How to Prepare

Certified Petroleum Data Analyst (CPDA) candidates should complete a self-assessment based on the information below to determine how to focus their studies. These resources are provided based on the Competency Areas of the exam.

Exam Blueprint

Number of Scored Questions Number of Experimental (Unscored) Questions Total Examination Questions Allotted Exam Time Average Time per Question
180 20 200 4 hours 72 secs

Certified Petroleum Data Analyst Exam Blueprint

Competency Approximate Percentage of Questions
Data Governance  11
Data Analysis   30
Data Quality Management   15
Data Security   9
Spatial Data   9
E&P Life-Cycle Processes   15
Master Data Management  9
Communication   2
Total  100

Competency Definitions, Indicators, And Resources

Competency Name: Data Governance

Competency Definition:
Explain how an organization manages its data assets, including business rules, policies, practices, procedures, roles, and responsibilities 1.

Competency Indicators:
Describe data governance in an organization (e.g., industry standards for data loading).

    1. Describe the rationale for data governance within an organization and its relationships with external stakeholders, such as regulatory bodies and partners.
    2. Describe the impact of data governance on the daily activities of a data analyst.
    3. Comply with internal data governance policies (where they exist).
    4. Use common organizational terms and definitions.
    5. Explain the reasons for maintaining data over time.
    6. Describe the data lifecycle (e.g., acquire, load, use, refine, archive, and delete data).
    7. Promote the value of data governance and the need to comply with it.


Data Governance Institute. (n.d.). The DGI data governance framework. Retrieved from

Mosley, M., Brackett, M., & Earley, S. (Eds.). (2009). The DAMA guide to the data management body of knowledge (DAMA-DMBOK guide). Bradley Beach, NJ: Technics Publications.

1 Data governance is developed at an organizational level, and a data analyst is expected to follow the organizational policies and processes rather than define them.

Competency Name: Data Analysis

Competency Definition:
Analyze data and oversee the maintenance of data stores and data-related solutions.

Competency Indicators:
Identify appropriate sources of raw data:

  • National and regulatory data repositories
  • Internal managed and unmanaged data stores
  • Data vendors
  • Service companies
  • Joint venture partners
  • Other 
    • Investigate new sources of data from vendors and regulators.
    • Describe physical and digital data.
    • Describe data domains (e.g., well, seismic, production, land), including units of measure.
    • Explain how data from different domains is used by an organization.
    • Describe major elements of the data retention policy and schedule as applied to data.
    • Describe the mapping of data across data stores, including:
  • Data rules
  • Translation of data
  • Transformation of data
  • Basic data workflow
    • Examine and describe the data workflow for all types of data (i.e., structured, unstructured, and semi-structured).
    • Cross-correlate data between different domains and data stores to ensure accuracy, consistency, and coherence.
  • Create necessary reports to identify missing data, duplicates, anomalies, and availability.
  • Build queries and filters to find physical and digital data items.
  • Provide the correct coordinate reference system (CRS) when loading data with location information.
  • Validate geospatial data precision.
  • Describe processes for data loading.
  • Ensure that loaded data adheres to the approved standards.
  • Solve basic workflow problems with close supervision.
  • Take ownership for metadata (i.e., data about data) management.
  • Ensure that the metadata is loaded to document data context, quality, and provenance.
  • Use receipt-tracking systems to ensure consistency.
  • Provide status information about requests to management and clients.

Document data movement procedures between data stores.
Adhere to internal naming standards.
Explain archiving in the data management process.
Follow retention standards for data.
Ensure that the history of data transformations is captured as it goes through the data lifecycle.
Explain backup and recovery procedures in an organization.

Mosley, M., Brackett, M., & Earley, S. (Eds.). (2009). The DAMA guide to the data management body of knowledge (DAMA-DMBOK Guide). Bradley Beach, NJ: Technics Publications.
Pascual, M. S. (2011). Standard GIS data: A look at accuracy, precision, and types of errors. Retrieved from
Srivastava, R. N. (2008). Spatial data quality: An introduction. Retrieved from

Competency Name: Data Quality Management

Competency Definition:
Apply quality management methods to assess, improve, and ensure the fitness of data for business use in accordance with industry and organizational business rules.

Competency Indicators:
Describe criteria for data quality:

  • Completeness
  • Consistency
  • Coherence
  • Accuracy
  • Timeliness
  • Relevance
    • Recognize the source of data and data state (e.g., raw, processed, and edited data).
    • Examine the quality of data to determine its fitness for a specific purpose.
    • Confirm the quality requirements for data with a business owner or a vendor.
    • Describe mandatory elements for data quality control (e.g., for wells, well header: name, location, spud date, and organization).
    • Verify that the data is uploaded in the right format.
    • Apply standard industry business rules to data:
  • Align a well header with the legal description (e.g., UWI)
  • Check the integrity of dates and depths
    • Apply organizational business rules to data.
    • Identify and resolve duplicate data for confirmation with a business owner: (e.g. well header, seismic survey, and well log).

Identify data reliability:

  • Identify data sources
  • Describe the preferred source list using the trustworthiness of the source as a criterion (e.g., internal reports, national data repository, and a vendor)
  • Review the reliability grade to data
  • Publish the information on the reliability of the data source in the format that is useful to users

Report data quality metrics to raise awareness of data quality status and potential issues.
Notify business owners of data about data quality issues.

Mosley, M., Brackett, M., & Earley, S. (Eds.). (2009). The DAMA guide to the data management body of knowledge (DAMA-DMBOK Guide). Bradley Beach, NJ: Technics Publications.

Competency Name: Data Security

Competency Definition:
Explain and comply with organizational policies and processes for data security management. 2

Competency Indicators:
Comply with internal data security management policies.

    1. Comply with necessary regulatory security policies (e.g., Sarbanes-Oxley, embargoed countries).
    2. Explain different security categories by domain and role of systems of record (e.g., confidential, public, tight holes, and edit vs. view).
    3. Explain the security policies used for master data stores.
    4. Explain the security policies used for document management.
    5. Ensure that data security rules are applied to data rooms, applications, and data stores.
    6. Comply with rules for access to and release of data to stakeholders.

Mosley, M., Brackett, M., & Earley, S. (Eds.). (2009). The DAMA guide to the data management body of knowledge (DAMA-DMBOK Guide). Bradley Beach, NJ: Technics Publications.

2 Data security management is set at an organizational level, and a data analyst is expected to follow the organizational policies and processes rather than define them.

Competency Name: Spatial Data

Competency Definition:
Use knowledge of coordinate reference systems and mapping technologies to accurately represent the position of assets.

Competency Indicators:
Explain the importance of coordinate reference data and its accuracy to an organization.

    • Explain how to transform coordinates from one coordinate system to another.
    • Explain the difference between geographic and projected coordinate systems.
    • List commonly used formats for latitude, longitude, and UTM.
    • List domains where coordinate reference data is used.
    • Explain the relationship between the Geographic Information Systems (GIS) and attribute data.
    • Describe the required metadata to be captured for coordinates.
    • Perform quality control on coordinate data, including:
  • Expected coordinate ranges
  • Magnitude of variability
  • Consistency with other sources
    • Verify the location within acceptable tolerances using GIS.

Identify ways to deal with common issues when handling coordinate data (e.g., mixed coordinate datum, different versions of local grids, mixed survey acquisition methods).
Use appropriate mapping technology to retrieve datasets based on location information.
Use mapping applications appropriately.
Verify the accuracy of coordinates.

ArcGIS Resource Centre. (2013). Map projections guidebook. Retrieved from
OGP Geomatics Committee. (2012). OGP P1/11 Geophysical position data exchange format (Report No. 483-1). Retrieved from
OGP Geomatics Committee. (2012). OGP P2/11 Positioning data exchange format (Report No. 483-2). Retrieved from
OGP Geomatics Committee. (2012). OGP P6/11 Seismic bin grid data exchange format (Report No. 483-6). Retrieved from
OGP Geomatics Committee. (n.d.). P6 GIS data model. Retrieved from
U.K. Offshore Operators Association. (1994). UKOOA data exchange format P5/94 Pipeline position data. Retrieved from
U.K. Offshore Operators Association. (2002). UKOOA data exchange format P7/2000 Format for well deviation data. Retrieved from

Additional Resoources:
International Association of Oil and Gas Producers,

Competency Name: Exploration and Production (E&P) Life-Cycle Processes

Competency Definition:
Explain the major players, business life cycle, roles, and processes involved in E&P.

Competency Indicators:
Explain the roles of major players in E&P, including operators, regulators, data vendors, software vendors, and service companies.

    1. Explain the roles and work functions of internal clients.
    2. Explain how internal clients consume data (e.g., planning, appraisal, delineation, and interpretation).
    3. Describe the phases of the business lifecycle (e.g., planning, drilling, completing, producing, and disposing).
    4. Explain basic terminology used in the E&P industry.

American Petroleum Institute. (2013). Wells to consumer interactive diagram. Retrieved from
Petroleum Industry Data Exchange. (2013). Petroleum industry data dictionary. Retrieved from
PPDM Association. (2012). PPDM business rule. Retrieved from
PPDM Association. (2012). Well identification. Retrieved from 
PPDM Association. (2012). Well status. Retrieved from  
PPDM Association. (2012). What is a well? Retrieved from
PPDM Association. (2012). Wiki general well and industry data. Retrieved from 
Schlumberger. (2013). Oilfield glossary app. Retrieved from

Additional Resources:

Competency Name: Master Data Management

Competency Definition:
Explain a set of policies, practices, and procedures used in master data management and comply with them.

Competency Indicators:
Describe master data management in an organization (i.e., creation of a “golden version” of master data attributes).

    1. Describe the rationale for master data management within an organization.
    2. Describe the impact of master data management on daily activities.
    3. Identify master data sources and contributors.
    4. Comply with internal master data management policies (where they exist).

Mosley, M., Brackett, M., & Earley, S. (Eds.). (2009). The DAMA guide to the data management body of knowledge (DAMA-DMBOK Guide). Bradley Beach, NJ: Technics Publications.

Competency Name: Communication

Competency Definition:
In a clear and timely manner, communicate with others on the appropriate aspects of data management to support the business.

Competency Indicators:
Use active listening skills when communicating with others.

    1. Ask probing questions to better understand business requirements and priorities.
    2. Clearly articulate problems and suggestions to internal and external clients.
    3. Build effective working relationships within an organization.
    4. Establish working relationships with external clients (e.g., vendors, partners, and regulators).
    5. Identify and engage the appropriate expert for a task or problem.
    6. Explain problems using appropriate technical language.
    7. Provide constructive feedback to others.
    8. Actively seek feedback from internal clients.

Work with business units to ensure the optimal use of data to meet analytic requirements of end users.
Communicate the availability of data to internal clients in a timely manner.
Use the communication media appropriate to the audience and purpose of communication.
Coordinate the movement of data amongst users, disciplines, applications, and stakeholders.
Communicate data management standards to users in an organization.
Provide an estimated time of delivery of completed tasks to others.