For years now organisations have been grappling with the power of big data. And the humans who can effectively build and deploy data-driven models have become some of the most highly-desired employees in the global labour market. Demand for data scientists has doubled across several industries in 2020 and it has been a challenge for recruiters to fill their roles.
When it comes to HR data, the challenge is no different. Whether or not the technical label of ‘big data’ applies to the data collected through HR processes, there’s no doubt that HR departments are gathering more data than ever before and have access to metrics on employee engagement, turnover, performance, salary, capabilities and, in more mature departments, success profiles. The million dollar question is: what value is this data generating?
The uncomfortable truth is that many organisations don’t know the answer to this question. But as critical business decisions increasingly revolve around finding top talent, developing a healthy corporate culture, and focusing on various people-related issues across the talent life cycle, organisations can no longer stick their heads in the sand.
Yet relying on data to make HR decisions remains uncharted territory for many organisations. Most don’t have the expertise to manage and analyse the data they’re collecting; its value lays dormant because there’s no one to bridge the gap between data and insight.
Enter, the HR data scientist. Here we discuss why this role is so important and how to create the conditions to attract and retain the best talent.
The value of data-driven HR
One of HR’s greatest challenges has always been the ability of HR leaders to demonstrate return on investment in real terms. Adopting a data-driven HR model helps overcome this challenge, rightfully positioning HR as a strategic business unit.
A dedicated HR data scientist can help realise this value through holistically managing and analysing the wealth of data gathered throughout the employee lifecycle. Their expertise in systems thinking can build a comprehensive understanding of how the myriad of people-related factors combine to create success or failure in the organisation. An HR data scientist has the skills to uncover these links, backed by data, a task that has often eluded HR in the past.
One example is recruitment. A data scientist can reverse engineer what makes an organisation’s best employees successful by putting a sharper filter on how skills, experiences and personality traits interact with the organisation’s culture and goals. These models feed into workforce planning to identify skills gaps and recruitment of new talent. Well-designed systems can also help to reduce or eliminate bias, opening up the talent pool and contributing to diversity and inclusiveness.
This is just the beginning. Reward systems are another example, while the efficiency and effectiveness of HR delivery systems can also be interrogated and optimised to better integrate AI and automation to deliver on those goals in real time. Employee retention data, which is a goldmine for HR, is also ripe for analysis. Having predictive employee retention models and subsequent proactive interventions can reduce attrition of good talent.
Attracting and retaining the right talent
There’s no doubt the market for data scientists is hot; it’s a big challenge to not only find but hang on to the best talent.
When it comes to recruitment, candidates – particularly those who have both business acumen to derive actionable insights from data and leadership skills that go beyond the technical – are in high demand. But this doesn’t mean any available talent is the right talent. The best data scientists will not only bring technical and functional skillsets, but also an entrepreneurial mindset and the capacity to lead and influence, not only the members of their direct team but also globally throughout the organisation.
The competition for candidates doesn’t end once they’re on the payroll. Other employers are actively scoping talent and the individuals themselves are always on the lookout; the Financial Times reported that most individuals in this field spend 1-2 hours a week looking for a new role.
Organisations will have to take steps to counteract these market forces through a variety of measures that go well beyond the expected competitive salary.
- Supportive culture: Organisations need to demonstrate their commitment to creating a data-driven culture at the top levels and educating and upskilling business leaders and employees to become stewards of data. One sign of a supportive culture is the willingness to invest in the latest technology to enable data teams to work more effectively.
- Data-smart strategy: Data scientists want to work for organisations with a clear strategy around, and budget for, data initiatives. This may also require some honesty about the current level of data sophistication within the organisation to ensure candidates are given a true picture of the role they’re considering.
- Purpose and meaningful work: High salaries are one thing, but like most people, data scientists want to see their work as contributing to a wider purpose they can align to. They value opportunities to let loose their curiosity, to innovate and to see how their work directly benefits the company.
- Development and career advancement: Organisations need to lay out a clear path for career progression for data scientists – but it shouldn’t necessarily look like the proverbial career ladder. Skill-building and conferences are important, but opportunities to collaborate across business units or to work on projects outside of their business-as-usual tasks also offer meaningful learning opportunities that can enrich their experience and expand their understanding of the organisation and the challenges it’s trying to meet.
For a long time HR hasn’t been speaking the same language as other business units. But investing in an HR data scientist role can bring new fluency to the conversation and begin realising the potential of a data-driven approach to HR.