The analysis of workforce data is key in allowing organisations to understand various aspects of their operations, says Libby Duane Adams.
But, while the output of that analysis can be used to identify patterns in employee retention and predict future needs, successfully optimising areas such as recruitment and training depends on understanding the reasons behind those patterns – the “why” behind the “what”.
The data-driven insights derived from this understanding can contribute to an organisation’s decision intelligence and its overall value proposition. It’s vital, therefore, to provide human resources (HR) professionals with the know-how and data literacy capabilities that will enable them to capitalise on this human capital data.
Essential analysis
The volume of data being generated and used today continues to grow. According to the European Commission (EC), it is expected to reach 175 zettabytes by 2025 – an increase of 430 per cent on the 38 zettabytes generated in 2018. This huge surge in data presents every area of a business with a wealth of new opportunities to deliver decision intelligence at scale. With more well-informed decisions, HR departments, for instance, can develop targeted retention strategies, and proactively address factors that influence employee turnover. But many organisations can find it challenging to deliver the necessary insights at the speed and scale needed to transform their decision-making.
 |
Get our essential weekday HR news and updates.
|
Analytics are, therefore, essential to all lines of business. The analysis of workforce data allows HR professionals to identify patterns and predict future needs, enabling them to align recruitment and retention strategies to wider organisational goals, and ultimately improving productivity and their organisation’s competitive advantage. But, without fully comprehensive analysis, the value of all that data can be lost, with HR professionals unable to access the insights they require to inform their decision-making.
Historically, heuristics or past experiences might have been used to explain variations in performance rates, with analysts considering common variables that could affect performance. Such an approach doesn’t take into account the potential effect of less significant factors such as emerging trends, however. Today, though, the use of accessible analytics infused with integrated generative AI provides the detailed data-driven insights an HR team needs.
Empowering domain experts
The decision intelligence required to solve an HR department’s challenges doesn’t necessarily lie with those who possess advanced coding skills, but rather the domain experts, who can often be an untapped resource. Accessible, easy-to-use analytics technology can help bridge the gap between those domain experts and data analysis, a role traditionally conducted by data scientists and engineers. Regardless of their technical experience, it enables HR professionals to extract data-driven insights from human capital analytics, allowing them to unlock valuable intelligence on areas such as attrition and retention rates.
By drawing on the huge volume of data available to them, a whole tier of HR domain experts can better understand the “why” behind the “what” and optimise areas of human capital operations and procedures without the need to become data specialists. Effectively combining their hard-earned domain expertise with code-friendly or even code-free self-service technology, they can easily address their data problems because they have the context of the questions being asked.
But the success of this approach requires an intuitive, easy-to-use solution, with which those domain experts can automate the process of pattern identification and future needs prediction. What’s more, by providing an explanation for trends such as why attrition or retention rates are trending up or down, AI-driven, automated data analytics can enable HR teams – and the wider business – to make more informed decisions, empowering them to proactively address those factors which influence employee turnover, or develop more targeted retention strategies.
Speed and scale
HR professionals were once required to place their requests for data analysis into a queue for IT teams or data scientists to address. This was, understandably, frustrating, as it meant they were unable to gain the timely insights they need to improve business outcomes. Today, though, the ability to automate the analysis of their data and the generation of insights means HR professionals can make data-driven decisions in real time.
Delivering value from the vast amount of data available to organisations today requires its analysis to be performed at the speed and scale of business. However, this depends on the ability of domain experts, like HR professionals, to harness analytics effectively and accelerate their organisation’s decision intelligence. Business and tech leaders must, therefore, prepare for the future now by making the AI-powered analytics tools available that will enable HR departments to take full advantage of this data-powered intelligence and transform how they understand and address talent management.
By enabling HR teams to see the “why” behind the “what” of human capital data, accessible, AI-powered analytics can help them make data-driven decisions that deliver on their own objectives and on those of the business as a whole.
__
Libby Duane Adams is Chief Advocacy Officer and Co-Founder at Alteryx.
Amelia Brand is the Editor for HRreview, and host of the HR in Review podcast series. With a Master’s degree in Legal and Political Theory, her particular interests within HR include employment law, DE&I, and wellbeing within the workplace. Prior to working with HRreview, Amelia was Sub-Editor of a magazine, and Editor of the Environmental Justice Project at University College London, writing and overseeing articles into UCL’s weekly newsletter. Her previous academic work has focused on philosophy, politics and law, with a special focus on how artificial intelligence will feature in the future.
- Amelia Brand
- Amelia Brand
- Amelia Brand
- Amelia Brand
