Visual reference

Active Actors

How many distinct contributors took at least one tracked action in the window. Useful for spotting growth (more active actors) or attrition (fewer than expected).

Active custom fields

How many custom fields are actively being written to over the last twelve months, compared against the declared catalog and the platform ceiling. Helps catch fields that were created once and forgotten — clutter that quietly slows down screens, search, and audits.

Active custom fields

How many custom fields are actively being written to over the last twelve months, compared against the declared catalog and the platform ceiling. Helps catch fields that were created once and forgotten -- clutter that quietly slows down screens, search, and audits.

Active users

How many of your teammates have actually signed in and used the dashboard recently. The gap between this number and Total Members points at unused seats or teammates who never finished onboarding.

Activity by Project

Time-series of activity per project -- each line represents one project's daily activity over the window. A project that suddenly goes quiet mid-sprint is worth checking; activity peaks misaligned with planned sprints often indicate reactive firefighting rather than planned delivery. Consistent regular activity across a project is a strong signal of a well-run, predictable team. Use **Deep Analysis** to get an AI-generated read on which projects' rhythm looks intentional and which look reactive.

Actor × Project Coverage Matrix

Matrix showing which actors have been active in which projects during the selected window -- direct evidence of what each person is doing versus what their role permits. An actor active in every project at once, especially a new one, is an anomalous access pattern worth investigating; an actor in only one or two projects is operating within an expected scope. Use **Deep Analysis** for an AI read on which cross-project actors warrant a role check and which patterns look consistent with intended access.

Actor Accountability Rate

Per-project percentage of events with known actor attribution (latest available week). Color-coded: green 75 percent or above, amber 60-74 percent, red below 60 percent. Auditors care about this number more than most -- significant actions without identifiable owners are a compliance gap. A project below 60 percent usually has automation or integration sources logging without an actor identity. Use **Deep Analysis** to get an AI-generated read on which projects' attribution gap is automation-driven versus governance-driven.

Actor Activity Consistency Score

A multi-line chart of weekly event count per actor over time, with a consistency rating: steady contributors rate high, irregular ones rate low. Variability alone isn't a problem, but paired with sustained absence it becomes a useful retention or wellness signal. A team of mostly consistent contributors is a sustainable engine; one dominated by irregular patterns is structurally fragile. Use **Deep Analysis** for a read on which irregular patterns are role-driven versus warning signs.

Actor Bypass Fingerprint

A per-actor risk score combining bypass count and reopen contribution. An actor at the top with a high bypass count warrants a direct conversation; a service account or integration ranking high is a security concern. A broad spread across many actors points to a process-design problem rather than individual non-compliance. Use **Deep Analysis** for a read on which actors need conversations and which signals reflect process design.

Actor Cycle Time Profile

A per-actor profile showing median resolution time and total tickets completed — a useful input for one-on-ones, workload balancing, and spotting members carrying disproportionate complex work. An actor well above the portfolio median with a high ticket count warrants a conversation; compare issue types before drawing conclusions, and a cluster near the median indicates a balanced team. Use **Deep Analysis** to get an AI-generated read on the team-balance picture and which 1:1 conversations are highest leverage.

Actor Event-Type Specialization

A heatmap of actors against event types, where each cell is the percentage of that actor's events of that type. It reveals natural specialization and surfaces actors whose visible mix undersells their real value, such as a developer whose dominant activity is reviewing rather than throughput. An unusual mix for a role often signals that role definitions have drifted. Use **Deep Analysis** for a read on whose mix warrants a role-design conversation.

Actor Ownership Churn Ranking

Ranks actors by a weighted ownership churn score that separates triage behavior from avoidance patterns. It's a pattern surfacer, not a performance judgment: a manager's score is naturally high on initial assignments, but a contributor with many unassignments may be dropping work, and churn concentrated in one or two actors is a bus-factor risk. Use **Deep Analysis** to get an AI-generated read on each top actor's specific pattern and what's likely driving it.

Actor Project Spread

Per-actor project count, color-coded by spread: specialist (1 project, green), dual-project (2, blue), broad (3-5, amber), cross-portfolio (6+, red). Cross-portfolio actors are bus-factor risks for the projects depending on them; specialists may need cross-training to maintain delivery resilience. A team where most actors are amber or red is structurally spread thin; a team where most are green has deep focus but limited interchangeability. Use **Deep Analysis** to get an AI-generated read on whether your spread distribution supports your operating model.

Actor Ranking

Horizontal bar chart ranking actors by how many backward transitions they triggered. A high count is not proof of wrongdoing, but it marks closing and reopening habits worth a direct conversation. A spread across many actors points to a systemic process gap; the same name recurring across compliance visuals is a stronger escalation signal. Use **Deep Analysis** to see which actors warrant a one-on-one and what to discuss.

Actor Risk Profile

Ranks accounts by the count and type of admin-level events they trigger. The "who" view for permission drift -- particularly important for identifying over-permissioned accounts (service accounts with admin rights they shouldn't have) or unusual activity from unrecognized admin accounts. A service account appearing at the top with deletion events is a potential security concern; accounts with no recognized name or unclear purpose warrant an immediate access review. Use **Deep Analysis** to get an AI-generated read on which actors warrant access review and which look operationally normal.

Actors tracked

How many actors are currently in scope for the activity-mapping analysis. The denominator for several of the rates below.

Actors with PII events

How many distinct contributors have generated detected events. Useful for spotting structural training needs (many actors with isolated events) versus targeted ones (a few actors with many events).

Admin-Level Change Events Over Time

Weekly timeline of admin-level configuration activity. A burst in a single week that doesn't correspond to a known deployment or migration is a governance red flag; consistent low-level activity spread across weeks is normal housekeeping. A spike dominated by deletion events (field deletions, user removals) is more significant than a creation spike and warrants closer review. Pair with the change-window calendar to verify each spike has an authorising event. Use **Deep Analysis** to get an AI-generated read on which spikes correspond to planned events versus which need explanation.

Admins & owners

The count of people with elevated permissions in your organization. Good housekeeping: keep this number small, and review it whenever someone changes role or leaves the company.

Anomaly Weeks

How many recent weeks contained portfolio-level anomalies -- unusual deviations from the recent baseline across multiple projects. Spikes here often signal a shared external event rather than independent project issues.

Assignee Churn per Issue

Heatmap of projects against issue types showing where assignee churn per ticket is highest. Frequent ownership changes can reflect genuine confusion, but they also obscure who was ultimately accountable. A dark cell at one project-and-type combination means structurally unstable ownership for that work; a project dark across all types signals a portfolio-wide accountability problem. Use **Deep Analysis** to see which cells matter most and what a process clarification should target.

Assignment Surge Timeline

A daily timeline breaking assignment activity into initial assignments, reassignments, and unassignments. Simultaneous reassignment and unassignment spikes mark an ownership-handover wave, a reassignment surge alone indicates bulk triage, and days where unassignments far exceed reassignments are most concerning — work is being dropped faster than picked up. Use **Deep Analysis** to get an AI-generated read on which surge days were planned reshuffles versus emergent disruptions.

Audit Trail Coverage by Event Type

Stacked bar showing the proportion of events with full changelog entries (green) versus those without (red), broken down per event type. Shows which event types are producing the most audit gaps. A gap concentrated in one event type (often field updates or comments) is the targeted intervention point; gaps spread across all types indicate broader changelog policy gaps. Use **Deep Analysis** to get an AI-generated read on which event-type gaps are most diagnostic of the underlying policy gap.

Average Time in Status

Per-project bar chart of average time issues spend in each workflow status. A status with disproportionately high average time is the primary process bottleneck -- focus improvement efforts there, not on execution speed. If the same stage (e.g. In Review) is slow across all projects, the problem is structural -- a shared resource or process constraint; a project where To Do dominates has a backlog problem, not a delivery problem. Use **Deep Analysis** to get an AI-generated read on whether your bottleneck is project-specific or organizational.

Avg Accountability Rate

Average accountability rate across actors -- the share of significant actions clearly attributed to a single owner rather than diffuse or unowned. Auditors care about this number more than most.

Avg alert density

Average rate of compliance alerts raised per project. High alert density without scoring improvements usually means alerts are firing but not acted on -- a process bottleneck rather than a detection problem.

Avg backward rate

Average backward-transition rate across your projects. Useful as a single trend line; if it doubles over a quarter, the team has likely loosened acceptance criteria without realising it.

Avg carryover %

The average percentage of sprint scope that the team carried over from one sprint to the next, across recent sprints. Useful for spotting structural over-commitment rather than one bad sprint.

Avg Config Stability

Average configuration-stability score across your projects. Higher means fewer disruptive configuration changes; sudden drops usually correlate with team transitions or major schema cleanups.

Avg control coverage

Average control coverage across the portfolio -- what share of expected controls are actively enforced. A high score means the controls exist; the next question is whether they're effective.

Avg governance score

The average MetaFrazo Governance Score across all projects in the most recent week, on a 0-100 scale — a single read across workflow discipline, accountability, configuration stability, and control coverage. Green at or above 80, amber 60-79, red below 60. How it's calculated: [MetaFrazo Governance Score](/help/metafrazo-governance-score).

Avg Governance Score

Portfolio-wide average MetaFrazo Governance Score on a 0-100 scale — the single best read on whether your team's governance (workflow discipline, accountability, configuration stability, control coverage) is holding up across projects. How it's calculated: [MetaFrazo Governance Score](/help/metafrazo-governance-score).

Avg Lead Time

Average time from ticket creation to resolution across the portfolio. Pair with backlog change: rising lead time plus growing backlog is the classic "we need more capacity" pattern.

Avg Portfolio Risk Score

The single portfolio-wide average risk score. A useful month-over-month trend line for executive scorecards; sustained increases are the leading indicator that more granular review is needed.

Avg Quality Score

Portfolio-wide average quality score, blending reopen rate, rework, and first-pass completion. Helps reviewers ask "is the team faster but sloppier" rather than treating velocity in isolation.

Avg recurrence rate

Portfolio-wide average recurrence rate. Useful as a single trend line: if the average is creeping up over six weeks, root-cause discipline across the team is slipping.

Avg Velocity Score

Portfolio-wide average of resolution velocity across all tracked projects. Useful as a single headline trend; pair with the per-project ranking below to spot which projects pull the average up or down.

Avg volatility index

A summary measure of how shuffled your tickets are across teammates. Higher values mean the same tickets keep changing hands; lower values mean ownership tends to stick once assigned.

Avg Weekly Hours Logged

Average weekly hours logged by contributors. Useful as a sanity check rather than a performance metric; sustained increases over months often correlate with later burnout.

Avg Weekly Resolution

Average number of tickets your team resolves per week across the portfolio. The headline throughput number for capacity planning.

Avg Workflow Adherence

How closely the team's actual transitions match the documented workflow, averaged across projects. A workhorse number that hides specific problems until you drill into the per-project chart below.

Backlog Age Distribution

Stacked bar chart grouping open tickets by age since their last status transition across four buckets: fresh (0-7 days), aging (8-14), stale (15-30), frozen (30+). When frozen issues exceed 50 percent of open issues in any project, an amber banner is shown. A large frozen bucket is the clearest signal of a backlog problem -- those tickets are occupying planning space without prospect of delivery. Frozen-dominated projects need a deliberate grooming session, not just faster delivery. Use **Deep Analysis** to get an AI-generated read on which projects' frozen volume warrants triage first.

Backlog Arrival vs Resolution Rate

Tracks weekly arrivals (tickets entering the backlog) against weekly resolutions (tickets pulled out). The area between the two lines is your net backlog change. A widening gap over multiple weeks is the definition of backlog acceleration -- requires either reducing arrivals (scope control) or increasing resolutions (capacity); weeks where resolutions exceed arrivals are healthy debt-clearing. A consistently balanced chart means stable backlog size. Use **Deep Analysis** to get an AI-generated read on which structural interventions match your current gap shape.

Backlog Inflow Calendar Heatmap

A calendar heatmap of daily backlog inflow intensity across all weeks, revealing whether spikes follow predictable patterns or are real anomalies. Consistent Monday peaks usually reflect planning events; random spikes with no calendar correlation more likely need root-cause investigation. Use **Deep Analysis** for a read on which patterns are calendar-driven and which are anomalies worth investigating.

Backlog Net Growth Trajectory

Multi-line area chart of weekly net backlog change per project. A consistently positive line signals accumulating backlog; a negative line indicates a debt-clearing phase. Sustained upward slope is the structural signal that current capacity cannot meet current intake -- a conversation about scope or staffing, not effort. The view is the most direct evidence of whether each team is catching up or falling behind. Use **Deep Analysis** to get an AI-generated read on which projects' backlog trajectories are most concerning and what intervention they call for.

Backward transitions

How often tickets move backward in the workflow (for example, from In Review back to In Progress). Some backward movement is healthy (revising work after feedback); excessive backward movement signals process or quality problems.

Backward Transitions

Grouped bar chart of weekly forward workflow transitions alongside backward ones (Done or Closed reopened into an active status). When backward transitions stay above a small baseline share, rework is structural rather than incidental -- a sign that requirements, handovers, or definition-of-done are unclear. Use **Deep Analysis** to see whether rework concentrates in specific projects, periods, or actors, and what to address first.

Batch Close Detection

Detects events where one actor closed two or more distinct tickets within a 30-minute window. At low volumes this can be efficient review work; at high volumes, especially for Critical or High priority tickets, it suggests individual tickets aren't getting the attention they need. A clean log means tickets are being closed individually. Use **Deep Analysis** for an AI read on whether your batch closes are efficient reviews or process bypass, and which warrant a closer look.

Batch close events

How many batch-close events were detected (multiple tickets resolved in quick succession by the same actor). Sometimes legitimate (quarterly cleanup); often a sign that "let's empty the backlog" took priority over real resolution.

Breach Acceleration Trend

Per-project week-over-week change in breach rate -- the direction of change matters as much as the current state. A project at 40 percent that improved from 60 percent is a different situation from the same 40 percent that deteriorated from 20 percent. The chart immediately distinguishes improving teams from deteriorating ones, even when they share an absolute breach rate. Use **Deep Analysis** to get an AI-generated read on which improvements look durable versus which deteriorations are accelerating.

Burst actors

Number of actors who have suddenly started producing tickets and events after a period of inactivity (or zero history). For example, a new account that produces 200 events in its first day deserves an authentication check.

Bypass Risk Score per Project

A 0-100 gauge per project blending three governance signals: workflow bypass rate, reopen rate, and stuck-issue ratio. Below 40 is normal; 40-70 is emerging issues worth monitoring; above 70 needs immediate investigation. Comparing scores across the portfolio instantly shows where governance attention is most needed. Use **Deep Analysis** for a read on which projects to prioritize and why.

Change Lead Time

DORA Change Lead Time: median days from "work started" to "change shipped" across the portfolio, week by week. The headline delivery-performance metric alongside DORA's other three. Lower is faster delivery; a falling trend is the goal during a delivery improvement programme. A stable lead time with rising change failure rates is a sign that speed is being bought with quality. Useful in DORA dashboards alongside Deployment Frequency, Change Failure Rate, and Time to Restore.

Chronic incident issues

Tickets that have been opened, closed, and re-opened repeatedly -- the long-running chronic issues. Usually one or two of these account for an outsized share of your team's frustration; addressing them changes the team's day-to-day quality of life.

Compliance Alert Density

Per-project rate of compliance alerts, normalized by activity volume, so a busy project's 10 alerts read differently from a quiet one's. Density is more meaningful than raw counts when comparing projects of different sizes or tracking improvement over time. High density alongside falling compliance scores is the worst combination -- many alerts, but they aren't driving action. Use **Deep Analysis** for an AI read on which projects' alert density is informative versus noise.

Compliance Event Canvas

An interactive, zoomable canvas of every compliance-relevant event in your project history, filterable by actor, type, time, and category. Built for live review sessions: pick an audit window and surface what stands out -- notable spikes, unusual actor patterns, or gaps versus a control standard. Useful for confirming consistent control coverage across a quarter or focusing on a single flagged project.

Composite Deviation Score

Per-project composite score combining all individual control deviations -- unlogged resolutions, batch closes, direct skips, and post-creation priority changes -- into a single number. A high score signals multiple concurrent control gaps and a pattern of process bypass, not an isolated exception. It's most useful for executive reporting and for prioritizing which projects to investigate first. Use **Deep Analysis** for an AI read on which composite scores are most actionable and which signals are doing the heavy lifting.

Composite Risk Score Ranking

A ranked list of every open ticket scored across five risk dimensions: priority, age, inactivity, reopen history, and assignment churn. This measures governance risk, not complexity: high-scoring tickets show multiple warning signs that they may not resolve cleanly without intervention. The top 5-10 are usually your next focused-effort backlog. Use **Deep Analysis** for a read on each top ticket's risk shape and the most likely intervention.

Config Stability Score Over Time

Multi-line time series of weekly config stability score per project (0-100). Higher scores indicate fewer non-standard field changes relative to total changelog activity -- a measure of how disciplined the team is about staying inside the documented schema. A persistent downward trend signals an active customisation cycle; flat is what mature organisations target. Use **Deep Analysis** to get an AI-generated read on which projects' config drift is intentional versus accumulating.

Configuration Change Event Audit Log

A filterable audit table of configuration events ordered by time, with date, event type, project, issue key, and triggering actor. This is the primary tool for forensic investigation and compliance review: filter by actor for an access review, by event type to isolate destructive actions, or by date range to scope an audit period. Use **Deep Analysis** to get an AI-generated read on suspicious patterns in your filtered set and what investigation steps to take next.

Configuration Changes Over Time

Stacked bar chart of daily configuration event volume, split between additive actions (created, updated) and destructive ones (deleted, trashed, soft-deleted). A sudden deletion spike on a single day warrants investigation -- accidental removal of configurations still in use is one of the most common causes. Activity that aligns with sprint starts is normal planned setup; deletions approaching creation volume over time signal schema instability. Use **Deep Analysis** to get an AI-generated read on whether your recent destructive spike is planned or unauthorized.

Configuration Event Type Distribution

A bar chart of admin-level configuration events grouped by domain (Field Context, Issue Type, User, Project, Other). Most activity is legitimate, but spikes in field creation or user changes can signal unplanned schema growth, permission creep, or changes outside an approved window; a balanced, low-volume spread points to a well-governed, stable instance. Use **Deep Analysis** to get an AI-generated read on which category spike looks unplanned and what change-window verification it argues for.

Consistent Actors

How many contributors show consistent week-over-week activity. A higher count means a more sustainable workforce; a low number with high concentration means the team relies heavily on a few engines.

Contributor Acceleration Index

A per-actor index comparing the work each person advances against what they create or are assigned; above 1.0 means they clear more than they add, below 1.0 the reverse. This isn't a performance ranking, but when low-index contributors coincide with backlog growth it's worth a targeted conversation. Use **Deep Analysis** to get an AI-generated read on the balance picture and which conversations would be most useful.

Control Coverage Rate

Percentage of resolutions that passed all defined controls -- worklog present, a second actor involved before closure, and required workflow stages traversed. It's the most direct measure of program effectiveness, revealing how much work is escaping the compliance safety net: above 90 percent is strong discipline, below 70 percent means the program isn't in routine use. Use **Deep Analysis** for an AI read on which controls are most frequently bypassed and which projects need attention.

Control Deviation Trend

Total compliance deviations across all signal types, tracked over 12 rolling weeks. The rolling total reveals the overall trajectory of compliance health that individual signals can obscure -- three or more consecutive weeks of rising deviations means the program is losing ground. A flat or declining trend with steady activity volume indicates discipline is holding. Use **Deep Analysis** for an AI read on which signals contribute most to the recent trend and what to address first.

Control Effectiveness

Weekly ratio of compliant versus non-compliant resolutions across the portfolio -- the most fundamental compliance metric. It makes immediately visible whether effectiveness is improving or whether more work is slipping through the cracks; a rising compliant share reflects successful enforcement, a falling share is an early warning. A stable ratio just below target often means policy is enforced inconsistently across teams. Use **Deep Analysis** for an AI read on which teams drive the current ratio and what targeted enforcement should look like.

Creation Composition

Mix of ticket types (bugs, stories, tasks, etc.) created in the window. A sudden shift -- a six-week stretch where bugs jump from 20 percent to 45 percent of intake -- usually signals either a quality regression or a deliberate cleanup effort. Different mix shapes argue for different team responses: bug-heavy mixes need quality investment, feature-heavy mixes need scope discipline. A stable mix is consistent with mature delivery operating in steady state.

Critical alerts

Of those spikes, how many crossed the critical-severity threshold. Helps separate background noise from the few events that genuinely affected delivery.

Critical count

Of those scored, how many cross the critical-risk threshold. Usually a small number; if it grows steadily week-over-week, you have a portfolio-level problem rather than a few outlier tickets.

Critical issues

How many of the detected events crossed the critical-severity threshold. These are the events to address this week, not next quarter.

Critical issues (severity tile)

Current count of tickets in the Critical severity tier of the sensitive-data detector. The same number as the Critical issues KPI, broken out as its own card for the severity-breakdown row on the page.

Critical stuck issues

Tickets currently stuck in an active status well past a healthy duration. These are the ones masquerading as active work but not actually moving; left alone they slip into release retrospectives as surprises.

Cross-Project Contagion Matrix

Matrix showing which project pairs share the most actors who triggered risk events. When the same individual triggers governance failures in multiple projects simultaneously, it isn't coincidence -- it's contagion. Helps identify whether risk is spreading through shared team members, contractors, or service accounts operating across project boundaries. Use **Deep Analysis** to get an AI-generated read on which project pairs' shared-actor contagion looks structural and what containment looks like.

Cross-Project Spread

Flags actors who became newly active across three or more projects within a two-week window -- one of the clearest early signals of access drift. Sudden multi-project access may be a legitimate role change or access granted without proper review; gradual expansion is far less concerning. A clean chart indicates stable, predictable access across the portfolio. Use **Deep Analysis** to see each flagged actor's expansion shape and which need verification against role-change records.

Cross-Project Timing Correlation

Quantifies the timing correlation between project pairs -- when two separate projects both experience issue spikes at the same time, consistently, it may not be coincidence. Shared deployment pipelines, infrastructure components, or dependencies can cause correlated failures across multiple projects simultaneously. Strong correlations identify which projects are likely sharing a failure mode that manifests jointly. Use **Deep Analysis** to get an AI-generated read on which project pairs' correlations look causal and what shared dependency to investigate.

Cross-Team Collaboration Index

Matrix showing the percentage of tickets where each pair of actors both generated events within the same project. Higher values mean more frequent joint working; low collaboration with high specialisation is normal in expert teams, low both is silos worth addressing. The matrix is most useful for spotting unexpected collaboration pairs (often a hidden mentor-mentee relationship) and absent expected pairs (which often reveals a structural communication gap). Use **Deep Analysis** to get an AI-generated read on which collaboration shapes argue for team-design changes.

Cumulative Flow Diagram

Stacked-area chart showing how many tickets sit in each workflow status over time -- the classic cumulative-flow visualization. Widening bands near the top indicate build-up in that status; bottlenecks become visually obvious before they show up in cycle-time numbers. A balanced flow with similar-width bands across statuses is the healthy steady state. The view is the most diagnostic chart for "where is work actually getting stuck".

Cycle multiplier

How much longer a reopened ticket takes to finish, compared to a ticket that closes on the first try. A multiplier of 2x or 3x is common; values above 4x signal that reopens are essentially restarts.

Cycle Time by Priority

Median and P90 cycle time per priority level. A large gap between Highest and Low median cycle times confirms priority triage is working as intended; similar medians between High and Medium suggest these two levels aren't being treated differently and one is redundant. A wide P90 versus median for any priority means delivery is inconsistent -- some tickets in that category take much longer than others for unclear reasons. Use **Deep Analysis** to get an AI-generated read on whether your priority scheme is functioning as designed.

Cycle Time Control Chart

Statistical-process-control view of cycle time with average and upper-control-limit bands drawn in. Points outside the bands are the cycles worth investigating individually; the trend of the average is the long-term capability signal. Teams using this in retrospectives typically focus on the outlier weeks first, then the band drift over months. A tight, stable band indicates mature delivery; a widening band is the early warning that process variance is increasing.

Cycle Time Histogram

Histogram of cycle times across all completed tickets in the window. Shape tells the story: a tight single peak is a predictable process; a long right tail means a handful of outliers are dragging the average; a bimodal distribution often reflects two different work classes (small bugs versus features) being mixed in the same flow.

Cycle Time Impact: Reopened vs Direct

A grouped bar chart comparing average cycle time for tickets resolved without reopening versus those reopened at least once, putting a concrete day count on what rework costs. A large multiplier is a strong case for better acceptance criteria, clearer requirements, or earlier testing; a small gap means reopens are handled efficiently. Use **Deep Analysis** to get an AI-generated read on what the multiplier implies for your team's process investments.

Daily Backlog Inflow (Control Chart)

A statistical control chart of daily backlog inflow with a rolling baseline and control bands, separating routine variation from genuine anomalies. Days flagged as spikes need investigation; spikes dominated by new creations signal a demand shock, while those driven by reopens signal a quality failure. Use **Deep Analysis** for a read on which spikes look anomalous versus predictable.

Daily Governance Score

A day-by-day time series of the MetaFrazo Governance Score (0-100), blending workflow discipline, accountability, configuration stability, and control coverage. Consistently above 80 signals healthy governance; watch for sustained downward trends and when they began. How it's calculated: [MetaFrazo Governance Score](/help/metafrazo-governance-score).

Delivery Velocity

Dual-line chart of issues created vs issues resolved over time -- the gap is the net change in backlog size. When creation consistently outpaces resolution, backlog pressure is building even if no one has noticed; this is a leading indicator, not a lagging one. Persistent gaps where creation exceeds resolution are early warnings to address before they become a resourcing crisis; convergence over time suggests a well-balanced team at steady cadence. Use **Deep Analysis** to get an AI-generated read on whether your current gap looks recoverable or structural.

Deviation actors

How many distinct actors are contributing to control deviations. A small number indicates targeted coaching can fix the pattern; a large number indicates structural process issues.

Deviation Rate by Issue Type

Heatmap of control deviation rate by project and issue type. Deviations aren't evenly distributed: a dark cell at one project-and-type combination points to a targeted enforcement gap, while a project dark across all types signals a culture problem rather than a type-specific one. A uniformly light heatmap indicates consistent compliance across all work categories. Use **Deep Analysis** for an AI read on which cells matter most and what targeted enforcement should look like.

Direct open→done

Count of tickets that went directly from an open status to Done with no intermediate steps. Most are legitimate (housekeeping work, duplicates marked done); persistent rates point at a workflow that doesn't reflect actual practice.

Direct Open→Done Skip

Weekly bar chart of tickets that moved straight from an initial status to a terminal one in a single step, bypassing every intermediate stage. A high rate means the team is either closing tickets without doing the work or skipping the review stages that catch quality issues. Week spikes often track sprint-end pressure; a steady decline means workflow compliance is improving. Use **Deep Analysis** to see which projects, issue types, and weeks drove the recent skip rate.

Email-Pattern Issues Log

Truncated log of specific tickets containing email-pattern PII -- the most common and highest-risk type. Truncation avoids re-exposing the full data; the log is structured for data-protection teams to remediate case by case. A long list in a short period may point to a workflow or integration generating exposure; a log that clears over time shows remediation working. Use **Deep Analysis** to see patterns across entries and the most likely structural source to address.

Escalation Run Duration

How many consecutive weeks each project has spent above the escalation threshold -- chronic governance failures become impossible to overlook. A one-week spike may be temporary; a six-week run is structural and requires leadership intervention, not another sprint of "we'll get to it". Short runs are usually recoverable with focused attention; runs above three weeks should trigger an executive conversation. Use **Deep Analysis** to get an AI-generated read on which long-running escalations look stuck versus on a path to recovery.

Escalation Score Timeline

Longitudinal view: composite risk score per project plotted over time. Rather than showing where projects stand today, shows whether they're trending toward escalation, stabilising after an intervention, or continuing to deteriorate. For executives reviewing governance trends, this is the primary evidence of whether risk management is improving or getting worse over time. Use **Deep Analysis** to get an AI-generated read on which projects' trajectories look most concerning and which deserve a "what worked" study.

Event Density Anomaly Detector

Control chart of portfolio-wide weekly event count with a 14-week rolling mean and plus-or-minus two-standard-deviation control limits. Weeks exceeding the upper limit are highlighted red as statistical anomalies. Flags portfolio-level event volume that deviates meaningfully from the recent baseline -- often a shared external event (a major release, an incident, a process change) rather than independent per-project issues. Use **Deep Analysis** to get an AI-generated read on which anomaly weeks correspond to known events and which need explanation.

Event Seasonality (DOW × Week-of-Month)

Heatmap of average event volume by day-of-week and week-of-month. Reveals seasonal patterns in workflow events; useful for sprint timing decisions. A hot vertical band on every Monday usually means weekend processes (deploys, automation) are the actual driver of weekly volume, not human work. End-of-month spikes often coincide with release pressure or quarterly close. Use **Deep Analysis** to get an AI-generated read on which seasonal patterns suggest planning changes and which are accepted realities.

Event Volume by Actor

Per-actor activity profile showing total event count and the mix of action types. The breakdown makes role-versus-action mismatches visible -- an actor who is mostly configuration changes may be an admin, or may have access they shouldn't be using. Volume outliers in both directions warrant attention, from automation under a user's credentials to inactive accounts that should be deprovisioned. Use **Deep Analysis** for an AI read on which actors look unusual for their role and where an access review should focus first.

Event-Type Specialization by Actor

Per-actor table showing each actor's dominant event type alongside a derived segregation-of-duties risk label, built for role-based access reviews. It compares what people actually do against what their assigned role should permit -- a developer whose dominant activity is configuration changes may have more access than the role requires. Aligned actors are operating as expected. Use **Deep Analysis** for an AI read on each mismatch and what an access adjustment should look like.

Executive Summary

Board-ready one-page summary: number of projects in escalation, number approaching the threshold, portfolio average risk score, week-over-week change. No charts, no detail -- just the numbers executives need to assess whether the portfolio requires attention. Pairs naturally with monthly review decks; the format is designed to translate directly into an executive update without further trimming. Use **Deep Analysis** to get an AI-generated narrative that complements the numbers and frames the leadership conversation.

Fast-Close Distribution

Histogram of the time between ticket creation and closure across buckets from under five minutes to over a day. Tickets closed within minutes were almost certainly never worked -- they were rubber-stamped, possibly to inflate completion metrics. A heavy under-five-minute bucket signals systematic rubber-stamping; a distribution skewed toward longer lifecycles is healthy. Use **Deep Analysis** to see which projects and actors drive the fastest closures and where to focus an investigation.

Field events

Configuration events specifically affecting fields -- adding, renaming, retyping, or changing field configuration schemes. Often the noisiest category in a healthy admin practice.

Field events

Permission events affecting field configuration -- adding restricted fields, changing who can edit a field, restructuring field-permission contexts. Often the most subtle category and the one auditors ask about most.

First Response Time

How quickly new tickets get their first acknowledging move -- comment, assignment, status change. A leading indicator for customer-felt responsiveness, distinct from full resolution time. Long first-response distributions usually reflect intake or triage problems rather than execution problems; teams may need a clearer "who picks up new tickets" rule.

Flow Performance & Variant Explorer

An interactive map of your real workflow with timing laid over it: each step is shaded by how long work typically waits there, the busiest hand-offs stand out, and a side panel ranks the distinct routes work takes end to end — click one to trace it on the map. A date slider replays the workflow as it looked at any point in time, and median-to-85th-percentile figures show both the typical case and the slow tail. Use it to find the steps and routes that quietly stretch out delivery. It builds on the workflow shown in Workflow Discovery — configure your status and actor groupings there.

Forensic Timeline

For a selected high-risk ticket, a chronological timeline of every significant event in its lifecycle -- creation, status changes, priority changes, assignee changes, reopens, comments. Shows exactly how the ticket accumulated its current risk profile and which intervention opportunities were missed along the way. For compliance audits, this is the record demonstrating due diligence on high-risk tickets. Use **Deep Analysis** to get an AI-generated narrative of the timeline -- what likely happened, what the warning signs were, and what to prevent next time.

Governance Maturity Score by Project

Grouped horizontal bar chart per project across four governance dimensions: Workflow Adherence, Config Stability, Accountability, and Audit Coverage (0-100 each, latest available week). The most useful single chart for governance audit prep -- a project low on Accountability needs role discipline; low on Audit Coverage needs changelog policy attention. A project balanced and high across all four is the gold standard. Use **Deep Analysis** to get an AI-generated read on which projects' dimension gaps argue for targeted versus broad governance investments.

Governance Maturity Trend

Multi-line time series of weekly composite governance maturity score per project, with a dashed red line at 60 marking the minimum-maturity threshold. Below the line requires intervention; above the line is in steady state. The single line for "are we maturing or backsliding" -- rising during a maturity programme, flat in established practice. Use **Deep Analysis** to get an AI-generated read on which trajectories signal genuine maturity gains versus tactical fixes that may regress.

Growing weeks

How many consecutive recent weeks the backlog grew (arrivals outpacing resolutions). A streak of three or more is a useful trigger for planning-room conversations about capacity vs scope.

High / Critical Projects

How many of your projects are currently scoring High or Critical on the composite risk score. Useful as a board-readiness number: "we have three projects in critical risk this month, here's the picture."

High issues (severity tile)

Current count of tickets in the High severity tier of the sensitive-data detector. High items are still meaningful and worth review, even if Critical takes immediate priority.

High SoD risk actors

Contributors with the highest segregation-of-duties risk scores. Usually a small number; their patterns deserve targeted review rather than blanket policy changes.

High-drift actors

Actors with the highest composite permission-drift scores -- those whose recent activity pattern includes the most permission-altering events. Usually small in number; the list is a useful daily check for compliance reviewers.

High-risk projects

How many of your projects are currently scoring above the high-risk threshold on overall workflow compliance. A non-zero count is your weekly "go look here first" signal.

Highest peak ratio

The biggest spike-to-baseline ratio observed in the window -- for example, "5.2x the recent average". A useful at-a-glance read on the most extreme outlier.

Highest project score

The single highest project-level risk score in your portfolio right now. If the same project tops the list for three or more weeks, structural intervention beats incremental fixes.

Highest risk score

The single highest current risk score in your portfolio. Look at the ticket behind it; if it's been holding the top slot for weeks, it deserves a dedicated work session, not another sprint of "we'll get to it".

Hourly Activity Density

Heatmap of activity volume by day-of-week and hour-of-day -- establishes the team's normal working rhythm so anomalies become visible. A team working 9-to-5 weekdays shows a very different shape from a globally distributed team, and both are valid; understanding the normal pattern is the prerequisite for spotting deviations. Bright cells outside the dominant pattern often indicate automation or after-hours human activity worth verifying. Use **Deep Analysis** to get an AI-generated read on your team's working pattern and which deviations look most worth following up.

In warning zone

How many open tickets have consumed enough of their SLA budget to be at significant risk of breaching soon (typically above 80 percent consumed). These are still recoverable with a same-day push.

Inactive count

How many high-risk-scoring tickets are also currently inactive (no recent transitions or comments). The most expensive category: high consequence and no one moving on it.

Inactive High-Priority Detector

Surfaces high-priority tickets that have not had a single event in seven days -- the silent risk pool. Inactive doesn't mean forgotten by accident; it could also be blocked or parked. But in any of those cases, the ticket represents unacknowledged risk that needs explicit acknowledgement. Each ticket here should pair with a standup answer: why is this not moving, and what's the next step? Use **Deep Analysis** to get an AI-generated read on which inactive tickets look blocked versus forgotten, and what to follow up first.

Inactive Project Bucket Distribution

Shows the share of your projects in each activity bucket: active (events in the last 90 days), slowing (90-180), dormant (180-365), abandoned (365+), and archived. When dormant, abandoned, and archived together exceed roughly a third of the portfolio, project pickers and filters start returning stale results, so it is worth a cleanup pass.

Inactive Project Bucket Distribution

Bar chart splitting the project portfolio into activity buckets: active, slowing, dormant, abandoned, and archived. When dormant, abandoned, and archived together exceed roughly a third of the portfolio, project pickers and filters are returning stale results, and a cleanup cycle usually pays back quickly. If dormant projects are sitting in abandoned rather than archived, the tenant isn't closing projects out properly. Use **Deep Analysis** for an AI read on which projects to retire first and where cleanup has the biggest impact.

Inactive project ratio

Of the projects that have ever emitted activity, the share that have gone silent or are formally archived — split across active, slowing, dormant, abandoned, and archived. A high dormancy share usually means the project list is hiding work that should be wrapped up or closed.

Inactive project ratio

Of the projects that have ever emitted activity, the share that have gone silent or are formally archived -- split across active, slowing, dormant, abandoned, and archived. A high dormancy share usually means the project list is hiding work that should be wrapped up or closed.

Incident Resolution Time (MTTR)

Distribution of resolution times split into two cohorts: tickets resolved without reopening, and tickets reopened at least once before final resolution. For DORA compliance, Mean Time to Recovery maps directly to this view -- faster resolution and a tighter distribution are stronger MTTR. A long right tail in either cohort reveals where capacity should be targeted. Use **Deep Analysis** to get an AI-generated read on what's driving the slow-end distribution and how to compress it.

Incident Temporal Heatmap

Calendar heatmap of when incidents happen by day-of-week and week-of-month. Reveals temporal patterns the team often hasn't noticed -- always Monday after deploys, end of month under release pressure, every quarter-end. Understanding the temporal pattern is the first step to breaking it; a flat heatmap suggests incidents are randomly distributed rather than driven by predictable triggers. Use **Deep Analysis** to get an AI-generated read on which calendar patterns look causal and what process change would attack them.

Inflow Source Attribution

Per-week breakdown of backlog inflow by source -- newly created tickets, tickets returned from active states, reopened terminal tickets. Knowing why a spike happened is what lets you respond correctly. A spike dominated by new creations signals unexpected demand (scope creep, external trigger, planning failure); a spike dominated by returned-from-active is WIP instability; a spike dominated by reopens is a quality signal. Use **Deep Analysis** to get an AI-generated read on the most likely source of your recent spike and what response it actually warrants.

Intake vs Outflow Trend

Compares the rate of new ticket arrivals against ticket resolution, week by week. When the resolution line dips below the arrival line for several weeks, the backlog grows even if individual sprints feel healthy. A widening trend over multiple weeks is the structural signal that capacity does not match intake; consistent convergence is the healthiest pattern. The view exposes asymmetries that average aggregates would mask.

Issue Age at Backlog Entry During Spikes

Breaks down each spike's tickets by age at the moment they entered the backlog -- distinguishing genuine new demand from delayed-triage backlog dumps. A spike dominated by same-day issues is a real demand surge that may require a capacity response; a spike dominated by older issues is a triage event (the work isn't actually new, just newly visible). A mix of ages indicates a combined event needing both responses. Use **Deep Analysis** to get an AI-generated read on the composition of your recent spike and what response shape it argues for.

Issue Reassignment Hotspot Leaderboard

A ranked list of tickets with the highest reassignment counts, flagging those handed off three or more times. Tickets with no current owner are the highest priority since they're stuck with no responsible party, and a ticket bounced five or six times usually points to unclear requirements or a task no one wants; clustering within one epic signals a scoping problem. Use **Deep Analysis** to get an AI-generated read on which hotspots share a root cause and what clarification work would resolve them.

Issue type events

Permission events affecting issue type configuration -- new issue types, changed permission schemes per type, retired types. Less common than user events but each change touches every ticket of that type.

Issue-Level Field Change Heatmap

A heatmap of daily change counts for governance-sensitive fields (assignee, priority, issue type, reporter, project, due date). Persistent assignee churn suggests ownership isn't clearly defined; frequent priority changes indicate scope isn't well understood at planning. Unexpected reporter or project changes should be rare and warrant investigation. Use **Deep Analysis** to get an AI-generated read on which field's churn is most diagnostic and what it points at.

Issues in breach

How many open tickets are currently past their SLA budget. Each is a customer commitment we're failing to keep; pair with priority + customer-visible flag to triage the order of escalation.

Latest governance score

The most recent MetaFrazo Governance Score for your portfolio, on a 0-100 scale — a single read across workflow discipline, accountability, configuration stability, and control coverage. A sustained drop of more than ~5 points is worth a look. How it's calculated: [MetaFrazo Governance Score](/help/metafrazo-governance-score).

Latest spike date

When the most recent spike happened. If "yesterday" you may still be processing the surge; if "three weeks ago" you can safely assume the system has rebalanced.

Lead Time Trend by Issue Type

Multi-line chart of weekly average lead time (days from creation to first terminal resolution) broken down by issue type. Diverging trends between issue types reveal type-specific process or capacity issues -- if Bug lead time held steady while Feature lead time doubled in the last quarter, the team is keeping reactive promises but starving proactive work. Use **Deep Analysis** to get an AI-generated read on which type's divergence is most diagnostic of an underlying capacity allocation problem.

Low issues (severity tile)

Current count of tickets in the Low severity tier of the sensitive-data detector. The acceptable-noise tier; useful as a sanity check that the detector isn't drowning in false positives.

Lowest MTBI

The smallest mean time between incidents seen across any of your projects. A low MTBI on a single project surfaces it as the natural candidate for a focused reliability investment.

Median cycle time

Half of your tickets resolve faster than this number, half take longer. Median is the right measure for "typical" cycle time because it ignores the long tail of outliers.

Medium issues (severity tile)

Current count of tickets in the Medium severity tier of the sensitive-data detector. Often the largest tier; cleaning here is the structural fix rather than the urgent one.

Mined Petri Net

An automatically discovered map of how work actually moves through your statuses, inferred from thousands of observed transitions rather than the workflow you documented. Unexpected shortcuts and dead-end states stand out — a direct path that shouldn't exist often signals a routine workaround, while a map that matches your documentation is strong evidence practice and policy align. This is the structure view; for how long work waits at each step and which routes are slowest, use Flow Performance, which lays timing over the same map.

Missing Approval Heatmap

Heatmap of projects against issue types, where each cell combines zero-comment closures and solo resolutions into a single missing-approval rate. A dark cell at one project-and-type combination points to a targeted policy failure; a project dark across all types signals a portfolio-wide culture problem. A uniformly light heatmap is the target state. Use **Deep Analysis** to see which cells matter most, what they point at, and what an intervention should look like.

Most-Reopened Issues Leaderboard

A ranked table of tickets with the highest reopen counts, including project, issue key, summary, reopen count, and a risk badge. Tickets reopened twice or more should be discussed in the next retrospective; if several cluster in the same project or epic, the problem may be at the requirement or design level rather than execution. Use **Deep Analysis** to get an AI-generated read on whether your top repeat-reopens share a root cause and where to invest in clarity.

Net Backlog Change

Net change in backlog size in the window -- positive means the backlog is growing, negative means shrinking. The single number that says whether you're catching up or falling behind.

New Actor Burst Detection

Flags new actors whose first-week activity far exceeds the typical debut for new accounts. A moderate first week is normal onboarding; an unusually high one may indicate account takeover, improper access provisioning, or a service account given overly broad access. Multiple burst actors in the same period often signal a systematic provisioning gap. Use **Deep Analysis** to see each flagged actor's activity shape and whether the access pattern justifies an immediate review.

Off-hours actors flagged

Number of contributors whose off-hours activity exceeds a healthy threshold. Often a leading indicator of burnout risk in addition to a compliance signal.

Off-Hours Events by Actor

Per-actor share of total events that occurred outside business hours. Some off-hours activity is expected, but an actor well above roughly a quarter warrants explanation -- it may indicate automation running under their credentials, deliberate timing to avoid visibility, or unusual personal hours. Combined with other signals, high off-hours actors become a stronger investigation target. Use **Deep Analysis** for an AI read on which off-hours patterns look like automation, role-driven, or worth a conversation.

Off-Hours Transitions

Calendar heatmap of status transitions happening outside business hours -- early, late, or on weekends. Off-hours activity alone is not suspicious, but recurring volume is, and combined with rapid or backward transitions it becomes a stronger signal. A consistent cell across weeks often flags automation or someone deliberately working unusual hours. Use **Deep Analysis** to see whether the pattern is automation, after-hours work, or something to review.

Open Issue Age Distribution

Heatmap of issue counts per project across four age buckets: 0-7 days, 8-30, 31-90, 90+. Darker cells mean more open issues in that age band. A long tail of issues older than 90 days is the usual signature of unmanaged scope creep -- visible in this view even when each individual project looks healthy on its own. Concentrations in the 0-7 day band are healthy intake; concentrations in 90+ are structural drag. Use **Deep Analysis** to get an AI-generated read on which projects' age shape argues for active triage versus accepted noise.

Overall compliance

Overall percentage of tickets that met their SLA in the window. The headline number for "how well is the team hitting commitments"; the visuals below explain where any miss is coming from.

Overall reopen rate

The share of completed tickets that were later reopened, across your selected projects and window. A sustained value above 10-15 percent usually points to weak definition-of-done or thin acceptance testing.

Ownership Gap Detector

A per-project breakdown of unowned tickets by workflow status, separating unowned-in-backlog from unowned-in-progress. Unowned in-progress work is a governance and delivery risk that needs immediate action — assign it or move it back to backlog; a large unowned backlog count indicates grooming isn't happening regularly. Use **Deep Analysis** to get an AI-generated read on which ownership gaps need immediate action and which are symptoms of intake-process problems.

P90 cycle time

90 percent of tickets resolve faster than this number. The P90 is what you use for setting honest expectations with stakeholders -- "most issues close within X days" -- without being misled by the easy ones.

Pending invites

How many invitations you've sent that haven't been accepted yet. A consistently high number is usually a sign that invites are landing in spam filters or that recipients need a nudge.

Period start

The starting date of the rolling window the escalation scores cover. Helps reviewers understand whether they're looking at last week's snapshot or a quarterly summary.

Permission Drift — Category Breakdown (mini)

Splits recent permission-related events into four buckets: user, field, issue type, and project. A sudden field-level spike without related user changes often means someone restructured the permission scheme, making this a useful daily compliance touchpoint.

Permission drift events

How many access-related changes have occurred over the recent window -- users added or removed, fields and project permissions altered, issue type permission schemes touched. Spikes are worth a quick second look from a compliance perspective.

Permission Drift Score by Actor

Per-actor composite score that rolls multiple permission-drift signals -- reporter changes, bursts, cross-project spread, self-assignment hotspots, churn -- into one value, so investigators don't have to correlate them by hand. High scores suggest a pattern rather than coincidence; mid-range scores warrant monitoring. Most actors sit safely low; the top few are where targeted investigation pays. Use **Deep Analysis** to walk through each high-risk actor's signal mix and where to focus.

Permission-change score

A composite score capturing how much your permission landscape moved in the window, weighting larger-blast-radius events more heavily than routine adds. Useful for setting an internal threshold for "review this week".

PII Detection Trend

Time series of detected personal-data events as a percentage of total activity rather than an absolute count -- the more meaningful view, since a busier portfolio naturally has more incidents. A falling rate as activity grows means hygiene is improving; a rising rate signals that the share of work generating exposure is increasing. A stable rate is a steady-state problem worth a focused intervention. Use **Deep Analysis** to see what's driving the direction and what to do about it.

PII Detections by Project

Distribution of detected personal-data events across your projects, broken down by type (email, phone, name, IDs). A project with a high count has a data hygiene problem -- personal data is routinely landing in issue text that should stay out of the tracker. The dominant type tells you where to focus training. A project with zero detections may be very clean or simply low on text, so context matters. Use **Deep Analysis** to see which projects to prioritize and the highest-impact training topic.

PII Risk by Actor

Per-actor count of PII-containing events, meant to show where targeted awareness training will help most. Most people include personal data accidentally, often by copy-pasting from emails or support tickets -- this is not a disciplinary ranking. A disproportionately high count is a candidate for one-on-one guidance; an even spread argues for team-wide training. Declining counts on an actor show prior guidance landed. Use **Deep Analysis** to see which actors to approach individually and which patterns suggest a team-level fix.

PII Severity Score Distribution

Distribution of current PII detections across four severity tiers, from Critical down to Low. Not every detection needs the same urgency -- a phone number in a closed ticket differs from email-plus-phone in an active one. A high Critical count needs immediate action; a large Medium count is lower urgency but still needs a remediation plan, since the data persists after closure. Use **Deep Analysis** to see which Critical entries to triage first and what a remediation queue should look like.

PII Type Distribution

Breakdown of all detected personal-data events by type across the portfolio. Not all PII carries the same risk, so understanding the dominant type focuses remediation. A portfolio dominated by emails usually points to copy-paste from customer communications; a high share of numeric IDs may mean reference numbers used as shortcuts. An even spread suggests multiple independent sources needing a broader review. Use **Deep Analysis** to see the likely sources of each type and the most leveraged remediation.

Portfolio Activity Density

Project-by-week heatmap showing raw event counts per project per ISO week -- absolute activity volume rather than anomaly scores. Enables executives to compare team workload side by side and detect under- or over-activity across the portfolio. A project consistently dark may be understaffed for its scope; one consistently bright may be carrying disproportionate load relative to its peers. Use **Deep Analysis** to get an AI-generated read on which workload distributions look intentional versus accidental.

Portfolio Actor Coverage

Bar chart of unique active actors per project per week plus an events-per-actor ratio. Identifies projects with too few contributors (concentration risk) -- a project with very few active actors is fragile to individual unavailability, a key bus-factor risk for the portfolio. A project where the events-per-actor ratio is very high suggests work is concentrated on a small number of people. Use **Deep Analysis** to get an AI-generated read on which projects' staffing pattern warrants resilience attention.

Portfolio Anomaly Heatmap

A color-coded grid of every project against every week in one executive view. A whole row of dark cells is a chronic governance failure needing escalation; a whole dark column points to a portfolio-wide event such as a deployment or process change. Use **Deep Analysis** for a read on which patterns are chronic versus episodic and where leadership attention pays best.

Portfolio avg score

The average risk score across all your tracked projects -- a one-number portfolio health summary. Useful as the headline metric for monthly business reviews.

Portfolio Composite Score

A blended governance score across the portfolio. The single number used to communicate governance posture to executives and external reviewers.

Portfolio Health Score Matrix

A project-by-dimension heatmap scoring every tracked project on velocity, quality, and compliance on a 0-100 scale. A project dark across all three warrants immediate leadership attention; bright in velocity but dark in quality often means a team shipping fast while accumulating rework debt. Most useful as the opening visual in monthly business reviews. Use **Deep Analysis** for a read on which health patterns are most diagnostic and which conversations to prioritize.

Portfolio Issue Type Mix

Stacked 100 percent bar chart showing the share of each issue type (Bug, Story, Task, etc.) per project, using unique-issue counting so each ticket is counted once. A project dominated by Bugs is spending capacity on remediation rather than value delivery; a sudden shift in the mix usually signals either a quality regression or a deliberate cleanup cycle. Useful for executive scoping conversations -- the mix tells you what each project's quarter is actually about. Use **Deep Analysis** to get an AI-generated read on which projects' mix shape argues for what kind of investment.

Portfolio Quality Index

Weekly quality index per project (0-100, computed as 1 minus the reopen rate multiplied by 100). Normalises across projects of very different scale so cross-project comparison stays apples-to-apples. A sustained downward trend on any single project is the leading indicator of structural quality drift; both lines climbing together is what successful quality interventions look like. Use **Deep Analysis** to get an AI-generated read on which projects' trajectories look durable versus tactical.

Portfolio Resolution Velocity

Grouped bar chart of weekly resolved-issue count per project, with week-over-week delta. Enables cross-portfolio execution comparison and surfaces acceleration or deceleration patterns at a glance. A project resolving more this week than last is healthy momentum; sustained deceleration usually points at a capacity, dependency, or scope issue worth investigating. Use **Deep Analysis** to get an AI-generated read on which projects' velocity shape is most diagnostic of an underlying constraint.

Portfolio Risk Composite Trend

Multi-line time series of composite risk score per project per week, with a 70-point reference line marking the escalation boundary. The single most-watched line on the executive dashboard -- if it's trending down sustainably, the underlying interventions are working. Projects crossing the 70-point line are the agenda for the next governance review; projects approaching it without crossing yet are where preventive attention has highest leverage. Use **Deep Analysis** to get an AI-generated read on which projects' trajectories warrant escalation versus monitoring.

Post-Creation Priority Changes

Tracks priority changes after ticket creation, separating escalations from de-escalations. Some change is normal, but a systematic pattern of de-escalations can signal priorities inflated at creation and quietly downgraded, or sprint pressure driving deprioritization to dodge missed-target accountability. A balanced, low-volume distribution indicates stable, accurate initial prioritization. Use **Deep Analysis** for an AI read on which projects show priority gaming and how to address it.

Priority and Assignee Drift by Project

A grouped bar chart per project showing counts of assignee and priority changes, ranked by a composite drift score. High assignee drift often means ownership is unclear at planning; high priority drift suggests priorities aren't well-calibrated at sprint planning, while low scores mark well-planned, well-owned projects. Use **Deep Analysis** to get an AI-generated read on which projects' drift is structural and what planning intervention it argues for.

Priority deviations

Number of priority field changes that happened after ticket creation. Some are inherent to triage; persistent activity is often used to game SLA tracking, not to reflect changing urgency.

Priority Escalation Tracker

Tracks tickets whose priority has been escalated after creation -- a signal that something was underestimated when the ticket was opened. Frequent escalations on a single ticket usually mean the team is using priority changes to express urgency the original spec missed. Tickets that escalate often go through more reopens and reassignments than the average -- the priority change rarely solves the underlying scope problem. Use **Deep Analysis** to get an AI-generated read on which escalations are catching real urgency versus papering over a scoping miss.

Priority-Adjusted Breach Heatmap

Heatmap of SLA breach rate by priority. SLA breaches are not uniformly distributed -- a project meeting its SLA for Medium tickets but consistently breaching on High priority has a specific capacity problem at the top of the queue, not a general delivery problem. Healthy patterns put breach rate inversely to priority (Highest breaches least); inverted patterns reveal escalation paths that aren't actually accelerating delivery. Use **Deep Analysis** to get an AI-generated read on which priority+project cells are most diagnostic and what they call for.

Project Configuration Mutation Log

A table of project configuration mutations — the "where" view of schema governance. Projects with disproportionately high field or issue-type change counts are candidates for a schema review, while the Global row typically reflects bulk migrations or system-level changes worth understanding. Use **Deep Analysis** to get an AI-generated read on which projects' mutation pattern looks healthy and which need cleanup.

Project events

Configuration events touching projects themselves -- creating, archiving, renaming, or reassigning a project's lead. Less frequent but each event tends to have a bigger downstream effect on the dashboard.

Project events

Permission events at the project level -- adding admins, changing roles, reassigning project lead, updating permission schemes. The biggest blast-radius category; each one is worth a quick look.

Project Ranking

Leaderboard ranking every project by its current weekly compliance score. Green is above 80 (on target), amber is 60-79 (watch), red is below 60 (immediate attention). Distinct from the Executive Portfolio Health Matrix in section 4 -- that is multi-dimensional health; this is a single-metric compliance ranking designed for the compliance team's weekly cadence. The top is good practice worth sharing; the bottom is the agenda for the next focused review. Use **Deep Analysis** to get an AI-generated read on which projects are on the move (up or down) and what's driving each direction.

Project Risk Ranking

Ranked table of every tracked project by current composite risk score -- columns include project name, current score, week-over-week change, and risk tier (Low, Medium, High, Critical). The top of the list is where leadership attention pays best; the bottom often hides hidden good practice worth sharing. Negative week-over-week changes flag positive momentum; large positive changes are the early warning signal before the trend chart shows it clearly. Use **Deep Analysis** to get an AI-generated read on which projects on the move warrant the most attention.

Projected Weekly Load per Project

Grouped bar chart showing actual 4-week average weekly event load per project alongside a 2-week forward projection based on the recent trend slope. Identifies projects growing their operational footprint and lets capacity planning prepare. A projected load significantly above current average is a "you'll need help" signal; one trending down may be intentional focus or quiet stalling. Use **Deep Analysis** to get an AI-generated read on which projected loads warrant staffing conversations and which are noise.

Projects below floor

How many projects are currently scoring below the agreed compliance floor. The action-list for the next governance review.

Projects Improving

How many projects are showing meaningful risk-score improvement. The good-news counterpart to the High/Critical KPI; the two together give a balanced executive picture.

Projects in escalation

How many of your projects are currently scoring above the escalation threshold. Zero is the goal; non-zero is the agenda for the next leadership review.

Projects on board alert

Of those escalating, how many have crossed into the higher "board alert" threshold. These are the projects that warrant explicit sponsor attention, not just team-level firefighting.

Projects Tracked

How many projects MetaFrazo currently has under analysis for your organization. Sanity check before drawing portfolio-level conclusions.

Projects with PII

Number of distinct projects where sensitive data has been detected. Useful as a scoping number -- a problem in one project is contained, the same problem across ten projects is a process gap.

Projects with recurrence

How many of your projects show measurable incident recurrence in the window. Zero is exceptional; a small steady value is normal; growth indicates accumulated technical or process debt.

Rapid Multi-Transition Issues

Weekly stacked bar chart of tickets that recorded two or more status transitions within an hour, bucketed by intensity. The pattern is hard to spot manually since each transition looks normal alone -- it only shows in the gaps between events. High-intensity tickets rarely have a work history matching their state history, and clusters near deadlines reflect pressure to clear tickets. Use **Deep Analysis** to see which tickets to spot-check first and the likely process gap.

Rapid-transition issues

Number of tickets that have undergone many status changes in a short window -- often an early sign someone is using the workflow to "look busy" rather than to manage work.

Reassignment Frequency Heatmap

Heatmap of when ownership changes are concentrated by day-of-week and hour. Consistent Monday peaks are normal sprint-planning reassignments and not a governance concern; spikes on unexpected days (mid-sprint, weekends) warrant investigation -- something disrupted normal workflow. Weeks with near-zero activity are baseline periods useful for calibrating what normal looks like. Use **Deep Analysis** to get an AI-generated read on which spikes are planning, which are firefighting, and which suggest structural ownership confusion.

Recurrence Rate & MTBI by Project

Per-project recurrence rate (share of resolved tickets that came back) alongside mean time between incidents. A recurring incident is a systemic failure left unaddressed at its root. High recurrence with low time between incidents is active firefighting; high recurrence with long gaps usually reflects scheduled-but-incomplete fixes. Use **Deep Analysis** for a read on which projects' recurrence is structural and the likely root cause.

Recurring Signature Detector

A list of tickets resolved and reopened at least twice: the chronic repeat offenders consuming disproportionate team capacity. A ticket reopened multiple times is direct evidence the underlying problem hasn't been permanently fixed. Investigating the top one or two often reveals a deeper quality or specification issue that, fixed at the root, prevents reopens elsewhere. Use **Deep Analysis** for a read on common patterns across the top offenders and the likely shared root cause.

Red signals

How many of today's automatic checks are flagging immediate attention (e.g. high-priority tickets without an owner, SLA breaches, sprint carryover, reopen surges, or issues stuck in the same status too long). A non-zero count is something to act on this morning.

Release Cadence

Trends how often releases or release-like events happen across your projects. Less about counting releases and more about spotting whether your delivery cadence is steady, accelerating, or has gone silent. Sustained slowing usually precedes a planning conversation; sudden acceleration may reflect a release-train change that the team is adjusting to. A steady cadence is the desired pattern for teams targeting DORA elite metrics.

Reopen Destination Status Distribution

Horizontal bar chart of where reopened tickets land: To Do (coral) means full restart, In Progress (teal) means picked up immediately. Bars labeled with absolute count and percentage; a warning banner appears when total reopens are under 20 (small samples should be directional only). 100 percent of reopens landing in To Do indicates full rework cycles with significant capacity implications; a mix suggests inconsistent rework handling across teams. Use **Deep Analysis** to get an AI-generated read on whether your destination pattern fits your team's intended rework strategy.

Reopen Rate by Project

Horizontal bar chart ranking each project by reopen rate -- each bar shows total completions and total reopens side by side. Projects above 15-20 percent reopen rate warrant a direct process review; projects at zero may be genuinely clean or closing tickets that were never actually completed, both worth a spot check. A large gap between projects on the same team suggests process consistency issues that culture or coaching can address. Use **Deep Analysis** to get an AI-generated read on which project comparisons are most diagnostic and what to investigate.

Reopen Rate Momentum

Whether the reopen rate is accelerating, holding steady, or decelerating. Negative momentum is healthy; positive momentum is the early-warning indicator a quality conversation should follow.

Reopen Rate Momentum Indicator

Multi-line chart of weekly reopen rate per project. A rising line is deteriorating quality; a falling line is improvement. Most diagnostic on month-over-month timeframes -- weekly noise tends to obscure underlying trend. A project whose reopen rate doubles over a quarter likely has either a scoping problem or a definition-of-done problem; the chart shows the trajectory but the deep_analysis helps identify which. Use **Deep Analysis** to get an AI-generated read on the underlying driver of your current trajectory and where to intervene.

Reopen Rate Over Time

A line chart of weekly reopen rate, with each point sized by the number of completions that week. A rate rising over multiple weeks indicates a systemic problem, while spikes in specific weeks often track sprint pressure or deadline crunch; a sustained decline is strong evidence that process improvements are working. Use **Deep Analysis** to get an AI-generated read on which weeks' rates are signal versus noise and what to address.

Reopen Rate Surge

Detects projects whose reopen rate jumped significantly week-over-week. A 35 percent rise in one project often points to a code change that shipped broken or a regression in a release candidate. Multiple projects surging at once usually indicates a systemic event, such as a deployment or process change, rather than independent issues.

Reopen Surge Detector

Measures the acceleration of reopens, not just the count, so a project that doubled its reopen rate in two weeks stands apart from one steady for months. A week-over-week jump above 50 percent is high-priority; several projects accelerating at once usually signals a systemic event rather than isolated failures. Use **Deep Analysis** for a read on which projects' acceleration looks structural and what to investigate first.

Repeat Reopener Actor Fingerprint

A ranked table of which actors triggered the most reopen events, with reopen count, unique issues reopened, and share of all reopens. Reopening isn't inherently wrong, but when one actor accounts for most reopens it's worth investigating; a spread across many actors points to a process-wide problem instead. Use **Deep Analysis** to get an AI-generated read on which actor patterns warrant a one-on-one and which suggest a team-level fix.

Reporter field changes

How often the reporter field on tickets gets changed after the fact. Most legitimate cases involve handoffs (an assistant filing on behalf, an automation correcting itself); persistent activity by one actor is worth a closer look.

Reporter Field Changes Over Time

Time series of how often a ticket's reporter field is changed after creation. In normal workflows the reporter almost never changes -- when it does, someone may be retroactively reassigning accountability for who identified a problem. Any significant volume is unusual and warrants investigation; spikes often track reviews, incidents, or deliberate record manipulation. Use **Deep Analysis** to see whether the pattern is automation, legitimate handoff, or worth a closer look.

Resolution Velocity & Projection

Multi-line chart of weekly resolved-issue counts per project. Reveals whether velocity is accelerating, stable, or declining across the portfolio at a glance. A project with consistently rising velocity is investing successfully; one with declining velocity is the natural candidate for a capacity or scope conversation. Use **Deep Analysis** to get an AI-generated read on whether the current trajectory supports next quarter's commitments.

Resolved-Without-Worklog by Project

Per-project rate of tickets resolved without any worklog entry. A worklog is the primary evidence that work actually happened, so a high unlogged-resolution rate flags audit gaps where there's no documented effort behind a resolution. Consistently logged projects meet their time-tracking compliance requirements. Use **Deep Analysis** for an AI read on whether your gaps cluster around specific issue types, actors, or time pressures.

Risk Cluster by Issue Type

Heatmap of average risk score by project-and-issue-type combination -- reveals where governance risk concentrates. In some projects Bugs carry the highest risk; in others it's Stories or Tasks. Bright cells are diagnostic targets for governance attention -- a project where Bug risk dominates likely has a quality and triage problem; one where Story risk dominates has a scoping or specification problem. Use **Deep Analysis** to get an AI-generated read on the most concentrated risk cells and what category of intervention they call for.

Risk Distribution by Project

Distribution of risk scores across all open tickets per project. Individual high-risk tickets are problems; a project where most open tickets have elevated scores is a structural governance failure. Projects with a long right tail (a few very-high-risk tickets) need targeted intervention; projects with a wide spread need structural review. A tightly-clustered low-score distribution is healthy. Use **Deep Analysis** to get an AI-generated read on which projects' shape signals a localized problem versus a structural one.

Risk Improvement Trajectory

Diverging bar chart of four-week change in composite risk score per project: bars right (positive) mean risk increase, bars left (negative) mean improvement. Ordered by magnitude of change. Useful for both celebrating wins (the leftmost bars) and identifying interventions worth replicating; useful for spotting projects that quietly accumulated risk while no one was watching (the rightmost bars). Use **Deep Analysis** to get an AI-generated read on which improvements are durable versus tactical and which deteriorations look structural.

Risk Reduction Signals

Detects weeks where multiple risk indicators improved at once, evidence that an intervention is having a real effect rather than a temporary dip. It's the good-news complement to the escalation timeline, useful for sharing post-mortems on what worked. Use **Deep Analysis** for a read on which projects' improvements look durable versus which are tactical and may regress.

Risk Signal Decomposition

Stacked bar showing weekly risk signal contributions per project: bypass events, reopen events, stuck issues, and configuration drift. Each segment's size shows its proportional contribution to the total score, so a steady total can mask a shift in composition (delivery risk turning into compliance risk, for example). Pick a single project to see its weekly timeline; unfiltered shows latest values across the portfolio. Use **Deep Analysis** to get an AI-generated read on which signal mix is doing the heavy lifting on the top projects.

Rubber-stamp issues

Tickets that show signs of rubber-stamp closure -- closed fast, with no review activity, often by the same actor who opened them. The most visible signature of process bypass.

Schema Change Category Breakdown

A proportional split of permission-drift events by category (field, user, project, scheme). If field changes dominate, focus on schema governance; if user events dominate, access management is the priority; a category above most of all drift is the period's primary focus, and a sudden shift in the dominant category signals changing admin behavior worth investigating. Use **Deep Analysis** to get an AI-generated read on what's likely driving your current category shift and where to direct review attention.

Segregation of Duties Violations

Tracks the rate of segregation-of-duties violations over time -- cases where the same actor created a ticket and was the sole contributor to its closure. The person who creates work shouldn't be the only one approving its completion, so a rising rate is the early signal that independent review is breaking down. A clean trend means two-actor closure is the norm. Use **Deep Analysis** for an AI read on whether your pattern is improving, deteriorating, or steady, and which projects or roles drive most violations.

Self-assign hotspots

Projects where self-assignment (an actor picking up a ticket without it being explicitly assigned to them) happens disproportionately often. Healthy in self-organising teams; suspect in teams with formal assignment processes.

Self-Assignment Rate by Project

Per-project rate at which actors assign tickets to themselves rather than receiving an assignment. Self-assignment isn't inherently wrong, but a consistently high rate may mean formal allocation is being bypassed or access is too permissive, while a very low rate may mean managers over-control allocation. Comparing projects shows whether differences are cultural or compliance gaps. Use **Deep Analysis** to see whether the variance reflects healthy team design or a process gap.

Self-chain actors

How many contributors have moved one of their own tickets through multiple statuses without anyone else's involvement. Not always a problem, but a useful early signal of single-actor-completion concentration.

Self-Chain Detection

Table of tickets where every status transition in a week came from a single actor, with two or more hops recorded -- so the separation of duties compliance relies on was entirely absent. A high hop count over a short span is the most critical pattern, and actors recurring across weeks warrant review. An empty log means every ticket had at least two distinct actors, a healthy sign. Use **Deep Analysis** to see which actors warrant a conversation and the likely process gap.

Single-Actor Resolution Rate

Weekly dual-line chart comparing closures where the creator also closed the issue against closures involving at least one other actor. A solo close means no independent review and no peer validation -- the most basic form of missing approval. A rising solo-close rate signals deteriorating approval discipline; a consistently low rate is the target. Use **Deep Analysis** to see whether the pattern is role-driven or culture-driven, and which actors warrant a closer look.

SLA Breach Clock

Aggregate exposure summary: distribution of open tickets across four SLA-consumed bands -- on track, caution (50-75 percent), warning (75-100 percent), in breach (over 100 percent). The leadership-briefing version of the per-ticket triage table. A clean profile with most volume in "on track" and a small tail in "caution" is normal; a fat warning band means the next week will see a lot of breaches if nothing changes. Use **Deep Analysis** to get an AI-generated read on which warning-band tickets are most likely to breach and what to prioritize.

SLA Breach Escalation Alert Log

Compliance audit log: every resolved ticket that exceeded its SLA target, with the breach multiplier (a ticket that took 2.8 days when the SLA was 1 day shows as 2.8x). For regulatory compliance, this log provides the evidence that breaches occurred and documents their severity. Useful for incident retrospectives and reviewer requests; pairs with the breach-clock for a complete past-and-present view. Use **Deep Analysis** to get an AI-generated read on the worst-multiplier breaches and what process gap they all share.

SLA Breach Log

Summarizes the last 24 hours of SLA activity: how many tickets breached and how many are at risk, meaning over 80 percent of their time budget is already consumed. A Highest-priority bug at 92 percent consumed shows amber so the team can act before it becomes a recorded breach.

SLA Breach Probability per Open Issue

Live triage view: every open ticket evaluated against its SLA target and labeled "on track", "approaching breach", "in warning", or "already breached". Not a historical chart -- a live exposure snapshot for prioritising action. A ticket showing "207 percent of SLA target consumed" isn't just overdue; depending on your compliance obligations it may require formal incident documentation. Use **Deep Analysis** to get an AI-generated read on which open tickets need escalation versus which can be resolved with a single push today.

SLA Breach Rate Trend by Project

Weekly SLA breach rate trended per project. A single high-breach week can be an anomaly; a multi-week increasing trend is systemic. Catching the deterioration early -- before three consecutive bad weeks -- is the whole point. Projects trending up from 3 percent to 12 percent over two months usually have either a capacity or an SLA-aggression problem, each with different fixes. Use **Deep Analysis** to get an AI-generated read on which projects' trend looks recoverable versus structural.

SLA Compliance by Issue Type

Compliance percentage per issue type against type-specific SLA targets. An 80 percent rate for Bugs means 1 in 5 bugs is taking longer than target -- across a quarter, that accumulates into a backlog of overdue fixes and a visible quality problem. Types below 80 percent have a structural delivery problem requiring process redesign; types above 90 percent indicate strong SLA discipline; a high-compliance type with a rising breach trend is heading toward a problem worth catching early. Use **Deep Analysis** to get an AI-generated read on which type's compliance trajectory is most diagnostic.

SoD violation rate

Percentage of analysed actors flagged as participating in patterns that violate segregation-of-duties expectations (for example, both opening and closing the same ticket). Useful as a single headline compliance number for review meetings.

Solo-close rate

Share of tickets that were resolved end-to-end by a single contributor with no other actors involved. Tells you how much of your work is genuinely two-person-reviewed versus relying on the original author's judgement.

Spike Alert Log

Chronological log of every statistically significant backlog spike with date, project, volume, baseline at the time, and severity (Critical above 3x baseline, High 2-3x). Useful for post-incident reviews and capacity-planning retrospectives; provides compliance evidence of when demand anomalies occurred and how they were classified. A log with no entries in a period indicates stable, predictable inflow -- a positive governance signal. Use **Deep Analysis** to get an AI-generated read on patterns across recent alerts and which alerts warrant a formal review write-up.

Spike Severity Ranking by Project

Ranks projects by peak-to-baseline ratio so you compare apples to apples -- a project handling 20/day getting 25 is fine, while a project handling 5 getting 25 is in crisis. A 3x or higher ratio indicates a genuinely extreme backlog event, not just a busy period. Projects with low ratios, even at high absolute volume, are managing demand predictably. Comparing ratios across the portfolio identifies which team needs immediate triage support. Use **Deep Analysis** to get an AI-generated read on which project's spike is structural versus situational.

Sprint carry-over

The share of work items that rolled over from the previous sprint into the active one. Useful for measuring planning accuracy and execution capacity; sustained high values mean the team is consistently overcommitting.

Sprint Carry-Over Rate

A per-sprint stacked bar showing tickets completed before sprint close versus those carried over, with KPI badges for latest carry-over percentage, trend, and sprint count. A sustained high rate signals sprint scope that's consistently too ambitious, and a growing rate is more concerning than a stable one; a declining trend is clear evidence that planning discipline is improving. Use **Deep Analysis** to get an AI-generated read on whether your trend is recoverable, structural, or already healing.

Sprint Carryover Signal

Shows the active sprint's carry-over percentage with context against the last six sprints. A 47 percent carry-over above a 30 percent team average surfaces amber to flag a scope conversation for the next planning meeting; "highest in 6 weeks" is the early-warning callout.

State-Skip Detection

Horizontal bar chart of tickets that jumped straight from an open state to a terminal state in one step, skipping every intermediate stage. State-skipping is one of the clearest signs of governance failure: either the ticket was never worked, or its history was made to look complete. Persistently high projects need a workflow review; a single spike is often a bulk-close worth checking. Use **Deep Analysis** to see which projects to prioritize first.

Status Bottleneck Heatmap

Portfolio-level heatmap with projects on rows and statuses on columns -- cell color encodes the average time in that combination. A single dark cell is project-specific (something about how that team handles that stage); a dark column (same status slow across many projects) points to a shared process constraint; a dark row (all stages slow for one project) indicates a capacity or maturity problem specific to that project. Use **Deep Analysis** to get an AI-generated read on which bottleneck shape your portfolio shows and where leverage is highest.

Status Category Drift

Flags status names that map to more than one Jira status category across your workflows: silent drift that breaks category-pivoted queries, automations, and reports. A status mapping to two categories is bad; three categories is severe.

Status Category Drift

Table of status names that map to more than one Jira status category (To Do, In Progress, Done) across the tenant's workflows. Each row is a silent configuration drift signal with severe impact: filters return different result sets depending on the source workflow, and category-based reports quietly double-count or miss work. A status mapping to three categories is more severe than two. Use **Deep Analysis** for an AI read on each drifting status, the human impact, and the most likely remediation.

Status Category Drift Matrix

A status-name by project matrix for the status names that drift across status categories. Each cell shows the category (To Do, In Progress, Done) that one project's workflow assigns to that status name, color-coded so disagreements stand out at a glance -- and links straight to that project's workflow configuration. Where the flat drift list tells you a name is inconsistent, the matrix tells you exactly which workflow says what, so you know which one to fix.

Status Transition Frequency

Shows the most-used status names over the last 12 months. Near-duplicates with different casing or spacing ("Ready for review" vs "READY FOR REVIEW") are a textbook drift signal; an excess of Done-bucket statuses ("Done", "Completed", "Resolved", "Closed") in the top 10 means status names are being used where Resolution values should be.

Status Transition Frequency

Bar chart of the most-used status names across all transitions in the last 12 months, ranked by how often work actually lands in each. Near-duplicates differing only in casing or spacing are a textbook drift signal, and a crowd of Done-style statuses usually means status names are being used where resolution values should be. Obscure high-count statuses are often project-specific stages worth consolidating. Use **Deep Analysis** for an AI read on specific consolidations to recommend by name.

Status-vs-resolution sprawl

How many distinct Done-bucket status names the team uses compared to how many distinct Resolution values are in play. A high ratio is a sign that status names are doing the work Resolution should be doing — different shades of "done" expressed as separate statuses — which makes reporting and JQL brittle.

Status-vs-resolution sprawl

How many distinct Done-bucket status names the team uses compared to how many distinct Resolution values are in play. A high ratio is a sign that status names are doing the work Resolution should be doing -- different shades of "done" expressed as separate statuses -- which makes reporting and JQL brittle.

Stuck Status Detector

Surfaces tickets that have stayed in the same status well past a healthy duration: items waiting on review for days, or sitting in QA without progress. Catching these early helps you clear ticket-flow bottlenecks before they affect a whole sprint.

Stuck-in-Status Detector

Open tickets stuck in the same status well past the normal time for that stage, ranked by how overdue they are. Velocity charts miss these because the tickets aren't moving, yet they're still delivery blockers. A cluster stuck in one status points to a shared bottleneck; a stuck ticket with no assignee is the highest priority. Use **Deep Analysis** for a read on which to triage first and the bottleneck driving the rest.

Team Engagement & Activity Mix

Breakdown of all activity events by type -- status changes, comments, worklog entries, field updates, others. A team can be very busy in Jira while tickets barely move; a healthy mix shows status changes as the dominant activity. High comment-to-status-change ratios often signal unclear requirements or ongoing back-and-forth slowing delivery; very low worklog activity alongside high status changes can suggest time isn't being tracked accurately. Use **Deep Analysis** to get an AI-generated read on whether your team's activity mix points to clear scope, scope drift, or tracking gaps.

Time-to-First-Move Distribution

A histogram of time from ticket creation to first status transition, bucketed from under a day to over a week, with median and P90 badges. A median first-move above two days suggests intake isn't being triaged fast enough; a large long-tail bucket points to a bimodal pattern where some tickets are picked up immediately and others are systematically ignored. Use **Deep Analysis** to get an AI-generated read on what's driving your long tail and how to compress it.

Top 10 Workflow Paths

Ranked bar chart of the most-traveled full transition sequences -- the actual journeys tickets take from creation to close. If your intended workflow is To Do -> In Progress -> Review -> Done but the top path skips Review, that's a process-compliance signal. Short or incomplete paths in high volume suggest tickets are being closed without proper progression; long or looping paths indicate rework. A top path matching your defined workflow means the process is well-followed. Use **Deep Analysis** to get an AI-generated read on the gap between your intended and actual paths.

Top Active Custom Fields

Shows the custom fields receiving the most write activity recently, helping you distinguish fields doing real work, worth careful change management, from clutter worth retiring. A field with "test" or "TDG" in its name still receiving edits in production is a likely retirement candidate.

Top Active Custom Fields

Ranked bar chart of the custom fields receiving the most write activity over the last 12 months. A few fields doing most of the work is normal; a long tail of barely-touched fields is clutter worth retiring, and generic or test-like names still seeing production edits are especially worth checking. Note that low-activity fields used by automations or integrations can still be load-bearing, so verify before deleting. Use **Deep Analysis** for an AI read on concrete retirement candidates by name.

Top Actor Concentration

How much of the team's activity is driven by the top few contributors. High concentration is a key-person-risk signal; moderate concentration is healthy and reflects natural specialization.

Top bottleneck

The status where tickets currently spend the most time on average. The highest-leverage spot to fix if you want overall cycle time to drop; usually a review, approval, or test step rather than active development.

Top Deviation Actors

Ranked table of actors with the highest aggregate control-deviation activity -- unlogged resolutions, batch closes, direct skips, and other bypass signals combined -- so compliance conversations can be directed appropriately. Actors high across multiple types are systematic bypassers; an actor high on one type may simply have a knowledge gap better closed with targeted guidance. A clean table indicates strong individual-level compliance. Use **Deep Analysis** for an AI read on each top actor's signal mix and what to address with them.

Top Reopened Issues (mini)

Lists the tickets reopened most often recently, often the single most diagnostic signal about quality and specification problems. Investigating the top one or two usually reveals a deeper root cause that, once fixed, prevents reopens across many other tickets.

Top Solo-Close Actors

Ranked table of actors with the highest solo-closure counts, their solo-close percentage, primary project, and a risk tier. Concentrated approval gaps often reflect a specific role or process issue rather than team culture. A high count with a high percentage may mean someone is deliberately bypassing approvals; a high count with a low percentage may just be a very active person, so context matters. Use **Deep Analysis** to walk through each top actor's pattern and what's likely driving it.

Total assignment events

Total number of assignment-change events recorded across your projects. The raw volume baseline behind the more refined volatility signals below.

Total breach events

The total number of SLA breach events recorded in the window. The historical count for retrospective conversations rather than today's count of open breaches.

Total completions

How many tickets reached a completed status in the window (the denominator for the reopen-rate calculation). Useful sanity check before drawing conclusions from the rate.

Total config events

Total count of configuration-affecting events in the window across all categories (field edits, user changes, project changes, scheme changes). A single number useful for spotting unusually busy admin periods.

Total members

The total number of people in your organization with a MetaFrazo account, including owners, admins, and regular members. Useful for tracking team growth and reconciling your roster against your identity provider.

Total open

The total number of open tickets across your tracked projects. A standing baseline you can compare week-to-week to spot growth, and the denominator behind several of the other backlog signals.

Total PII events

Total count of detected sensitive-data events in the window. The raw inflow number; the severity breakdown below tells you how worried to be.

Total projects tracked

How many projects MetaFrazo is currently tracking for your organization. Useful as a sanity check before drawing portfolio-level conclusions: an average across three projects is anecdotal, across thirty is meaningful.

Total reopens

The absolute number of reopens recorded in the window. Pairs with the rate KPI -- a 20 percent reopen rate over 50 completions is anecdotal, over 500 completions is structural.

Total scored issues

How many tickets have a recent risk score recorded. The denominator behind the rest of the section's signals.

Total spikes

The total number of backlog-spike events detected in the window across all your projects. A single number that summarises how stable your inflow has been recently.

Total unowned issues

How many tickets currently have no assignee. Some unowned tickets are normal (fresh intake awaiting triage); a growing pool that doesn't drain is a sign triage capacity is the bottleneck.

Transition Volume Over Time

Timeline chart of daily status-transition counts across your connected projects. Spikes often correlate with sprint closures, release pushes, or catch-up periods -- check whether work quality holds during the peaks. Sustained drops that don't match known holidays or planned downtime are worth investigating: teams may be blocked or disengaged. A steady, consistent rhythm without extreme peaks or valleys is the healthiest pattern. Use **Deep Analysis** to get an AI-generated read on whether your current rhythm reflects healthy delivery or a problem worth catching early.

Transitions Events Table

Detailed event log with one row per status-change event -- columns include issue key, summary, from status, to status, triggered by, and timestamp. This is the audit log behind every aggregated chart above; it's the starting point for any investigation that begins with "let me check what actually happened". Filter by actor, ticket, project, or window to drill into specific patterns the charts surfaced. Use **Deep Analysis** to get an AI-generated read on patterns in your filtered set and what they likely mean.

Trigger Attribution Breakdown

Per-project breakdown of which underlying signals are driving the composite escalation score (bypass risk, breach rate, recurrence, drift, ownership gaps). A project escalating primarily from "unowned" needs a staffing decision; one driven by "bypass" needs a process intervention; one driven by "breach" needs capacity. Telling those apart is the difference between an effective intervention and a wasted one. Use **Deep Analysis** to get an AI-generated read on each escalating project's signal mix and the intervention category it argues for.

Type Reclassification Flows

Tracks how often tickets are reclassified between types after creation, such as a Bug becoming a Task. Some reclassification is routine, but a consistent pattern reveals a gap in how work is categorized at intake. Use **Deep Analysis** for a read on whether your reclassification flows look routine or structural, and whether any pattern points to an intake-process design problem.

Unlogged resolutions

Count of tickets that were resolved with no work-log entry attached. Where time-tracking is part of the process, this is the clearest signal that effort isn't being recorded.

Unowned High-Priority Clock

Flags Highest- and High-priority tickets that currently have no assignee, with how long each has been unowned. For Highest priority, every hour without an owner is unmitigated risk; for High, the SLA window is four hours. Any Highest-priority entry here is the morning's first action: assign it now or escalate.

Unowned High-Priority Issues

Live list of Highest- and High-priority tickets currently without an assignee, with the time each has been unowned. For Highest priority, every hour without an owner is unmitigated risk; for High priority, the SLA window is four hours. Any Highest-priority entry here requires an immediate response -- assign or escalate. A cluster from one project that grows over time indicates a systemic ownership assignment problem, not isolated incidents. Use **Deep Analysis** to get an AI-generated read on what's driving the recent ownership gap and how to prevent recurrence.

Unowned highest priority

How many Highest-priority tickets are currently sitting without an assignee. Almost always zero in a healthy operation; a non-zero count is something to fix the same day.

Unowned in progress

The narrower, more urgent slice: tickets that have moved into an active status but still have no owner. Unlike fresh intake, these are work-in-progress with no accountable individual -- worth fixing the same day.

User Account Change Timeline

A weekly stacked bar of user account creations, updates, and deletions. Account changes are among the most compliance-sensitive events: deletions outside known offboarding windows and creation spikes without matching onboarding are potential audit flags, while a steady pattern aligned with HR cycles signals well-governed access. Use **Deep Analysis** to get an AI-generated read on whether recent lifecycle activity matches expected HR rhythm and which events warrant verification.

User events

Configuration events affecting accounts -- new users added, existing users deactivated, group memberships changed. The audit-relevant slice of the change stream.

User events

Count of user-account-related permission events in the window: accounts added or removed, group memberships changed, role assignments altered. Reconcile against your identity provider's off-boarding log.

User Lifecycle Heatmap

Heatmap tracking user lifecycle events (creations, updates, deletions) across weeks. Among the most compliance-sensitive activities -- a week with multiple deletions and no creations is a potential offboarding event worth verifying with HR; many updates without creations or deletions may indicate privilege changes happening without formal approval. A predictable HR-aligned pattern indicates mature access governance. Use **Deep Analysis** to get an AI-generated read on which lifecycle weeks need cross-verification with HR records.

Weekly Assignment Volatility Index

Weekly index measuring what share of assignment activity is reassignment -- work handed off again after initial assignment. A consistently high index above 30 indicates that ownership isn't being established clearly at the point of assignment; rising volatility alongside stable total activity means the team is reassigning more often rather than handling more volume. A declining index over time is evidence that ownership discipline is improving. Use **Deep Analysis** to get an AI-generated read on whether your team is structurally over-reassigning or experiencing a temporary disruption.

Weekly Cycle Time Trend

Weekly trend of median AND P90 cycle time on the same chart -- the P90 surfaces tail risk that a stable median can hide. A rising P90 with a stable median is one of the clearest early-warning signs of SLA drift: most work is still on pace but a growing subset is taking disproportionately long. Both lines declining together indicates genuine improvement across the board; a spike in both in a specific week often reflects a sprint disruption or particularly complex batch. Use **Deep Analysis** to get an AI-generated read on whether your trend is building tail risk or holding steady.

Weekly Effort Trend

Total team effort trended weekly. Distinct from output -- two teams can burn the same effort and ship very different amounts of work. Useful as the team's overall capacity-consumption signal; sustained climbs without matching throughput growth often precede burnout, retention issues, or process drift. A stable trend at a sustainable level is the goal; a downward trend during a quiet quarter is healthy recovery.

Weekly Governance Score by Project

The weekly MetaFrazo Governance Score for each project on a 0-100 scale, so you can see which projects are meeting the governance baseline and how the score is trending over time. The 70-point line marks the floor. How it's calculated: [MetaFrazo Governance Score](/help/metafrazo-governance-score).

Weekly Team Throughput by Actor

Ranked horizontal bar chart of average weekly event count per actor, with peak-week annotated. Useful for spotting volume outliers in both directions; designed for leadership conversations, not individual evaluation. Sustained increases over months often correlate with later burnout; sudden drops may indicate disengagement or a change in role. Use **Deep Analysis** to get an AI-generated read on which actors' patterns warrant a one-on-one and which look like role-appropriate cadence.

WIP Aging

Shows how long open tickets have been in their current status -- WIP age. Helps catch tickets that "look" active but haven't actually moved in two weeks. A long right tail indicates active-status work that's effectively stalled; a left-skewed distribution indicates a team genuinely working through its WIP. The view is the inverse of the cycle-time chart: instead of measuring how long completed tickets took, it measures how long active tickets have been sitting.

WIP Limit Pressure by Project

A per-project average and peak count of tickets concurrently in progress, with a pressure badge that flags projects where peak work-in-progress far exceeds the configured limit. Flagged projects are likely seeing delivery slowdowns that velocity metrics won't reveal, while projects comfortably below the limit are focused and predictable. Use **Deep Analysis** to get an AI-generated read on which projects need a structural WIP-limit conversation and which can self-correct.

WIP over limit count

How many projects are currently above their healthy WIP ceiling. If this is zero, your teams have headroom; if it's growing week over week, the system is overheating before symptoms surface elsewhere.

WIP Run Chart

Plots work-in-progress count over time. Sustained WIP above the team's healthy ceiling is one of the cleanest signals that the team is starting more than it can finish; this chart shows the trend before sprint retrospectives surface the symptom. Periodic dips below the ceiling are normal between sprints; sustained crossings indicate structural overcommitment. Pairs with the Cumulative Flow Diagram for a full picture of flow health.

Work Breakdown Structure

Hierarchical tree of work in your portfolio -- epics at the top, stories and tasks below, with completion progress per branch. The "what are we building this quarter" picture: useful both for planning sessions and for quarterly review decks. Long branches with little completion are typically scoping problems; branches with high completion but new children appearing are scope expansion. The view complements the Portfolio Health Matrix by adding the structure-of-work dimension to the cross-project scorecard.

Workflow Adherence Score by Project

Horizontal bar chart of workflow adherence score per project (proportion of issue-scoped events with documented changelog transitions, 0-100). Color-coded: green 75 or above, amber 60-74, red below 60. Useful for grounding workflow-revision conversations in data -- if adherence is below 70 percent for the same step every month, the step is the problem, not the team. Use **Deep Analysis** to get an AI-generated read on which projects' adherence pattern suggests workflow simplification.

Workflow Drift Stream

A real-time stream showing how your team's actual workflow practice is drifting from the documented workflow, with entries rolling in as new transitions are recorded. A steady upward drift trend can appear well before a release crunch, giving the team time to correct either the workflow or the practice. A quiet stream means current practice matches documentation -- the steady-state target.

Workflow Transition Matrix

Heatmap matrix with the "from" status on rows and "to" status on columns; cell color encodes how often that exact transition happened. A healthy matrix is dominated by forward diagonal movement; off-diagonal cells expose backward transitions and unintended shortcuts. High volume on "To Do -> Done" cells, for example, almost always points to skip transitions bypassing required workflow steps. Use **Deep Analysis** to get an AI-generated read on which off-diagonal cells matter most and what process gap they imply.

Workload Distribution by Project

Bar chart of total event volume per project for the selected window -- aggregates all workflow activity by project. An 80/20 split where one project dominates is often a sign that resources need rebalancing or that quieter projects are being neglected. Unexpected spikes in a previously quiet project may signal escalation or crisis; a balanced distribution typically indicates healthy portfolio management. Use **Deep Analysis** to get an AI-generated read on whether your current distribution reflects intentional focus or unmanaged drift.

Worklog Effort Distribution

Horizontal bar chart showing total worklog hours and average hours per week per actor, ranked by total hours descending. Useful as a sanity check rather than a performance metric -- two teams can burn similar effort and ship very different amounts of work. Sustained high weekly hours often correlate with later burnout; the chart pairs naturally with engagement and retention conversations. Use **Deep Analysis** to get an AI-generated read on which actors' effort patterns warrant a wellness check.

Worst breach multiplier

The largest ratio of "actual time taken" to "SLA budget" seen in the window. For example, a 4.7x multiplier means one ticket took nearly five times its allowed window -- worth a forensic timeline review.

Zero-comment close rate

Share of completed tickets that closed with no comments at all. Some zero-comment closes are legitimate (truly trivial tickets); a high rate usually means the team is skipping the documentation step under pressure.

Zero-Comment Closures by Project

Stacked bar chart per project showing the share of closed tickets that had no comments versus those closed with comments. A project where more than a small fraction of closures have zero comments has a systematic approval gap, not an isolated exception. A falling rate means review culture is strengthening; a rising one warns approval discipline is slipping. Use **Deep Analysis** to see what's driving the pattern and which projects to address first.