# Auto-insight > The viewer didn't look at the right chart, and the product should tell them what it noticed. - Canonical: https://patterns.konigi.com/dashboards/auto-insight - Group: Product mechanics - Level: flow - Status: published - Updated: September 10, 2026 - Also called: generated narrative, watchdog, anomaly feed --- ## Problem The answer was on panel nineteen. Nobody scrolled that far, because nothing about panel nineteen looked different from panels one through eighteen until you read its axis. ## Solution Have the system look, and say what it found. A feed of detected changes, or a sentence at the top of the page describing what is unusual right now, generated rather than configured. The pattern spans a wide quality range, and the difference is whether the output is a finding or a description. **A description** restates the chart: "Errors increased 40% compared to last week." The viewer could see that. It costs a sentence of attention and returns nothing. **A finding** does the work the viewer could not: "Errors increased 40%, and 94% of them come from one build, on one region, all after 14:02." That is the output of comparing many dimensions, which is exactly the labour a person cannot do by scanning. Honeycomb's BubbleUp is the clearest example of the mechanism—compare everything inside an anomalous region against the baseline outside it and rank the dimensions that differ. The economics are unforgiving, and the Google SRE argument about alerting transfers directly. Every page should be actionable; a page that merits only a robotic response should not be a page. An insight feed is an alerting channel with a lower barrier to entry, so it accumulates non-actionable output faster, and once a viewer has scrolled past four uninteresting insights they stop reading the fifth. A boring insight costs more than a wrong one. The wrong one misleads once; the boring one costs the channel its readership. Two properties are non-negotiable. **Provenance**: what was compared, over what window, and what counts as unusual. **A route to the evidence**: any insight should be one click from the data that produced it, or it cannot be checked and will not be trusted. ## Use when The dimensionality is beyond human scanning, the baseline is well enough established that unusual means something, and a person can act on what is surfaced. ## Don't use when The page is small enough to read. An insight panel on a six-panel dashboard is telling people something they can see, and it trains them to skip the top of the page. ## Trade-offs Automated detection has a false-positive rate, and on a dashboard those arrive without the friction that makes people tune an alert, so nobody ever fixes them. Generated prose reads as more confident than the statistics behind it, which is a real hazard when the phrasing implies causation. Baselines are wrong after any deliberate change, so a launch generates a week of insights about the launch. And the feature can substitute for design work: a page that needs restructuring gets an insight panel bolted on instead. ## Checklist - Is each insight a finding, or a restatement of something visible? - Does it say what was compared, over what window? - Is there one click to the underlying data? - What is the false-positive rate, and who reviews it? - Does the phrasing imply causation the analysis does not support? - What happens after a deliberate change that moves every baseline? - Can a viewer suppress an insight type, and does anyone? - How many insights per day, and does anyone read the last one? - Is there any measurement of whether these lead to action? - Would restructuring the page remove the need for this? ## Compare **Datadog Watchdog** is the most developed version in observability, surfacing detected anomalies across the estate as a feed rather than requiring a configured monitor per metric, which is the pattern's real promise: catching what nobody thought to watch. **Honeycomb BubbleUp** takes the opposite path—the human points at the anomaly and the system explains it—which keeps a person in the loop for the judgement and gives the machine the dimensional comparison it is actually good at. **Amplitude and BI tools** generate narrative summaries of chart movements, which sit closer to the description end and are most useful to readers who would not otherwise read the chart at all. **Grafana** has no equivalent in core, so the machine-detection role falls to alerting rules and to ML features outside the dashboard. ## Related Anomaly band is the visual form of the same detection, drawn rather than described. Alert rule is the same signal with a delivery mechanism and an owner. Cross-filter is the human version of the dimensional comparison. Single-column narrative is the layout that most naturally hosts a generated summary. Explain this metric is what an insight needs in order to be checkable rather than merely plausible. ## Related patterns - [Anomaly band](https://patterns.konigi.com/dashboards/anomaly-band) — A metric is moving, and the person watching it can't tell whether the movement is normal for this hour on this weekday or the start of an incident. - [Alert rule attached to panel](https://patterns.konigi.com/dashboards/alert-rule) — The chart shows the line to watch; the viewer wants to be told when it's crossed. - [Cross-filter](https://patterns.konigi.com/dashboards/cross-filter) — Selecting something in one panel should narrow every other panel to it. - [Single-column narrative](https://patterns.konigi.com/dashboards/single-column-narrative) — A non-expert needs one obvious reading order and no configuration. - [Explain this metric](https://patterns.konigi.com/dashboards/explain-this-metric) — Two people read the same tile and mean different things by it. ## Sources - [Datadog, Watchdog](https://docs.datadoghq.com/watchdog/) - [Google SRE Book, Monitoring Distributed Systems](https://sre.google/sre-book/monitoring-distributed-systems/) - [Honeycomb, BubbleUp](https://www.honeycomb.io/platform/bubbleup) --- Screenshots on patterns.konigi.com are reproduced for commentary and criticism. Product names and marks belong to their owners.