Anshul Mahesh

  Abstract This paper proposes a transformative approach to employee performance reviews by using mathematically defined metrics that go beyond basic time-to-completion or task quantity. The goal is to promote personal growth for employees, enabling them to better understand and enhance their capabilities while simultaneously aligning their efforts with the organization's goals. By measuring attributes like collaboration, complexity, versatility, competency, and value creation, this model fosters a more value-driven and adaptable workforce. Traditional methods of employee performance evaluation are often limited to surface-level metrics such as time to task completion or number of tasks accomplished. While these measures offer some insight, they fail to capture the multifaceted nature of true employee performance, which includes collaboration, versatility, competency, and adaptability. This paper introduces a revolutionary performance evaluation model that quantifies deeper emp...

  Abstract Data observability solutions are traditionally built to monitor backend systems, ensuring compliance, performance, and data integrity within a predefined tech stack. However, these solutions overlook the critical aspect of data transfer and duplication at the front end, where data travels between user nodes and endpoints. Node Sanitation Copilot (NSC) leverages task mining and remote capture technologies to provide real-time, UI-layer visibility into data classification, redundancy management, and access control, making it a pioneering solution in front-end data observability. This paper discusses the architecture of NSC, contrasts its functionality with traditional backend-focused observability tools, and explores novel applications where NSC can offer superior infrastructure support. 1. Introduction As digital data grows exponentially within organizations, tracking its journey from creation to deletion has become more complex. Data observability tools have evolve...