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Tracking Time in Status in Jira but seeing the same problems every sprint? It's not the team—it's the process. Learn how to find the root cause and fix it once.

Time-In-Status-SummaryCard.png

_The Summary card compares each issue to your team's actual history for the same issue type. Color-coded health tells you at a glance whether something needs attention._

---

## Mistake #2: Treating Symptoms, Not Root Causes

You notice issues pile up in “Code Review” every sprint. So you discuss the issue and agree to try “reviewing faster.” Next sprint, same problem.

**What teams do wrong:**

* Focus on individual issues instead of patterns
* Push people to work faster instead of fixing the process
* Have the same retrospective conversation every two weeks

**What to do instead:**

Look for patterns across sprints. Is the bottleneck consistent? Does it happen with certain issue types? Are certain team members being overloaded?

The problem usually isn’t people—it’s the process. Maybe reviews pile up because:

* No dedicated review time is scheduled
* One person is the bottleneck for all reviews of certain type
* Stories are too large to review quickly


CFD-interpreting-sprint-data.webp

_Flow Intelligence surfaces patterns across your team—trends, predictability, cumulative flow. Stop asking “why is THIS issue late?” and start asking “why does this KEEP happening?”_

---

## Mistake #3: Reporting on the Past Instead of Predicting the Future

Time in Status tells you what already happened. By the time you see a problem, it’s too late. The sprint is over. The stories didn't complete.

**What teams do wrong:**

* Only look at metrics in retrospectives (after the damage is done)
* Can’t answer “when will this ship?” with confidence
* Constantly surprised by delays

**What to do instead:**

Use historical data to predict future delivery. If your team’s stories typically take 3-5 days, and this one started 2 days ago, you can forecast when it’s likely to finish.



Time-In-Status-ForecastCard.png

The Forecast card shows three scenarios based on your team’s actual completion patterns:

* **Likely** — Half of similar work finished in this time or less
* **Plan for** — 85% of similar work finished in this time or less
* **Worst case** — 95% of similar work finished in this time or less

When someone asks, “When will this be done?”—you have an answer based on data, not gut feel.

---

## Mistake #4: Reports That Don't Lead to Action
A report that sits in a dashboard doesn't improve anything. The value isn't in the chart—it's in knowing what to do next.
Your Cycle Time went up 20% this sprint. Your throughput dropped. The cumulative flow diagram shows a pattern forming. What's causing it? What should you try first?

**What teams do wrong:**
-  Collect metrics but never act on them
- Spend retrospectives debating what the data means
- Make changes based on guesses, then don't know if they helped

**What to do instead:**
Get guidance alongside the data—so you can move from insight to action faster.

What-to-expect-4-FlowIntelligence-insights.png

Noesis helps you interpret what you're seeing and suggests what to try next. Ask it anything:

- "What does this cumulative flow diagram tell me?"
- "Why is our cycle time increasing?"
- "Work keeps piling up in QA—what should we try?"

You learn to read the patterns while solving real problems. The next time you see a similar issue, you'll spot it yourself.

---

## Mistake #5: Using Time in Status to Measure People Instead of Process

Time in Status becomes a surveillance tool. "Why did YOUR tickets take so long?" Developers get defensive. Trust erodes. People start gaming the metrics.

**What teams do wrong:**

- Use time data in performance reviews
- Call out individuals in standups
- Create pressure without providing support

**What to do instead:**

Debug your process rather than eroding trust. The best coaches don't track who's slow—they remove what's in the way and make everyone faster.

When work takes too long, it's usually systemic: too much work in progress, dependencies, or overload in review and test.

The question isn't "who's slow?" It's "what's blocking flow?" Maybe one person is the bottleneck for reviewing a certain part of the codebase. Maybe no one can step in when testing backs up. These are process problems with process solutions.

---

## Don't Repeat Yourself
Measuring Time in Status is useful. But it's just a starting point.
The real goal isn't tracking how long things take. It's:

- **Spotting patterns** before they derail yet another sprint
- Understanding **why delays happen** so you can fix them once
- Having **better conversations about process**, not blame

Fix the root cause. Stop explaining the same delays. Move on.

---
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It's a different approach to Time in Status: context-aware health indicators, delivery forecasting, team-level flow analytics, and AI guidance to help you actually fix what's broken.

If you're tired of collecting data that doesn't change anything, give it a try.
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5 Common Mistakes When Tracking Time in Status

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How Many Sprints Before You Spot the Pattern?

When stakeholders push for aggressive timelines, you feel the risk. 

But _"I think this is too ambitious"_ doesn't win arguments. Data does.

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You Know It's a Bad Estimate. But Can You Prove It?

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Without visibility into what, you'll keep guessing.

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Same Story Points. Different Outcomes. Why?

Dozens of estimation sessions per team. Hundreds of data points. All of it disappears the moment the meeting ends. What if that data could actually improve your predictions?

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You Run Planning Poker Every Sprint. What Happens to That Data?

Stakeholders add urgent work mid-sprint. You know it hurts delivery—but without concrete data, you can't quantify the damage or push back effectively.

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How Much Did That 'Quick Request' Actually Cost?

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Your Team Estimates. But Can You Actually Predict?

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Why You Keep Explaining the Same Delays Every Sprint?

Time In Status

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