backup-job-health

name	Backup Job Health
when_to_use	When assessing whether backup jobs are actually running successfully across a portfolio, rather than checking one client's one appliance in isolation. Use when: backup health, backup job failures, missed backups, backup success rate, failed backup jobs, backup storage trending, is this client's backups running, backup audit, backup job status.
description	Use this skill to assess portfolio-wide backup job health across whatever BCDR and SaaS-backup tools are connected: job success/failure rate, missed- backup detection, storage-consumption trending, and how to normalize very different vendor job models (image-based appliance backup vs. SaaS-data snapshot backup) into one comparable health view.

Backup Job Health

Overview

"Backups are running" is the single most-assumed, least-verified fact in an MSP's environment. A backup job that failed silently three nights ago looks identical, from a distance, to one that's been succeeding for months — nobody notices until a restore is needed and there's nothing current to restore from. This skill is the recurring, portfolio-wide sweep that catches that gap before it becomes an incident: job success/ failure rate, missed-backup detection, and storage-consumption trending, rolled into one normalized view across every backup and BCDR tool an org has connected.

This skill is about whether backups are happening. It is deliberately narrower than disaster-recovery readiness as a whole — it does not assess whether a backup, once taken, is actually recoverable (see restore-test-verification), and it does not assess whether the retention window or cadence in place actually satisfies a contracted requirement (see retention-rpo-compliance). Treat this as the first, most frequent layer of the DR assurance stack: if jobs aren't running, nothing downstream matters yet.

Key Concepts

Two fundamentally different job models

Backup and BCDR vendors protect data in two structurally different ways, and treating them as the same "job" concept produces misleading comparisons:

• Image-based appliance backup (e.g. Datto BCDR / SIRIS / Alto, Unitrends) — protects physical or virtual servers/workstations by taking periodic block-level or image-level snapshots to a local appliance, then syncing offsite/to the cloud. A "job" here is a scheduled backup of one protected agent/asset on one appliance. Health signals include: last successful local backup, last successful offsite sync, and (for Datto BCDR specifically) screenshot/boot verification status — see restore-test-verification.
• SaaS-data snapshot backup (e.g. Datto SaaS Protection, Spanning) — protects cloud application data (Microsoft 365 mailboxes/OneDrive/SharePoint/Teams, Google Workspace, Salesforce) by taking periodic API-level snapshots of tenant data. A "job" here is a scheduled backup pass across a set of protected seats/users for a tenant. There is no "appliance" and no local/offsite sync distinction — health signals are seat coverage (are all licensed users actually being backed up) and per-run success/failure across the tenant.

Normalize both into a single health record per protected unit (appliance-agent pair, or tenant-seat set) with the same fields — last successful run, run status, and failure streak — even though the underlying job mechanics differ. Don't force a one-size-fits-all metric like "backup window duration" that only makes sense for one model.

Job success/failure rate

For each protected unit, compute the success rate over a rolling window (default: last 30 days unless the org has a documented preference) and, more importantly, the current consecutive-failure streak. A unit with a 96% success rate over 30 days but a live 4-night failure streak right now is a more urgent problem than a unit with 90% success and no current streak — trailing averages hide exactly the thing that matters most: is it broken right now.

Missed-backup detection

A missed backup is distinct from a failed backup: a failed backup ran and errored; a missed backup never ran at all (no job execution recorded for the expected window). Both matter, but they point to different root causes — a failed job usually means an in-scope problem (disk full, credential expired, source unreachable), while a missed job often means a scheduling, licensing, or connectivity problem that's more structural (the agent/connector isn't checking in at all). Report them as separate categories rather than merging them into one "unhealthy" bucket, since the remediation path differs.

Storage-consumption trending

Track local and offsite/cloud storage consumption per appliance (or per SaaS tenant, where the vendor exposes storage/quota data) over time. Flag two distinct risk patterns:

• Approaching capacity — an appliance trending toward its local storage limit, which risks retention truncation (older recovery points get purged early to make room) even while nightly jobs continue to report success. This is a silent retention risk — see retention-rpo-compliance for how a storage-forced retention cut interacts with a contracted retention requirement.
• Anomalous growth — a sudden, unexplained jump in daily change-rate/storage consumption, which can indicate anything from a legitimate data-growth event to ransomware encryption activity happening on the protected source. Anomalous growth is worth flagging even when it isn't yet a capacity problem, because of what it might indicate about the protected system.

If no backup/BCDR tool is connected

State plainly that job health cannot be assessed: "No backup or BCDR connector is connected through the gateway, so there's no backup job data to audit." Do not fabricate success rates, job counts, or storage figures.

Common Workflows

Full portfolio sweep

1. Discover connected backup/BCDR tools via conduit__search_tools — don't assume which of Datto BCDR, Datto SaaS Protection, Spanning, or Unitrends (or others) are live for this org.
2. For each connected tool, pull the protected-unit list (appliances/agents for image-based tools; tenants/seats for SaaS-snapshot tools) and each unit's recent job history.
3. Normalize into one health record per protected unit: last successful run, current run status, consecutive-failure streak, and (where available) storage/quota state.
4. Bucket into: actively failing (current failure streak), missed (no run recorded for expected window), storage-at-risk, and healthy.
5. Report worst-first: longest active failure streak, then missed backups, then storage risk.

Targeted client check

1. Resolve the client to its protected units across whatever connected tool(s) cover them (a client may have both an on-prem appliance and a SaaS-backup tenant).
2. Pull and normalize job history for just that client's units.
3. Report success/failure rate, active streaks, and storage state for that client only.

Error Handling

• No backup/BCDR connector connected: stop and say so; do not fabricate job status.
• A connected tool doesn't expose storage/quota data: report job success/failure normally and mark the storage-trending section "unable to verify — connector does not expose storage data" rather than omitting it silently.
• A protected unit exists in inventory but has no job history at all: treat this as a missed backup, not a gap in the report — a never-run job is exactly the kind of silent failure this skill exists to catch.

Best Practices

• Always distinguish trailing success rate from current failure streak — lead with the streak, since it's the more actionable signal.
• Always separate "missed" from "failed" — they have different root causes and different remediation owners.
• State the rolling window used (default 30 days) explicitly in every report.
• Never let a healthy trailing average suppress a live failure streak in the report ordering — worst-current-state leads, not best-historical-average.

Related Skills

• Restore-Test Verification — whether a successfully-run backup is actually recoverable; this skill only confirms the job ran, not that its output is usable.
• Retention/RPO Compliance — whether the retention window and backup cadence in place satisfy a contracted requirement; relevant when storage-forced retention truncation is detected here.