Reports are not trusted
Teams question KPIs, filters, counts or source logic.
EAM Data Quality & Reporting
Make EAM data reliable enough for reports, dashboards and automation.
A focused service for teams whose asset data, work order history, failure codes, meter readings or report logic are blocking decisions, dashboards or AI ideas.
The EAM data and reporting angle is central here: evaluations, recurring analyses, operational reports, AI-readiness checks and the day-to-day structures teams need to trust before dashboards, automation or assistants depend on them.
Data-quality issue registerReporting foundationsAI readiness view
Typical starting points
Use this service when these symptoms are visible.
Dashboards depend on weak data
Asset, work order, failure or meter data is inconsistent.
AI ideas are blocked
The data is not reliable enough for assistants or anomaly support.
Recurring evaluations take too much effort
Reports and analyses require manual cleanup every cycle.
What Tiravera does
Practical work performed.
Review EAM data structures
Assets, work orders, failure/cause codes, meters, required fields and ownership.
Trace report logic
SQL, filters, joins, definitions and source assumptions.
Build issue register
Document quality issues, examples, owners, priority and business impact.
Define reporting foundations
Clarify which data can support dashboards, evaluations, automation or AI.
Outputs
Tangible deliverables.
Data-quality issue register
Concrete issues, affected reports, examples, owners and priority.
Cleanup priority map
What to fix first to improve decisions and recurring reports.
Reporting readiness view
Which reports, dashboards or AI candidates are realistic now.
Documentation
Definitions, assumptions and handover notes for the team or partner.
Best fit
Who this is for.
Maintenance and reliability teams
When work order and asset data drives daily decisions.
Reporting owners
When recurring reports need clearer logic and cleaner source data.
AI or automation candidates
When teams need to know whether data is ready before building.
Inputs needed
What to provide before or during the work.
Current reports and dashboards
Exports, SQL, screenshots, KPI definitions or dashboard examples.
Sample records
Work orders, assets, failure codes, meter data or exceptions.
Operational interpretation
People who can explain what the data should mean in daily work.
Delivery
From a clear scope to an accepted result.
Agree the work package
Define the problem, systems, access, owner, deliverables and acceptance criteria. We agree effort, timing and commercial terms before work starts.
Diagnose
Trace representative records and the current workflow. An audit delivers an issue map and priorities; implementation is scoped separately when the findings call for it.
Build and verify
Implement the agreed change or pilot. Check expected and actual results across normal, difficult and exception cases with the people who use the workflow.
Hand over and decide
Hand over the change record, test evidence, operating notes and remaining risks. Agree ownership and whether to deploy, harden, integrate or stop.
Bring one concrete problem.
Describe the workflow, share an example and say what should improve. The first 20-minute conversation is free; we agree the scope before paid work starts.