The rapidly maturing field of talent analytics can provide valuable insights into the hiring process, helping businesses optimise their recruitment strategies and streamline their operations. However, an important issue commonly overlooked is the significant loss of accuracy experienced after the first day of hiring – the unexpected post-hire blind spot.
According to a recent study by Gartner, approximately 80% of the information collected during the recruitment phase receives no further use post-hire, highlighting a striking blind spot in talent analytics. This discrepancy is a clear indication of the disconnect that exists between recruitment and talent management processes.
This underutilisation of collected data leads to a drop in analytics accuracy after the first day of employment, rendering a considerable chunk of the gathered information virtually redundant. Multiple online discussions and expert opinions have highlighted this issue, promoting enhanced scrutiny and suggesting potential solutions to mitigate this inefficiency.
Why the Initial Accuracy Decline?
To better understand this phenomenon, it’s important to delve into the mechanics of the talent acquisition process. The bulk of the data collected during this process is primarily used to match candidates’ skill sets with job roles, predict their potential performance, and determine their compatibility with the organisation’s culture.
Once a candidate is hired, this wealth of information usually gets archived, while the focus shifts to rudimentary data such as attendance records, performance appraisals, or training completion. This post-hire shift in data emphasis potentially undermines the accuracy and richness of pre-hire analytics, leading to a less comprehensive understanding of employee capabilities.
Moreover, there is a certain level of inconsistency in terms of the nature of the data collected pre and post-hire. For instance, personality assessment data collected pre-hire may not align with performance reviews or 360-degree feedback data collected later. This inconsistency can contribute to the loss of accuracy.
The Challenges of Post-Hire Data Collection
The logistics of collecting post-hire data present another challenge. Consistently collecting accurate, insightful, and valuable data requires an ongoing relationship between employers and employees. According to a recent Learning and Development report, only 24% of employees feel actively engaged with their employers about their development, demonstrating that significant work is needed to improve this relationship.
The Future of Post-Hire Talent Analytics
Understanding the blind spot does not imply a dead-end but instead presents an opportunity for companies to rethink their analytics strategy. To improve accuracy, companies need to create a bridge between pre-hire and post-hire data, treating it as a continuous cycle rather than discrete entities.
Some industry analysts suggest integrating talent analytics throughout the employment lifecycle, fostering a continuous feedback loop. Frequent skill assessments, for instance, could provide a regular, real-time view of employees’ progress and could be cross-referenced with pre-hire data for a more holistic picture.
Moreover, adopting technologies like artificial intelligence (AI) and machine learning could further refine talent analytics. These technologies can predict employee behaviours, needs, and goals, enabling companies to better tailor their talent management strategies.
In conclusion, the post-hire blind spot is undeniably a tough nut to crack. However, with a reimagined approach to talent analytics that integrates the entire employment lifecycle, alongside leveraging advanced technologies, companies can strive towards closing the gap between pre-hire and post-hire data, ultimately improving the accuracy of their talent analytics.
Original Source: https://hrexecutive.com/why-talent-analytics-lose-accuracy-after-day-one-the-post-hire-blind-spot/
