Data Governance & Master Data: Ownership & Standards Training Course from Institute For Oil & Gas Training develops practical capability in Data Governance & Master Data in Upstream Operations, helping oil and gas organisations establish reliable ownership, consistent standards, controlled master data and trusted information across the upstream value chain.
Upstream operations depend on data generated across exploration, drilling, production, wells, facilities, assets, finance, procurement, supply chain and commercial functions. When the same asset, well, field, supplier, equipment item or cost centre is represented differently across systems, organisations face reconciliation effort, reporting inconsistencies, duplicated records and reduced confidence in operational information. Effective data governance addresses these issues by establishing clear accountability, common definitions, controlled standards and processes for maintaining information throughout its lifecycle.
This course focuses on the practical management of data as a corporate asset. It examines how organisations establish a data governance framework, assign data ownership and stewardship, define data quality dimensions and implement master data management practices that support operational and financial decision making. Participants examine how a golden record is established, how a data dictionary and business glossary create consistent business terminology, and how reference data standards improve consistency between applications and reporting environments.
Data Governance & Master Data in Upstream Operations requires more than technical system administration. It involves business accountability, process discipline and agreement between functions that create, consume and maintain information. The course therefore connects governance principles with operational realities across upstream organisations, including asset hierarchies, well information, production data, equipment records, organisational structures, vendor data and financial master data.
Participants develop an understanding of how data ownership operates across business functions and how stewardship responsibilities support the accuracy and usability of critical information. They explore practical approaches to defining ownership roles, approving standards, managing exceptions, resolving conflicting definitions and establishing escalation routes for data issues.
The programme also addresses data quality monitoring and remediation. Organisations require consistent methods for identifying incomplete, inaccurate, duplicated, outdated or inconsistent records. Participants examine quality rules, validation controls, issue registers, remediation workflows and monitoring practices that support sustainable data quality rather than one-off cleansing exercises.
Master data management is considered from an upstream business perspective, with attention to the relationships between master records, reference data and transactional information. Participants examine how controlled master data supports integrated reporting, enterprise resource planning, operational technology, production systems, maintenance applications, procurement platforms and financial processes.
The course also explores data lineage and the ability to trace information from its source through transformation, integration and reporting. Clear lineage improves transparency when business users need to understand where a figure originated, which system supplied it, how it was transformed and which controls govern its use.
Institute For Oil & Gas Training positions these practices within the wider digital finance and automation environment. Strong governance and standardised master data provide an important foundation for automation because automated processes depend on consistent identifiers, definitions, classifications and business rules. Better governed data also supports more reliable dashboards, analytics, workflow automation and cross-functional reporting.
The programme is designed for professionals who need to establish or strengthen data accountability within oil and gas organisations. It is relevant to upstream operations, digital transformation, finance, information management, IT, engineering, production, asset management, procurement and corporate functions that depend on trusted master data.
Establish the principles and components of an effective data governance framework for upstream operations
Define data ownership and stewardship responsibilities across business and technical functions
Identify critical upstream master data domains and their business significance
Apply recognised data quality dimensions to assess information reliability
Develop practical approaches to master data management across integrated systems
Establish principles for creating and maintaining a trusted golden record
Develop controlled definitions through a data dictionary and business glossary
Apply reference data standards to improve consistency across systems and reports
Trace information through source systems and transformations using data lineage principles
Establish data quality monitoring and remediation processes
Identify root causes of duplication, inconsistency and incomplete master data
Define governance controls for data creation, approval, change and retirement
Strengthen collaboration between business data owners, data stewards and technical teams
Support digital finance and automation initiatives through consistent and governed data
Improve the reliability of management reporting and cross-functional analysis
Apply governance principles to practical upstream business scenarios
Institute For Oil & Gas Training uses a practical corporate delivery approach focused on application rather than academic theory. The methodology connects data governance concepts with realistic oil and gas business situations involving multiple systems, functions, data owners and information standards.
Participants work through oil and gas scenarios involving inconsistent asset records, duplicated equipment information, conflicting well identifiers, supplier master data issues and inconsistent financial classifications. Each case examines the business consequences of weak governance and the controls required to address the underlying problem.
Simulated governance exercises demonstrate how a data governance framework operates in practice. Participants examine governance roles, decision rights, escalation routes, approval processes and stewardship responsibilities before applying them to representative upstream data challenges.
Practical exercises focus on identifying critical master data domains and determining how ownership, definitions, standards and approval controls should operate. Participants examine how different records can be consolidated into a trusted golden record.
Participants assess sample data against data quality dimensions including accuracy, completeness, consistency, timeliness, validity and uniqueness. Exercises focus on identifying root causes and developing structured remediation approaches.
The programme includes practical exercises for defining business terms, assigning accountable owners and establishing consistent terminology. Participants consider how a data dictionary and business glossary support communication between technical and business teams.
Participants trace information through source systems, transformation processes and reporting outputs. These scenarios demonstrate how data lineage supports transparency, control and investigation when information is challenged.
Group exercises require participants to resolve ownership conflicts, prioritise data quality issues and establish standards for shared information. The approach reflects the cross-functional nature of governance within operating companies and joint business environments.
The delivery incorporates scenarios involving exploration, drilling, production, maintenance, facilities, supply chain and finance. This enables participants to connect governance practices with the operational and commercial information flows used across oil and gas organisations.
Effective data governance creates a stronger information foundation for oil and gas organisations operating across complex assets, functions and technology environments. Institute For Oil & Gas Training enables sponsoring organisations to strengthen accountability for critical data and establish consistent practices for managing information throughout its lifecycle.
Clear data ownership reduces uncertainty about who is accountable for definitions, quality, approval and remediation. This supports faster resolution of data issues and reduces dependence on informal arrangements between departments.
A structured master data management approach improves consistency across systems. Standardised asset, well, equipment, supplier, organisational and financial records support more reliable integration between enterprise applications and operational platforms.
Improved data quality also reduces avoidable reconciliation activity. When information is complete, consistent and uniquely identified, teams spend less time investigating conflicting records and more time using information for operational and commercial decisions.
The use of a golden record supports a consistent representation of critical entities. This provides a controlled reference point for downstream processes, reporting and analytics while reducing the impact of duplicate or conflicting records.
A robust data dictionary and business glossary strengthen communication between business and technical teams. Common definitions reduce ambiguity around key measures, attributes, classifications and business terms.
Reference data standards provide further consistency by controlling shared classifications, codes and values. This supports reliable integration and reporting where multiple applications depend on common categories and identifiers.
Data lineage improves transparency across reporting and analytics environments. Organisations gain a clearer understanding of how information moves from source systems to transformed datasets and final outputs.
Data quality monitoring and remediation provide a repeatable approach to identifying and resolving information problems. Instead of treating data cleansing as an isolated project, organisations establish processes for ongoing quality management.
Strong governance also supports automation. Automated workflows require dependable master data, stable identifiers and clearly defined business rules. Governance therefore provides a practical foundation for digital finance, analytics and enterprise automation initiatives.
From a risk and control perspective, documented ownership, definitions, standards and remediation processes provide greater visibility over information management responsibilities. This supports stronger internal control practices and improves the ability of management teams to investigate data issues.
The organisational impact extends across finance, operations, procurement, maintenance, engineering, IT and digital transformation. A common governance structure helps these functions manage shared information through consistent processes rather than isolated departmental practices.
Participants gain practical capability to manage data as a business asset within an oil and gas environment. The course strengthens their ability to translate data challenges into defined governance requirements and structured improvement actions.
Professionals develop stronger skills in identifying data owners, defining stewardship responsibilities and establishing decision rights. These capabilities support effective collaboration between operational, commercial and technical stakeholders.
Participants also strengthen their understanding of master data management and learn how to distinguish master data, reference data and transactional information within practical business scenarios.
The course develops the ability to assess data using recognised data quality dimensions. Participants become better equipped to identify quality problems, determine root causes and structure appropriate remediation activities.
Participants gain practical knowledge of establishing a golden record and controlling the information required to maintain reliable master records. This supports professionals involved in data management, digital transformation, ERP environments and information integration.
The programme also strengthens skills in creating a data dictionary and business glossary. Participants learn how controlled terminology supports consistent communication and reporting across functions.
Understanding data lineage gives professionals a clearer method for tracing information across systems and transformations. This supports investigation, reporting assurance and collaboration with technical teams.
For managers and senior professionals, the course provides a structured basis for defining governance responsibilities, prioritising data improvement initiatives and aligning data standards with operational requirements.
For specialists working in finance and automation, the programme strengthens the connection between data governance and dependable digital processes. Participants can apply governance principles to the master data that supports reporting, financial systems and automated workflows.
Professionals responsible for governance structures, information ownership, stewardship, standards and data quality gain practical methods for strengthening enterprise data management.
Managers leading digital programmes gain a stronger understanding of the data foundations required for integration, analytics and automation.
IT specialists responsible for enterprise applications, data platforms and integration benefit from stronger alignment between technical controls and business data ownership.
Operations managers gain practical insight into the governance of well, asset, production, equipment and operational master data that supports daily business processes.
Finance professionals gain techniques for improving the quality and consistency of master data supporting financial reporting, enterprise systems and automation.
Asset and maintenance teams benefit from improved approaches to equipment, asset hierarchy and reference data governance.
Procurement and supply chain professionals gain methods for managing supplier, material, category and reference information consistently across systems.
Engineering professionals gain greater awareness of data standards, ownership and lineage across technical information environments.
Existing and prospective data stewards develop practical methods for controlling definitions, monitoring quality and coordinating remediation.
Senior managers gain a structured understanding of governance responsibilities and the organisational controls required for reliable enterprise information.
This module establishes the governance foundation for managing data as an organisational asset. It examines governance structures, decision rights, accountability and the relationship between business functions and technical teams.
Provides an international framework for addressing data quality and information exchange requirements.
Supports consistent approaches to identifying and managing quality characteristics.
Provides useful principles for organisations establishing structured data quality practices.
Supports the development of controlled information processes across enterprise environments.
Define the components of a practical data governance framework
Establish appropriate ownership and stewardship responsibilities
Identify critical upstream data domains
Define governance decision rights and escalation routes
Connect data governance responsibilities with operational business processes
This module focuses on the control of critical master data across upstream systems. It examines how organisations create consistent records for assets, wells, equipment, suppliers, organisational structures and other key entities.
Provides principles for managing metadata and data element definitions.
Supports consistent identification and description of data elements.
Helps organisations establish controlled metadata structures.
Provides a recognised foundation for managing shared data definitions.
Identify critical master data domains
Establish principles for master data management
Define requirements for a golden record
Identify duplicate and conflicting records
Establish controlled processes for creating and changing master data
Align master data ownership with business accountability
This module addresses the standardisation of information used across business systems and reporting environments. It focuses on consistent terminology, controlled classifications and shared reference values.
Provides principles relevant to master data exchange and information quality.
Supports consistent handling of master data between business partners and systems.
Encourages structured approaches to identifying and exchanging data.
Supports controlled master data practices across integrated environments.
Develop practical data dictionary structures
Establish a business glossary for shared terminology
Define reference data standards
Improve consistency across classifications and codes
Establish ownership for critical business definitions
Control changes to shared data standards
This module focuses on the continuous monitoring and improvement of data quality. Participants examine how quality issues are detected, investigated, prioritised, remediated and monitored across the information lifecycle.
Provides a recognised model for representing the provenance of information.
Supports description of where information originated and how it was produced.
Helps organisations document relationships between sources, activities and resulting data.
Provides a useful foundation for structured data lineage and provenance practices.
Apply data quality dimensions to upstream information
Establish practical quality monitoring controls
Identify root causes of recurring data issues
Structure data quality remediation workflows
Trace information through source and transformation processes
Apply data lineage principles to reporting and analytics
Establish continuous data quality improvement practices
This module brings governance, master data, standards and quality management together within the wider digital operating environment. It focuses on applying governance controls to integrated systems, digital finance and automation initiatives.
Provides requirements for an asset management system.
Establishes principles for managing assets through a structured management system.
Supports consistent asset information and lifecycle management practices.
Provides relevant context for governing asset-related information within asset-intensive organisations.
Integrate data governance with upstream operational processes
Apply master data controls to digital finance and automation environments
Strengthen data controls across integrated systems
Establish governance measures for ongoing performance monitoring
Develop practical implementation priorities for data governance improvement
Connect data ownership, standards, quality and lineage into a coordinated governance model
Attendees receive a Certificate of Completion from Institute For Oil & Gas Training upon finishing the course.
Certificate issuance requires attendance and completion of the full course programme. Participants are expected to engage with the course activities and complete the required programme attendance.
Fees include tuition, course materials and refreshments. Need different dates or a different city? Ask about your preferred date.
Data Governance & Master Data in Upstream Operations focuses on establishing ownership, standards, quality controls and management practices for critical information used across upstream oil and gas activities.
The course is designed for data governance professionals, data stewards, IT specialists, digital transformation managers, upstream operations teams, finance professionals, asset managers, procurement teams and business data owners.
Participants learn how to identify critical master data domains, assign ownership, establish controlled records, manage duplicates, create a golden record and maintain consistent master data across integrated systems.
Yes. The programme covers data quality dimensions, data profiling, quality monitoring, issue classification, root cause analysis, remediation workflows and continuous data quality improvement.
The course demonstrates how governed master data, consistent definitions, reliable reference data and clear data lineage provide stronger foundations for reporting, digital finance, system integration and automated business processes.
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Next: 12 Oct 2026
4 dates available
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