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Key aspects of the Input Data requirements in the PDR include:
Data Sources
The PDR identifies the various internal and external data sources that will feed into the solution, such as enterprise systems, third-party APIs, and customer/user inputs.
It clearly maps out how data will be extracted, transformed, and integrated from these different sources.
Data Types and Formats
The PDR specifies the different data types (e.g., numerical, textual, date/time, geospatial) that the solution must be able to ingest and process.
It also defines the expected data formats (e.g., CSV, JSON, XML) and any necessary data normalization or standardization requirements.
Data Volume and Velocity
The PDR assesses the anticipated volume of data (both in terms of historical data and real-time inputs) that the solution must be able to handle.
It also considers the velocity, or speed, at which data may need to be ingested, processed, and actioned by the solution.
Data Quality and Governance
The PDR outlines the data quality standards and validation rules that the solution must apply to ensure the integrity and reliability of the input data. It also defines the data governance policies and procedures, such as data ownership, access controls, and compliance requirements.
Data Security and Privacy
The PDR addresses any sensitive or confidential data that may be involved and the necessary security measures and controls to protect that data. It also considers the relevant data privacy regulations and the solution's alignment with those requirements.
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KEY INSIGHT: Thoroughly defining the Input Data requirements, the PDR structure helps ensure that the proposed solution is equipped to handle the necessary data inputs, maintain data quality and integrity, and comply with relevant data-related policies and regulations. |
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The PDR identifies the various internal and external data sources that will feed into the solution, such as enterprise systems, third-party APIs, and customer/user inputs.
The PDR specifies the different data types (e.g., numerical, textual, date/time, geospatial) that the solution must be able to ingest and process, as well as the expected data formats (e.g., CSV, JSON, XML) and any necessary data normalization or standardization requirements.
The PDR assesses the anticipated volume of data (both in terms of historical data and real-time inputs) that the solution must be able to handle, as well as the velocity, or speed, at which data may need to be ingested, processed, and actioned by the solution.
The PDR outlines the data quality standards and validation rules that the solution must apply to ensure the integrity and reliability of the input data, and defines the data governance policies and procedures, such as data ownership, access controls, and compliance requirements.
The PDR addresses any sensitive or confidential data that may be involved and the necessary security measures and controls to protect that data, as well as the relevant data privacy regulations and the solution's alignment with those requirements.
Key Takeaways of Input Data Requirement
The following are the key values and takeaways from role of input data requirements in the PDR:
Ensures the Proposed Solution Can Fulfill Its Intended Capabilities
The detailed definition of input data sources, types, formats, volumes, and velocities helps validate that the proposed solution will have access to all the necessary data inputs required to power its core functionalities.
This alignment between input data requirements and solution capabilities is critical for the solution to effectively deliver on its intended purpose.
Promotes Data Quality and Integrity
By outlining data quality standards, validation rules, and governance policies, the Input Data requirements help ensure the reliability, accuracy, and trustworthiness of the data feeding the solution. This data quality focus is essential for the solution to generate credible and dependable outputs.
Addresses Data Security and Privacy Concerns
The Input Data requirements section covers the identification of sensitive data and the implementation of appropriate security controls and privacy measures. This helps mitigate risks related to unauthorized access, data breaches, and compliance violations.
Facilitates Effective Data Management
The comprehensive definition of data sources, types, formats, and flows allows for better data management planning, integration, and orchestration. This supports the efficient ingestion, processing, and utilization of data by the solution.
Enables Scalability and Adaptability
The assessment of data volume and velocity requirements helps ensure the solution can handle increasing amounts of data and respond to evolving data needs over time. This scalability and adaptability is crucial as the solution's data requirements may change due to business growth, new use cases, or regulatory changes.