[This post was originally published in the Straits Times on 27 Jul 2016. It has been edited for use on this blog.]
The rise of big data and predictive analytics in human capital management has opened the floodgates to tools and processes to add scientific rigour to people management, transforming human resource practice
Factory workers at the Western Electric Company, based just outside 1920s Chicago, were found to be more motivated and productive because of social factors than monetary incentives and good working conditions.
These factors included choosing their own co-workers, working as a group, being treated as ‘special’, and having a sympathetic supervisor.
Fast forward almost 100 years after the Hawthorne Studies, as the experiments above were known, the art of managing people is making another significant leap towards becoming a science.
The rise of big data and predictive analytics in human capital management has opened the floodgates on tools and processes to add scientific rigour to people management.
With the backdrop of tectonic shifts in the nature of work as well as increased volatility in the business landscape, the era of People Analytics, or Human Resource (HR) Analytics, has arrived. Whether we like it or not.
Adopting People Analytics
A 2015 study conducted by PricewaterhouseCoopers (PwC), a consultancy, found that 86% of business leaders thought that creating or maturing their people analytics function over the next one to three years was a strategic priority, but only 46% of them had such a dedicated function. This gap is forecasted to close quickly in the coming years.
There are three fundamental enablers for closing this gap as pointed out in the Harvard Business Review.
Velocity: The first enabler is the velocity of data. In the time taken for you to read this sentence, more data has passed through the internet than all the data stored on the internet less than just 20 years ago.
This speed has already become a baseline expectation of business functions. More than half of the leaders studied by PwC demanded data on a business event within an hour of occurrence. In light of this, the annual cycle of employee engagement surveys starts to look extremely antiquated, and other tools are emerging to deliver high frequency feedback for leaders to calibrate their management style in real-time.
Volume: The second enabler is the volume of data that is being transferred. Right now the cost of data storage and processing has been falling exponentially: Wikibon, a technology and business systems community, estimates that the cost of flash storage will fall 98% over the period from 2015 to 2020.
In tandem, the volume of data that is being created every day is 5 billion gigabytes and growing. These trends make it easier than ever before to warehouse huge repositories of people data, which holds the potential of yielding never-before-considered insight on managing people and teams.
Variety: The last major enabler is the variety of the data collected. The multiple sources of data allow companies to mitigate the omitted variable bias in using traditional HR data. GPS data from mobile phones, near-field communication chips in employee ID tags, facial recognition, social media sentiment tracking, information from wearables, and other data sources have made up what Kevin Ashton has coined the “Internet of Things” at the workplace.
Deloitte, an accounting firm, has experimented with a wearable gadget that tracks the movement and conversations of every employee with consent, to draw conclusions about the behaviour of their best performers, granular understanding of productivity, and speech patterns of the best managers.
Evolving Nature of Work
The evolving nature of HR technology operates a feedback loop for analytics to inform and influence behaviour. Josh Bersin, a thought leader in the people analytics space, identified that HR applications have become ‘consumerised’, functioning as tools for employees first, not HR.
These enable them to better manage others, learn and develop, and steer their own careers. Microsoft’s recent US$26bn acquisition of LinkedIn is the most recent data point affirming this trend.
With more than 2.1 billion smartphone users on the planet spending half of their 5.6 hours per day on the internet accessing content on those phones, mobile technology has become ubiquitous as the primary technology interface we use.
The Sidekick system developed by the Commonwealth Bank of Australia functions as a mobile HR Management System, and has been downloaded by more than 10,000 employees. Within a year, it has reportedly reduced pay slip requests by 46%, and time-approval requests from hourly workers by 35%. A by-product of this is powerful new information for the HR team to help make employees’ work lives better.
Meanwhile, even the concept of the ‘employee’ has been evolving. Uber’s legal battle in California heralded the need to redefine what it means to work for a company. Companies such as Upwork, Fiverr, 99Designs, Eden McCallum and locally, Grab, are blurring the classification of talent as a corporate asset.
Moving further down this path may make the ability to build a supply chain of ‘just-in-time’ talent just as important as developing talent organically within a company. Crunchbase, a funding platform, found that venture-capital investment in the ‘on-demand economy’ rose from less than half a billion dollars to 30 companies in 2010 to more than US$1.5bn to 117 companies by 2013.
None of these disruptive technologies could have adequately prepared companies for a ‘black swan’ type Brexit, nor the complex risk of a Trump presidency. Prof Richard Foster at Yale University shows that the average lifespan of an S&P500 company in the 1920s was 67 years, while in 2012 it was only 15 years. It may be that companies need vastly superior analytics just to cope with increased business volatility.
Standing in the way are still two major impediments to the immediate adoption of people analytics in companies.
First, data silos and incompatible information management systems make meaningful analysis intractable in some cases. This is such a massive problem that the market for enterprise resource planning software (which promises to maintain common standards for data collection and analysis across an organisation) is expected to reach $41.69 billion by 2020, registering an annual growth rate of 7.2% from 2014-2020.
Second, the analytical capabilities of HR professionals are found lacking – while 63% of Chief HR Officers surveyed reported using data to provide decision-support insight, only 20% of non-HR leaders agreed.
It will not be long before these tides of change wash onto our shores. SkillsFuture policies may help to make analytical know-how more liquid, increasing the overall stock of data science expertise within HR functions. Rapidly evolving enterprise software and numerous integrations programmed with an ecosystem of mobile apps may provide the silver bullet that resolves data silos and broadens analytical possibilities.
The final, and perhaps trickiest, change needed lies in the minds of business leaders and HR professionals. By embracing the full potential of people analytics, it is possible that strategic human capital management can finally be unlocked.
Download our eBook on The Age of People Analytics below:
Author: Chee Tung
CheeTung is the CEO of EngageRocket, an HR tech startup that analyses employee feedback in real-time to advise you on how to build a better culture, one team at a time.