Use case
Use case3 min readBy The Data Workers Team

Data discovery for financial services, run by agents

Analysts spend days finding the right table, then learn it is stale or not approved for their use. Here is how Data Workers runs it instead.

Key takeaways

  • •The problem today. Analysts spend days finding the right table, then learn it is stale or not approved for their use.
  • •What changes. Agents keep a governed catalog of every dataset with owner, freshness, lineage and approved uses, answerable in plain English.
  • •What stays with your people. Access to restricted data: regulators expect need-to-know.
  • •The result. Analysts find the right, approved data in minutes.

The problem today

Analysts spend days finding the right table, then learn it is stale or not approved for their use.

What changes with Data Workers

Agents keep a governed catalog of every dataset with owner, freshness, lineage and approved uses, answerable in plain English.

The workflow: Data Context & Catalog agent (generate_documentation) → Quality Monitoring agent (check_freshness) → Data Access & Governance agent (check_policy) → Data Context & Catalog agent (trace_cross_platform_lineage).
Fig. 1:The workflow: Data Context & Catalog agent (generate_documentation) → Quality Monitoring agent (check_freshness) → Data Access & Governance agent (check_policy) → Data Context & Catalog agent (trace_cross_platform_lineage).

The workflow, step by step

  1. 1.The Data Context & Catalog agent calls generate_documentation and documents every dataset.
  2. 2.The Quality Monitoring agent calls check_freshness and reports freshness.
  3. 3.The Data Access & Governance agent calls check_policy and shows approved uses.
  4. 4.The Data Context & Catalog agent calls trace_cross_platform_lineage and shows lineage.

The impact metrics we move

  • •Finding the right dataset: from about 8 hours to 30 s
  • •Access to approved data: from about a week to 5 min
  • •Lineage question: from about 2 days to seconds
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

  • •Every dataset is documented as agents work, so the catalog stays current.
  • •Access follows policy, so analysts start the same day.

Where your people stay in charge

Access to restricted data always go to a named approver, because regulators expect need-to-know. Everything starts at L1, observing only, and autonomy rises only on evidence.

The pilot

  • •Scope. Catalog and access for one business line.
  • •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:
  • •Finding the right dataset: from about 8 hours to 30 s
  • •Access to approved data: from about a week to 5 min
  • •Lineage question: from about 2 days to seconds
  • •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 financial services hub · The full team playbook

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