Turn indexed data into monitoring dashboards and SPL — without writing SPL by hand
The Splunk Data Explorer Agent inspects what is already indexed, infers field types and likely use cases, and suggests dashboards, saved searches, and CIM mappings. Combined with DAP, the platform deploys those artefacts to your target app in the same session — so a sourcetype with one original intent often ships three to five additional monitoring use cases on day one.
Last updated .
Outcomes you can expect
- Sourcetypes ship with 3-5 monitoring use cases rather than the single original intent.
- Dashboards and saved searches land deploy-ready, with CIM mappings and scheduling defaults applied.
- DAP plans give every deployment a reviewer-approved rollout and queryable execution log.
- Coverage tracks onboarding velocity automatically through scheduled re-exploration.
Estimates based on Deslicer customer observations and Splunk practitioner experience for environments running full manual pipelines. Actual results vary by environment complexity, team size, and existing tooling.
How the workflow runs
- 1
Pick an index or sourcetype to explore
Tell the Data Explorer Agent which index or sourcetype to examine. The agent runs `| fieldsummary`, samples events, and infers field types — dates, IPs, hostnames, numeric metrics — without you having to remember the SPL incantation or the underlying field-extraction order.
- 2
Review inferred use cases
The agent proposes three to five monitoring use cases per sourcetype, ranked by signal strength against the underlying schema. Each proposal cites the field combinations that support it, so reviewers can sanity-check fit before any SPL or dashboard is generated against production data.
- 3
Generate dashboards and saved searches
For each accepted use case the agent emits a starter dashboard (Dashboard Studio JSON) and the supporting saved searches. The artefacts include scheduling defaults, severity thresholds, and CIM mappings so they land Magic 8-ready instead of needing a second polish pass.
- 4
Validate against the live environment
Before anything is deployed, the agent runs the generated searches against a sample window and reports row counts, latency, and missing-field warnings. Reviewers see whether a proposed alert would have fired in the past 24 hours, which catches noisy thresholds before they reach on-call rotations.
- 5
Deploy through a DAP change plan
Accepted artefacts attach to a DAP change plan with the target app, dashboard, and saved-search names. The confirmation card lists every change item grouped by app and stanza for reviewer approval. Approved plans roll out per-host with the same audit trail used for config edits.
- 6
Schedule re-exploration as data evolves
Promote the Data Explorer Agent into a scheduled workflow so new sourcetypes trigger a fresh proposal pass automatically. Teams use this to keep monitoring coverage in step with onboarding velocity instead of waiting for the next quarterly review to catch the gap.
References
Run this use case in your environment
Start free, connect a Splunk environment, and run the workflow with a reviewer-approved DAP change plan from the first execution.
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