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Common Data Model (CDM) Specification, Version 3.0 Note to programmers: The separate CDM parseable file is more helpful for direct use in implementation, and contains the complete table specifications.
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How to fill out common data model cdm

01
To fill out the common data model (CDM), follow these steps:
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Understand the CDM structure: Familiarize yourself with the different entities, attributes, and relationships defined in the CDM.
03
Identify the data sources: Determine which data sources you want to map to the CDM. This could include databases, APIs, spreadsheets, etc.
04
Map the data to the CDM: For each data source, map the appropriate attributes to the corresponding CDM attributes. Ensure the mapping is accurate and complete.
05
Clean and transform the data: Perform data cleaning and transformation processes to ensure the data aligns with the CDM requirements. This may involve data validation, normalization, entity resolution, etc.
06
Validate the data: Verify that the mapped and transformed data meets the validation rules and constraints defined by the CDM. Address any issues or errors that arise during the validation process.
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Load the data into the CDM: Load the validated data into the CDM, ensuring that the records are correctly associated with their respective entities and relationships.
08
Test and reconcile: Perform thorough testing to ensure the CDM data is accurate, consistent, and reliable. Reconcile any discrepancies or inconsistencies found during the testing phase.
09
Maintain and update: Continuously maintain and update the CDM as new data sources or changes occur. Regularly review and enhance the data mapping and transformation processes to improve overall data quality and fidelity.
10
By following these steps, you can effectively fill out the common data model (CDM) with your data.

Who needs common data model cdm?

01
Common data model (CDM) is needed by various professionals and organizations involved in data integration, analytics, and information management. This includes:
02
- Data engineers and data architects: They require CDM to design and implement data integration solutions that leverage standardized data structures and semantics.
03
- Data analysts and data scientists: They need CDM to access and analyze data from different sources without worrying about data inconsistencies or variations.
04
- Business intelligence and reporting professionals: They can utilize CDM to build consistent and unified reports and dashboards across multiple data sources.
05
- IT and software development teams: They can use CDM as a reference for building applications, databases, and APIs that consume or produce data conforming to a standard schema.
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- Enterprise data management teams: They can leverage CDM to ensure data governance, data lineage, and data quality management across the organization.
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In summary, CDM is beneficial for any individual or organization that aims to achieve data interoperability, consistency, and efficiency in their data-related initiatives.
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The Common Data Model (CDM) is a standardized framework for organizing and structuring data in a consistent manner to facilitate data sharing, integration, and analysis across different systems and organizations.
Organizations that collect or process specific types of data mandated by regulatory authorities are required to file the Common Data Model (CDM). This typically includes businesses in certain industries such as finance, healthcare, and utilities.
To fill out the Common Data Model (CDM), organizations should follow the provided guidelines and templates that outline the specific data fields and formats required. Accurate data collection, validation, and adherence to standards are essential for proper completion.
The purpose of the Common Data Model (CDM) is to enable seamless data exchange and interoperability across different systems, enhance data accuracy, and support regulatory compliance and reporting requirements.
Information that must be reported on the Common Data Model (CDM) typically includes data on transactions, customer interactions, compliance metrics, key performance indicators, and any other data relevant to regulatory standards.
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