Data Governance & Quality
Data governance is the set of policies, processes, and standards that ensure data is accurate, consistent, secure, and usable across an organisation. In an oilfield generating millions of data points daily, poor governance leads to conflicting reports, unreliable analytics, and costly mistakes.
Pillars of Data Governance
Data Quality
Data must be accurate, complete, timely, and consistent. Quality checks include range validation (e.g., pressure cannot be negative), gap detection, and outlier flagging.
Example: A daily automated quality check flags that 23 wells have no flow data for the past 6 hours - triggering an investigation into a SCADA communications failure.
Data Standards & Naming Conventions
Consistent naming of tags, wells, equipment, and units across all systems. Without standards, the same well might be called "A-07", "Well_A07", and "WELLA07" in three different systems.
Example: Adopting PRODML/WITSML standards ensures that data from different vendors and systems can be integrated seamlessly.
Data Ownership & Stewardship
Every dataset must have a designated owner responsible for its quality and a steward who manages day-to-day data issues. Without clear ownership, data problems persist because nobody is accountable.
Data Security & Access Control
Role-based access control ensures that only authorised users can view or modify sensitive data. Production data, financial figures, and reservoir models often have different access tiers.
Data Quality Dimensions
Accuracy
Does the data reflect reality?
Completeness
Are there missing values?
Timeliness
Is the data current?
Consistency
Same value across systems?
Uniqueness
No duplicate records?
Validity
Data conforms to rules?