The illustration below summarizes how AI will solve data visualization problems in the future.
Evolution of Data Monitoring with AI/ML
How can data monitoring evolve in this era of AI/ML? I predict that the number of people who have to look at a dashboard to monitor data will slowly diminish over time. It is inefficient for humans to indonesia whatsapp number data constantly monitor data on a dashboard to take corrective actions when AI can continuously monitor the data and alert users when human intervention is needed or take corrective actions without human interference.
Dashboards have inherent limitations in visualizing data for monitoring. Because of their limited real estate, a visual dashboard usually only allows for the visualization of five to six metrics, while the amount of data to be monitored rapidly increases. Relying on humans to look at static-layout dashboards to monitor data will become unfeasible in this age of big data.
A recent example of using AI/ML for monitoring is in IT operations. The system performance of IT apps, which was once visually monitored via dashboards, is now systematically monitored with intelligent data observability tools. These tools not only proactively detect anomalies but can also perform root-cause analysis.
“Analyst” is a widespread job title in many organizations. Large organizations have numerous data analysts who analyze data, develop insights, and communicate the insights to the right stakeholders.