizational support functions. Increasingly, these high-impactÂ HR organizations are collaborating with product,Â marketing, customer service, and sales teams to focus theirÂ specialized expertise on the organizationâs talent. In thisÂ way, HR should work closely with data scientists to betterÂ sense, understand, predict, and shape behavior of theÂ workforce to achieve organizational strategies.
Data Science for HR
Data science is awash in its own buzz: Data is the new oil;Â artificial intelligence is the new electricity; and machineÂ learning tools will democratize data science.Â Certainly, the availability of big data and machine learningÂ tools give good reason for enthusiasm. People continuallyÂ leave behind trails of data as they go about their digitallyÂ mediated lives, both on and off the job. All of this dataÂ can be stored and processed more cheaply than everÂ before and used to make all manner of predictions. Not toÂ mention, easily available, user-friendly tools empower dataÂ scientists in ways they could only dream of not long ago. Â All of this makes for a fertile environment for data scienceÂ innovation in HR and beyond.
Powerful HR applications abound. Email and calendarÂ metadata enables HR to go beyond organizational chartsÂ by piecing together network maps to better understandÂ flows of information and collaboration. Wearables andÂ sensors are capable of measuring how well people sleep,Â whether they are getting enough exercise, and their levelsÂ of stress or engagement throughout the day. Social mediaÂ can be scraped for clues about employee engagement.Â Continuous pulse survey and collective intelligence toolsÂ enable HR professionals to crowdsource ideas to betterÂ understand and improve employee engagement. Also,Â algorithms can be built to predict high-performingÂ recruits, match people to jobs, estimate time away forÂ injured workers, and model attrition. These applicationsÂ only scratch the surface of whatâs possible.
The above possibilities reflect conventional discussions ofÂ data science in HR. They focus on polling the workforceÂ and leveraging data sources, analytical tools, and laundryÂ lists of point solutions. This is a good start but it isÂ important not to equate people analytic sophisticationÂ with specific types of quantitative technical sophistication,Â such as information processing or machine learning.
To illustrate, consider the challenge of using data to makeÂ better hiring decisions. It is increasingly practical to trainÂ complex machine learning algorithms on rich datasetsÂ to identify high-performing recruits. While valuable ifÂ done well, this alone does not constitute sophisticatedÂ people analyticsâit merely begins the sensing process.Â This starts with recognizing the pitfalls of relying onÂ unaided judgment about information derived fromÂ unstructured job interviews. Humans have a hard timeÂ weighing together five variables, much less 50 or 500; theyÂ are liable to make different decisions before lunch thanÂ after lunch; and they fall prey to âthinking fastâ decisionÂ traps, such as halo effects, over-generalizing from personalÂ experience, and unconscious bias. Unbiased algorithms canÂ help overcome the limits of human judgment, as long asÂ the data sets used to train them arenât inherently biasedÂ themselves. Training machine learning algorithms onÂ biased datasets and deploying them can therefore amplify,Â rather than mitigate, the cognitive biases.
An alternate way is to use data analytics to select jobÂ interview questions and to apply behavioral designÂ principles to de-bias hiring environments. Such data-drivenÂ and psychologically informed interventions help takeÂ some of the human biases out of hiring. Tapping into theÂ wisdom of crowds by having multiple interviewers followÂ the same structured procedure eliminates some of theÂ noise from the process. For example, the noise createdÂ by one interviewerâs bad mood will tend to get averagedÂ away by the uncorrelated judgments of the otherÂ interviewers. Not surprisingly, Bersinâs High Impact PeopleÂ Analytics study reports that organizations that use peopleÂ insights well are 10 times more likely to see a significantÂ positive outcome from that data on hiring.
This specific use case of data-driven hiring illustrates theÂ more general point that people analytics goes beyondÂ the routine application of machine learning techniques. ItÂ should be conceived broadly to incorporate methods fromÂ data science, the psychological and behavioral sciences,Â and the use of experiments to figure out what works.Â People analytics is ultimately about adopting a culture ofÂ evidence-based decision making and using the scientificÂ method to enable better people-related decisionsÂ to design personalized work environments in whichÂ employees can perform at their best.
Beyond individual people data, HR can benefit fromÂ data science on the workforce level. Automation andÂ smart machines will change the way work gets done,Â augmenting humans with machines and fundamentallyÂ redefining work. Without data and insights, designing theÂ workforce of the future is a guess at best and misleadingÂ at worst. With data science, HR leaders can forecastÂ complex scenarios that consider automation possibilities,Â a broader workforce continuum beyond on-balance sheetÂ employees, and a skills pool potentially unencumberedÂ by geography. This can provide needed direction forÂ accessing evolving and needed skills, countering the (muchÂ lamented) skills gap through proactive actions rangingÂ from traditional development (âreskilling the workforceâ),Â partnership with academia, joint ventures, mergers andÂ acquisitions, and even crowd work. The future of work isÂ full of thoughtful, integrated solutions that make the bestÂ of available technology tools.
A Talent Mandate
Exponential change, powerful technologies, and emergingÂ skillsets have created a talent mandate for HR leadership.Â Never before has human talent been such a differentiatorÂ and so critical to an organizationâs market success, andÂ never before has the right talent been so hard to findÂ and keep. In this talent-constrained world, designingÂ productive and compelling work and experiences for allÂ workers has become a business necessity. In fact, accordingÂ to Bersin, high-impact HR organizations are 3.5 timesÂ more likely to focus relentlessly on creating an engagingÂ workforce experience when designing HR offerings as low-performingÂ organizations.
HR leadership needs a new mind-set and new tools to takeÂ on these challenges and to thrive in a new paradigm forÂ managing enterprise talent. Without targeted insights onÂ peopleâs needs, expectations, performance, and skill sets,Â the tsunami of data available today remains untapped. ToÂ derive actionable insights and shape productive, healthyÂ behaviors, all this data needs to be integrated, analyzed,Â visualized, and summarized.
Encouraging leadership to obtain a better sense of theÂ workforce and the factors affecting it is a first step.Â Sensing is also key to helping reduce noise from theseÂ sources and unlock insights for creating business impactÂ through people. The most efficient and effective wayÂ to build this capability in HR is to tear down the silosÂ and work closely with data scientists and the rest of theÂ business to understand and define the future of theÂ enterprise, the future of the workforce, and the future ofÂ how work gets done.
Jeff Mike, EdD, is vice president and head of research ideation andÂ Kathi Enderes, PhD, is vice president and talent and workforceÂ research leader for Bersinâ¢, Deloitte LLP. James Guszcza, PhD, isÂ chief U.S. data scientist at Deloitte Consulting LLP.