JBON_DATA

AUTOMATION

Building Effective Automation Dashboards

Automation without visibility is automation you can't trust. Dashboards provide the real-time and historical views needed to ensure your automated processes are working as expected.

Dashboard Hierarchy

Design dashboards for different audiences:

  • Executive: High-level KPIs, ROI, trend lines
  • Operations: Current status, queue depths, alerts
  • Technical: Error details, system health, logs

Essential Metrics

Process Health

  • Success/failure rates
  • Process execution time trends
  • Exception frequency by type
  • Queue backlog trends

Business Value

  • Transactions processed
  • Time saved vs manual
  • Cost per transaction
  • SLA compliance

Alerting Strategy

Good alerts are actionable and minimal:

# Alert thresholds (example config)
alerts:
  - name: process_failure_rate
    condition: error_rate > 0.05
    window: 15m
    severity: critical
    
  - name: queue_backlog
    condition: pending_items > 1000
    window: 5m
    severity: warning
    
  - name: execution_time
    condition: avg_duration > baseline * 1.5
    window: 30m
    severity: warning

Tool Stack

Popular combinations:

  • Grafana + Prometheus: Infrastructure and metrics
  • Elastic Stack: Log aggregation and search
  • Custom dashboards: Streamlit, Plotly Dash
  • BI tools: Power BI, Tableau for business users

Simple Streamlit Dashboard

import streamlit as st
import pandas as pd
import plotly.express as px

st.title("Automation Health Dashboard")

# Load metrics
df = load_metrics()

# Key metrics in columns
col1, col2, col3 = st.columns(3)
col1.metric("Success Rate", f"{df['success_rate'].iloc[-1]:.1%}")
col2.metric("Avg Duration", f"{df['avg_duration'].iloc[-1]:.1f}s")
col3.metric("Queue Depth", df['queue_depth'].iloc[-1])

# Trend chart
fig = px.line(df, x='timestamp', y='transactions', 
              title='Transactions Processed')
st.plotly_chart(fig)

# Recent errors table
st.subheader("Recent Errors")
st.dataframe(df[df['status'] == 'error'].tail(10))

Design Principles

  1. Real-time where needed: Not everything needs live updates
  2. Context is key: Show historical baselines
  3. Drill-down capability: Summary to detail
  4. Mobile-friendly: Alerts need to work on phones
  5. Clear ownership: Who acts on what?

A dashboard that nobody looks at provides no value. Focus on actionable information for specific audiences.

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