Reliable reporting starts with reliable source data
Well data is accumulated across daily operations, rig activities, wellbores, jobs and reporting processes. A report can look complete while containing duplicate joins, inconsistent dates, outdated codes or missing records. These issues can distort operational KPIs and downstream decisions.
JUIA provides scoped well-data assessment and improvement services for WellView, and data engineering and validation support for OpenWells/EDM environments.
Common data quality problems
- Missing or inconsistent well, wellbore, job and reporting identifiers.
- Duplicated records caused by one-to-many joins or mismatched reporting grain.
- Incorrect or inconsistent event, job and rig activity coding.
- Inconsistent operation dates, reporting dates and local time conventions.
- Differences between application totals, database extracts and Power BI reports.
- Migration source-to-target reconciliation and historical data completeness.
A practical validation approach
Profile
Agree critical datasets and define completeness, consistency and accuracy checks against operational meaning.
Reconcile
Compare application records, database extracts and reporting outputs at the correct grain.
Improve
Prioritise exceptions, document corrections and establish repeatable monitoring where appropriate.
Example deliverables
A typical scoped assessment could deliver a data-quality register, prioritised remediation recommendations, validation SQL, reconciliation results and an agreed approach for recurring checks. Actual deliverables depend on data availability, user access and the commercial scope.
For ongoing quality management, see well data stewardship and governance services.
A practical control is to reconcile a sample of selected wells from the operational application to the source extract and then to the Power BI measure. Keep an exception log showing whether the variance came from source data, transformation logic or a reporting definition.
Joining a well table to multiple jobs, daily reports, time logs and cost records can multiply the apparent well or job totals when relationships are not handled at the correct level. Before interpreting a dashboard variance, verify the keys, aggregation level, date selection and calculation rules.
Why reporting grain is a common source of quality errors
Frequently asked questions
Can data-quality checks run against Snowflake?
Yes. Approved Snowflake datasets can support repeatable tests and reconciliation with operational systems. Specific checks must reflect the source model and refresh timing.
Can you help before a WellView migration or upgrade?
Yes. Pre-migration profiling and post-migration reconciliation can help identify missing records, changed calculations and reporting differences.
Can the work be performed remotely?
Many database assessment, reporting and reconciliation activities can be completed remotely, subject to approved access and security requirements.