Use case
Use case3 min readBy The Data Workers Team

Workforce and staffing data, run by agents

Staffing, timekeeping and census data never align, so labor productivity and agency spend are reconciled after the pay period. Here is how Data Workers runs it instead.

Key takeaways

  • •The problem today. Staffing, timekeeping and census data never align, so labor productivity and agency spend are reconciled after the pay period.
  • •What changes. Agents join timekeeping, scheduling and census data daily and keep productivity metrics governed.
  • •What stays with your people. Pay-affecting data: people's pay depends on it.
  • •The result. Managers see productivity daily, not after the pay period.

The problem today

Staffing, timekeeping and census data never align, so labor productivity and agency spend are reconciled after the pay period.

What changes with Data Workers

Agents join timekeeping, scheduling and census data daily and keep productivity metrics governed.

The workflow: Connectors agent (get_connector_status) → Pipeline Building agent (generate_pipeline) → Data Context & Catalog agent (define_business_rule) → Data Access & Governance agent (provision_access).
Fig. 1:The workflow: Connectors agent (get_connector_status) → Pipeline Building agent (generate_pipeline) → Data Context & Catalog agent (define_business_rule) → Data Access & Governance agent (provision_access).

The workflow, step by step

  1. 1.The Connectors agent calls get_connector_status and lands timekeeping and scheduling data.
  2. 2.The Pipeline Building agent calls generate_pipeline and joins labor to census.
  3. 3.The Data Context & Catalog agent calls define_business_rule and governs productivity metrics.
  4. 4.The Data Access & Governance agent calls provision_access and scopes employee data.

The impact metrics we move

  • •Productivity report: from about 2 weeks to 4 h
  • •Agency spend tie-out: from about a week to ~1 h
  • •Access for a manager: from about a week to 5 min
Time to complete, current practice (black) vs. with Data Workers (green). Illustrative.
Fig. 2:Time to complete, current practice (black) vs. with Data Workers (green). Illustrative.

What goes on autopilot

  • •Labor and census join daily, so productivity is current.
  • •Employee data access follows policy, so privacy holds.

Where your people stay in charge

Pay-affecting data always go to a named approver, because people's pay depends on it. Everything starts at L1, observing only, and autonomy rises only on evidence.

The pilot

  • •Scope. Daily productivity for one unit.
  • •First week. Agents run at L1, observing only, and show the fixes they would have made, with receipts.
  • •By week two. The routine work above runs on its own; anything sensitive stays with a named approver.
  • •At day 30. We review the impact metrics against your baseline:
  • •Productivity report: from about 2 weeks to 4 h
  • •Agency spend tie-out: from about a week to ~1 h
  • •Access for a manager: from about a week to 5 min
  • •Cost. Start with a pilot ($7,500 one-time, credited in full against the first year). Scale is from $1,000 a month and Enterprise from $3,000 a month, billed annually, with unlimited seats, no usage meter and no markup on model spend. See pricing.

Related: The health systems and providers hub · The full team playbook

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