K
Klivvr Blog

Data Visualization Patterns for Ops Dashboards

How to choose and implement effective data visualizations for operations dashboards, covering chart selection, color systems, responsive layouts, and accessibility.

technical5 min readBy Klivvr Engineering
Share:

Operations dashboards exist to make complex system state understandable at a glance. The right visualization makes a pattern obvious; the wrong one hides it. Choosing the appropriate chart type, color scheme, and layout for each metric is a design decision that directly affects how quickly the team can detect and respond to issues.

This article covers the data visualization patterns used in Klivvr's Web Ops Console.

Chart Selection Guide

Different data types demand different visualizations. The Web Ops Console uses a consistent mapping from data characteristics to chart types.

Time-series metrics (request rate, latency, error rate over time) use line charts. Line charts reveal trends, spikes, and anomalies in temporal data. They are the default for any metric that changes over time.

Current status (service health, resource utilization) uses gauge charts or status indicators. These show a single value against a threshold, making it immediately clear whether a metric is in a healthy, warning, or critical range.

Distribution data (latency percentiles, response time buckets) uses histogram or bar charts. Distributions reveal patterns that averages hide — a bimodal latency distribution suggests two different code paths, which a single average would mask.

Proportional data (traffic by endpoint, errors by type) uses stacked bar charts or donut charts. These show how a total is divided among categories.

Comparison data (service A vs. service B performance) uses grouped bar charts or overlaid line charts. Side-by-side comparison makes differences immediately visible.

function ChartFactory({ type, data, options }: {
  type: "line" | "bar" | "gauge" | "histogram" | "donut";
  data: unknown[];
  options: ChartOptions;
}) {
  switch (type) {
    case "line":
      return <TimeSeriesChart data={data} {...options} />;
    case "bar":
      return <BarChart data={data} {...options} />;
    case "gauge":
      return <GaugeChart value={data[0] as number} {...options} />;
    case "histogram":
      return <HistogramChart data={data} {...options} />;
    case "donut":
      return <DonutChart data={data} {...options} />;
  }
}
 
interface ChartOptions {
  height?: number;
  colors?: string[];
  thresholds?: Array<{ value: number; color: string; label: string }>;
  xLabel?: string;
  yLabel?: string;
  animate?: boolean;
}

Color System for Operational Status

Colors in ops dashboards carry semantic meaning. A consistent color system ensures that every team member interprets visualizations the same way.

const opsColors = {
  // Status colors
  healthy: "#22c55e",     // Green — everything is fine
  warning: "#f59e0b",     // Amber — attention needed
  critical: "#ef4444",    // Red — immediate action required
  unknown: "#9ca3af",     // Gray — no data or status unknown
  maintenance: "#3b82f6", // Blue — planned maintenance
 
  // Metric colors (for multi-series charts)
  series: [
    "#3b82f6", // Blue
    "#8b5cf6", // Purple
    "#06b6d4", // Cyan
    "#f97316", // Orange
    "#84cc16", // Lime
    "#ec4899", // Pink
  ],
 
  // Threshold bands
  thresholds: {
    normal: "rgba(34, 197, 94, 0.1)",
    warning: "rgba(245, 158, 11, 0.1)",
    critical: "rgba(239, 68, 68, 0.1)",
  },
};
function GaugeChart({ value, min = 0, max = 100, thresholds, label }: {
  value: number;
  min?: number;
  max?: number;
  thresholds: Array<{ value: number; color: string; label: string }>;
  label: string;
}) {
  const percentage = ((value - min) / (max - min)) * 100;
 
  const getColor = () => {
    for (let i = thresholds.length - 1; i >= 0; i--) {
      if (value >= thresholds[i].value) return thresholds[i].color;
    }
    return opsColors.healthy;
  };
 
  return (
    <div className="flex flex-col items-center">
      <svg viewBox="0 0 120 80" className="w-full max-w-[200px]">
        {/* Background arc */}
        <path
          d="M 10 70 A 50 50 0 0 1 110 70"
          fill="none"
          stroke="#e5e7eb"
          strokeWidth="8"
          strokeLinecap="round"
        />
        {/* Value arc */}
        <path
          d="M 10 70 A 50 50 0 0 1 110 70"
          fill="none"
          stroke={getColor()}
          strokeWidth="8"
          strokeLinecap="round"
          strokeDasharray={`${percentage * 1.57} 157`}
        />
        {/* Value text */}
        <text x="60" y="65" textAnchor="middle" className="text-2xl font-bold" fill={getColor()}>
          {value.toFixed(1)}
        </text>
      </svg>
      <span className="mt-1 text-sm text-gray-600">{label}</span>
    </div>
  );
}

Threshold Annotations

Operational charts benefit from threshold annotations that mark the boundary between normal, warning, and critical ranges. These turn a chart from "showing data" to "showing whether data is okay."

function TimeSeriesWithThresholds({ data, thresholds, yKey }: {
  data: Array<{ time: number; [key: string]: number }>;
  thresholds: Array<{ value: number; color: string; label: string }>;
  yKey: string;
}) {
  return (
    <div className="relative">
      {/* Threshold bands rendered as background */}
      <svg className="absolute inset-0 w-full h-full">
        {thresholds.map((threshold, i) => {
          const nextThreshold = thresholds[i + 1];
          const yStart = scaleY(threshold.value);
          const yEnd = nextThreshold ? scaleY(nextThreshold.value) : 0;
 
          return (
            <rect
              key={i}
              x="0"
              y={yEnd}
              width="100%"
              height={yStart - yEnd}
              fill={threshold.color}
              opacity="0.08"
            />
          );
        })}
      </svg>
 
      {/* Line chart on top */}
      <LineChart
        data={data}
        xKey="time"
        yKey={yKey}
        color={opsColors.series[0]}
      />
 
      {/* Threshold labels */}
      <div className="absolute right-2 top-0 space-y-1">
        {thresholds.map((t) => (
          <div key={t.label} className="flex items-center gap-1 text-[10px]">
            <div
              className="h-2 w-2 rounded-full"
              style={{ backgroundColor: t.color }}
            />
            <span className="text-gray-500">{t.label}: {t.value}</span>
          </div>
        ))}
      </div>
    </div>
  );
}

Responsive Dashboard Layout

Ops dashboards are used on large monitors in the office and on laptops during on-call. The visualization layout must adapt to different screen sizes without losing readability.

function ResponsiveDashboard({ widgets }: { widgets: WidgetConfig[] }) {
  return (
    <div className="grid gap-4 grid-cols-1 sm:grid-cols-2 lg:grid-cols-3 xl:grid-cols-4">
      {widgets.map((widget) => (
        <div
          key={widget.id}
          className={
            widget.size === "xl"
              ? "col-span-1 sm:col-span-2 lg:col-span-3 xl:col-span-4"
              : widget.size === "lg"
              ? "col-span-1 sm:col-span-2 lg:col-span-2"
              : widget.size === "md"
              ? "col-span-1 sm:col-span-2 lg:col-span-1"
              : "col-span-1"
          }
        >
          <WidgetRenderer config={widget} />
        </div>
      ))}
    </div>
  );
}

Conclusion

Data visualization in ops dashboards is not decoration — it is communication. The right chart type surfaces patterns. The right color system conveys status at a glance. Threshold annotations turn data into decisions. And responsive layouts ensure visibility regardless of device. The Web Ops Console applies these patterns consistently so that Klivvr's operations team can assess system health in seconds, spot anomalies before they become incidents, and drill into details when investigation is needed.

Related Articles

business

Build vs Buy for Internal Operations Tools

A framework for deciding whether to build or buy internal operations tools, covering total cost of ownership, customization needs, and the strategic value of purpose-built tooling.

6 min read
business

Building an Observability Culture

How to build an observability culture within engineering teams, covering the metrics that matter, democratizing system visibility, and the organizational practices that make observability effective.

6 min read