Is "Business Observability" a real category or am I making something up?
Show IH: Here is the idea.
Infrastructure observability exists because manually checking servers is insane. Datadog, New Relic, Grafana. You instrument everything, set thresholds, and get paged when something breaks. The watching is automated. Engineers don't open a dashboard hoping to notice something looks different.
Business metrics have never worked that way.
Every analytics tool I know of is passive. Stripe shows you revenue when you open it. Google Analytics shows you traffic when you log in. Mixpanel shows you retention when you remember to check. Nothing is watching between your visits. Nothing comes to get you when revenue drops 20% overnight.
I am calling this gap Business Observability, and I built KPILIO to fill it. It monitors KPIs around the clock, detects anomalies statistically, and sends a plain-English alert the moment something deviates from normal. The concept is direct: what Datadog did for servers, KPILIO does for business metrics.
My question for IH is this.
Do you think Business Observability is a real category worth naming and owning? Or does it collapse into analytics, KPI tracking, or something that already exists and I am not seeing?
And the second question: how are you actually monitoring your business metrics right now? Automated alerts, manual checks, or finding out too late?
Genuinely curious before I go all in on the category positioning.
kpilio.com if you want to see what I built.
Feels real if the buyer already has a painful gap between dashboards and decisions. In practice, category names matter less than whether people instantly say, "yes, I need that" when you describe the problem in plain language. Would test the pain first, then backfill the label once a few customers repeat it in their own words.
That is a fair point, the category name is a bet, really. What I'm more confident in is the pain: founders finding out about metric changes days after they happened. That's the thing I keep hearing. If "Business Observability" ends up being the wrong label, I'll drop it. Right now it's the best useful shorthand for the Datadog analogy.
I am curious whether you've felt the pain side of it how are you watching your metrics?
I'd love for you to try the platform out.
~ Robert Dixon
Been building analytics tools for some time already and I've had this exact same thought. The gap is real. Store owners open 4-5 dashboards daily just hoping to spot something weird. That's basically manual server monitoring circa 2005.
The tricky part is thresholds. Infrastructure has clear failure states — CPU at 99% is bad. But "conversion rate dropped 0.3%" might be a Tuesday or might be your checkout is broken. The signal-to-noise ratio is brutal for business metrics.
I'd focus on rate-of-change detection over absolute thresholds. That's where the real value hides. What vertical are you targeting first?
Hi @dave_builds,
This a a good question on thresholds. I set it up not to used fix rules like "alert me if recenue drops $500." Instead it learns what normal looks like for your specific business then flags when something statistically weird happens to your own history. The goal is that using the z-score it will adapt to your specific data., looking out at what should happen rather than setting it at static metric.
So for instance if your revenue is on average $4,200 for the last 30 dasy with a typical variance of $300 , plus or minus youd get a z-score of three. So anything above or below negative 2 is statistically unusual enough that the syste would flag this and send you an alert.
I truley appreciate your insights
~Robert D
@robertdixon The z-score approach makes a lot of sense — way better than fixed thresholds. The rolling 30-day window is smart because it adapts to seasonality automatically. A store doing $2K in January and $8K in December would have completely different "normal."
One thing I'd watch: business metrics tend to have way more legitimate variability than infrastructure metrics. A z-score of 2 on server CPU is always concerning, but a z-score of 2 on daily revenue might just be a weekend. Have you had to layer in day-of-week normalization, or does the rolling window handle that well enough?
Honest answer, the rolling window partially handles it because weekends are baked into the baseline, but I haven't built explicit day-of-week normalization yet. You're right that it's a more complex signal than CPU. The real fix is comparing each day against the same day-of-week historically rather than the flat rolling mean. It's on the roadmap. For now the sensitivity threshold lets users tune out the noise if their metrics are highly seasonal. Have you seen this bite you somewhere?
Yeah the day-of-week thing has bitten me. Had a store where every Monday flagged as a revenue drop — turns out weekends were just 2.5x higher. Same-day-of-week comparison is the fix, agree