Time and Activity: Data sources
Objective. Understand what feeds the app and how coverage and freshness decide whether any number is trustworthy - enough to catch a gap that distorts the denominator and escalate deep integration to product.
This screen answers 'where does this data come from, how complete is it, and how fresh?' It is the foundation under every attendance and utilisation figure, because a gap here silently distorts the numbers everywhere else.
See it in the demo
What this screen answers
A CSM opens this to answer 'can I trust the numbers yet, and for whom?' Sales needs awareness that every figure depends on coverage. It shows which attendance and activity sources feed the app, who is covered and how current the data is - the honesty layer that tells you whether a reading is complete or partial.
The task (validate, do not re-engineer)
Confirm coverage and freshness before trusting any reading: which sources are connected, which people have a signal, and whether the data is current for the period you intend to read. The highest-value CSM check is finding people or teams with no signal or stale data, because they distort shared denominators. Connector setup, mappings and integration are specialist and product work - capture gaps and escalate rather than attempting the integration yourself.
How to read it
The key misread is assuming a missing source reads as zero. It does not - incomplete coverage skews the denominator instead. A person with no activity signal does not show as fully idle; they quietly distort the team's available-hours base, so utilisation can be overstated or understated depending on how the gap falls. Always read a metric as 'for the covered population, as of this freshness'. A confident number over 70% coverage is a different claim from the same number over 98% coverage.
Honest limits
This is an admin screen; the specific sources, connectors and refresh behaviour for a customer must be confirmed with product, not assumed from the demo. It tells you whether data is complete and current, not whether the resulting behaviour is good or bad. Treat it as the gate before interpretation: if coverage or freshness is weak for the scope you care about, that is the finding, and the rest of the reading waits.
Interpretation check
A missing data source does not read as zero - it skews the denominator. Read every figure as 'for the covered population, as of this freshness', and check coverage before interpreting anything.
In the field
KnackGlobal (~6,000 RCM seats): across thousands of seats a few percent of uncovered agents can move a team denominator materially, so coverage validation is the precondition for the capacity-visibility claim.
Practical task
In the demo, note how you would check coverage and freshness for one team, and write a one-line trust statement: this reading is valid for X% coverage as of this date, or coverage is the finding.
Self-check
A team's utilisation looks unusually high. How could a data-source gap cause that?
People with no signal are not counted as idle; they distort the available-hours denominator. Partial coverage can overstate or understate utilisation. Check coverage before reading.
Why is 'coverage' the gate before interpretation?
Every attendance and utilisation figure is only valid for the covered, current population. Weak coverage or stale data makes the number unreliable, so that is itself the finding.
Does a missing source show as zero activity?
No. It skews the denominator rather than zeroing a person. That is why gaps are dangerous - they look like behaviour, not absence of data.
A customer needs a new source connected. What is the CSM move?
Capture the gap and escalate connector and integration work to product or the implementation specialist. CSM validates coverage; integration is specialist work.
Verified vs demo. Screen is live in the demo at /#/sources; exact controls and figures are SME-validation-pending.