Type
Recruitment Analytics and Reporting Tool
Objective
A Sweden-based major HRtech company sought the creation of an analytics and reporting tool to help streamline recruitment processes, optimize quality, and enhance
efficiency in the selection of candidates. This would include gaining insight into recruitment data, the establishment of key metrics, and the strengthening of
decision-making ability for future hiring strategies. It was primarily done for the sake of increased transparency, tracking of performance, and making data-driven decisions for hiring.
Technologies
Java, Ruby on Rails (RoR), Big Data, Apache Spark, Tableau
Country
Sweden
Project Attributes
Engagement Model
Fixed Cost
Duration
7 months
App Users
Recruiters, Hiring Managers, and Executives
Challenges
- Data Overload: The client had a lot of recruitment data but couldn’t draw any
meaningful inferences because of the amount and complexity of the data. - Lack of Real-Time Insights: Decisions were determined and made by hiring
managers solely based on old data in the absence of a live feed, hence leading
to no agility and response at all. - Poor Prediction accuracy: Analytical instruments which were put in place
lacked support for predictive hire patterns, hence making it a really hard task to
see whom to hire or staffing levels that are likely going to be needed soon
enough. - Manual reporting: In many cases, most of the reports were still being run
through manually, which thus required a lot of time with errors in the report
translation, hence impacting quality levels in strategic planning.
Solutions
- Big Data Analytics with Apache Spark: In Big Data and Apache Spark, the
team could quickly process large amounts of data, enabling real-time collection
and analytics. - Tableau Customized Reporting Dashboards: The team built reporting
dashboards in Tableau for hiring managers and recruiters so that they could
glance over their performance metrics: sources, time-to-hire, and success rates,
amongst others. - Predictive Hiring Models: Predictive models using historical hiring data would
provide the client with advance hire trends and enable the proactive
decision-making process. - Automated Reporting and Generation of Insights: The automation features of
reporting ensured the handling of data to minimal extents, reduced errors in
rates, and provided the output of data at uniformed levels for strategic insights of
hiring.
Results:
- Improved Data Insights: Data insights were enhanced with respect to the hiring pattern and trends for the recruitment process in this case.
- Real-time performance tracking: Real-time insights into the recruitment funnel allowed quicker responses and adjustments, thus improving the hiring process to a large extent.
- Enhanced Predictive Accuracy: Predictive hiring models increased hiring accuracy. This was due to the fact that the models helped recruiters spot potential candidates early on and cut down the time-to-hire.
- Increased Operational Efficiency: Automated reporting reduced manual data handling. Thus, the HR teams could concentrate on high-value tasks and strategic initiatives.
Conclusion:
The Recruitment Analytics and Reporting Tool transformed the hiring process in the client’s organization-from being data-less, haphazard, and time-consuming to data-driven and efficient, making them prepared for the future and recruiting leaders in innovation.