People Analytics: Where is HR Heading?

HR departments have long been the backbone of everyday operations in the workplace, managing everything from recruitment and training to employee wellbeing and performance management. However, in a world where data is the new oil, the role of HR is evolving like Moore's Law. The growing integration of people analytics into HR practices promises to revolutionise HR by leveraging data to drive insight-driven decision-making. This transformation is not without its challenges though. The current state of siloed data sources, the continual rise of generative AI, the necessity to aggregate and integrate data for deeper insights, and the every-growing demand for advanced data skills and data scientists all play pivotal roles in shaping the future of HR.

The disparate data challenge

One of the most significant obstacles facing HR today are our fragmented and disparate data sources. In many organisations, employee data is scattered across various systems, some often legacy, each serving a different need. Having these silos often hinders the ability to get a holistic view of the workforce, making it difficult to plan, identify trends, predict needs, and the make data-driven informed decisions that we so desire. For instance, HR core data may reside an HRIS or HCM platform, learning and performance in another, and service management in yet another, each with its own quality and management issues.

Data-rich, but insight poor

The rise of generative AI introduces both opportunities and challenges in this space. While generative AI can transform static data into visualisations and incredible insights, it requires high-quality, integrated data to function effectively, not to mention contextual commands from humans to truly make it accurate. Many HR departments find themselves data-rich but insight-poor, massively overwhelmed by the sheer volume of information without the means to analyse it effectively. Poor data quality, often a result of inconsistent data management, outdated information, and lack of standardisation, further exacerbates this issue.

Data science, not people science

Complicating these challenges is the growing need for data skills within HR itself. The ability to interpret complex data sets and draw actionable insights requires more than just basic data literacy, it demands expertise in data science, technology, and analytics, which is not necessarily a natural skill found within HR professionals. This skill gap highlights the importance of integrating data scientists into HR teams to ensure that the potential of people analytics is fully realised.

So, how can HR departments overcome these challenges to harness the full potential of people analytics? What steps are necessary to move from a fragmented, immature, and data-overloaded environment to one where data-driven insights drive strategic decisions and unlock new opportunities?

Eliminate disparity to maximise consistency

The journey towards effective people analytics in HR starts with addressing the fragmentation of data sources. Organisations need to invest into integrating their disparate HR systems to create a truly unified ecosystem, rather than a collective of siloed systems of record. These integrations should enable a comprehensive view of workforce data, allowing HR to analyse and interpret information layered over various dimensions. By consolidating data across the employee lifecycle into a single source of truth, we can eliminate data disparity and maximise consistency. Tools like Snowflake for example can consolidate and integrate data from numerous disparate HR systems into a unified repository, eliminating silos and enabling comprehensive data analysis. They can support various data types and sources, creating real-time, high-quality insights through advanced analytics through Al and ML. Snowflake enables real-time data processing, secure data sharing, and seamless integration with BI and analytics tools.

Connect our peers

Better yet, tools like Snowflake can be leveraged for cross-departmental data integration to enhance HR analytics by combining data from key internal partners like finance, property, procurement, and IT. This underutilised opportunity could enable deeper and richer analysis of people costs, engagement, behaviours, and productivity. By integrating financial and HR data in particular, organisations can better assess true employment costs and identify optimisation opportunities. Combining IT and property data on the other hand can help with better understanding employee engagement and workspace utilisation, while procurement data helps with understanding workforce agility needs through contingent workforce management. This more holistic data approach further enhances both tactical and strategic decision-making, whilst improving predictive analytics, workforce planning and organisational performance.

Gen-AI plays another hand

Generative AI can play a crucial role in this transformation. Unlike traditional data analytics, which often relies on static reports and historical data, generative AI can dynamically analyse data to provide real-time insights. For example, generative AI can sift through vast amounts of employee data to identify patterns and correlations that might not be immediately apparent. It can predict employee turnover, identify skill gaps, and even suggest personalised career development plans based on individual employee profiles.

However, the effectiveness of generative AI depends on the quality of the data it analyses. This is where the challenge of data quality comes into play. Organisations must implement robust data governance frameworks to ensure that data is accurate, up-to-date, and standardised. Regular audits, data cleansing processes, and employee training on proper data entry practices are essential steps in maintaining high data quality. Additionally, leveraging advanced data management tools can help automate many of these processes, reducing the burden on HR teams.

Once data quality is ensured, the integration and aggregation of data can unlock deeper insights and greater value. By combining data from multiple sources, organisations can gain a more complex and advanced understanding of their workforce. This integration can then facilitate the personalisation of employee experiences by tailoring learning and development, benefits, and career paths to individual needs and aspirations. Sentiment analysis tools can also assess employee feedback and emotions, enhancing change management efforts by identifying and addressing concerns before they escalate, maximising change adoption.

Management and leadership development

Enhanced data management and analytics also offer significant opportunities for improving line management capabilities and leadership. By providing managers with data-driven insights into team performance, skills, and potential, organisations can leverage more effective and informed leadership. These insights can drive cost savings and cost avoidance by optimising resource management, reducing attrition, and enhancing engagement and productivity.

Data literacy is a must

To fully realise the potential of people analytics, HR must also address the aforementioned data skills gap. The future of HR will increasingly depend on the integration of data scientists and analysts who can navigate complex data sets to deliver meaningful insights. Investing in data literacy training for existing HR staff through things like apprenticeship schemes, whilst hiring data science experts could be crucial steps towards this evolution.

Data ecosystem, not a data culture

Despite the challenges, the move towards integrated, insight-driven people analytics offers significant opportunities. By integrating disparate systems and through leveraging the power of generative AI, machine learning, and automation, HR can finally transition away from reactive and towards proactive decision-making. This shift then enables organisations to not only address current workforce challenges, but also anticipate and predict future needs and trends beyond just workforce management. Ultimately, the goal should be to create a data-driven ecosystem and not just a culture, where decisions are routinely based on facts and actionable insights, rather than gut feelings or anecdotal evidence.

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