Every kind of organization has specific key metrics that it uses to track performance. Education organizations track dropout rates, attendance, and the student/teacher ratio. Emergency service companies measure metrics like response time, percentage of false alarms, etc. In addition to industry-specific key metrics, individual departments within an organization track metrics specific to their managerial goals. A marketing department tracks KPI's such as conversion rates, value, and customer profitability. Supply chain managers often track inventory turnover, average order lead time, and out-of-stock performance. Metrics tracked by HR often include average training time, employee turnover, and average training cost per employee.
The above education dashboard was created using Style Intelligence, InetSoft's flagship BI application. The most crucial metrics are total applications, admission rate, and total enrollment. These key metrics are displayed visually as large highlighted text. The small comparison KPIs right below make it easy to compare these key metrics against benchmarks, in this case, figures for the previous years. The other sections of the dashboard go into further detail on these key metrics. Different student distributions are shown as bar charts broken down into individual years. An administrator could also use Style Intelligence's drill-down capabilities to view details and slice-and-dice.
Traditionally, key metrics are defined out of business experience by humans. The data-based definition of key metrics has become feasible with advances in machine learning. For example, it might not be apparent what key factors are affecting employee turnover. Traditionally human-defined key metrics such as job satisfaction surveys and compensation parity alone are not sufficient to explain retention performance. Machine learning can use data to find addtional key metrics that HR should analyze and monitor. Would you like to start a project to explore this?
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