AGD-Aligned Roles
As we are building this in a dencentralized manner, we need a concerted effort from everyone to make this happen, below are the various roles and different ways that you can partake in this journey!
1. Data Producers
Role Description: Data Producers are entities or devices that generate raw experience data within the network. This data could come from sensors, user interactions, social media, IoT devices, or enterprise systems. Data producers are responsible for continuously or periodically collecting and uploading raw data to the decentralized network for processing and analysis. Their contributions fuel the entire data lifecycle, providing the foundational information that is later processed, validated, and consumed by others in the network.
Responsibilities:
Capture and contribute raw data (e.g., transactional data, sensor data, user activity logs).
Ensure data is uploaded in a timely manner and adheres to basic formatting requirements.
Maintain control over data access rights, dictating who can use or purchase their data.
Collaborate with the Data Mapping and Schema Developer to ensure their raw data fits the network’s schema.
Example: An IoT sensor in a smart city network that produces data about air quality or traffic patterns.
2. Data Mapping and Schema Developer
Role Description: The Data Mapping and Schema Developer is responsible for defining how raw data from various producers is structured and represented in the network. They create the schemas that standardize data, ensuring that different datasets can be interpreted, processed, and used consistently across the network. Additionally, they develop and manage the mapping rules that align disparate data formats to a common schema, making it easier for data consumers to integrate and use the data.
Responsibilities:
Design standardized data schemas that represent the structure and format of experience data in the network.
Develop mapping algorithms that transform raw data into the standardized format.
Ensure compatibility between different data sources and schemas to support integration across the network.
Collaborate with Data Producers and Consumers to ensure schema relevance and accuracy.
Example: A developer who creates a schema to standardize customer transaction data across different retail systems for easier analysis by data consumers.
3. Data Validator
Role Description: The Data Validator ensures the integrity, accuracy, and consistency of data uploaded to the network. Their role is to verify that data adheres to the network’s defined schemas and meets quality standards before it is made available for consumption or further processing. They may use both automated validation tools and manual checks to identify errors, inconsistencies, or anomalies in the data. The Data Validator plays a critical role in maintaining the trustworthiness of the network by ensuring that low-quality or erroneous data does not propagate.
Responsibilities:
Validate that data conforms to schema specifications (e.g., correct formats, field types).
Identify and flag inconsistencies, missing values, or erroneous data entries.
Apply automated validation tools or algorithms to perform real-time data checks.
Ensure that validated data meets quality thresholds required for further analysis or consumption.
Example: A validation system that checks the accuracy of GPS coordinates from different data producers to ensure the data is reliable before it is used in a navigation app.
4. Data Annotator
Role Description: The Data Annotator adds meaningful metadata, labels, or tags to raw or semi-processed data to enrich it for further use. In many cases, data annotations are essential for supervised machine learning tasks, where labeled datasets are needed to train algorithms. Data Annotators often work with unstructured data (e.g., images, text, video) and assign descriptive labels that make the data usable for algorithms or human interpretation. They can work independently or in conjunction with AI tools that assist with automated annotations.
Responsibilities:
Manually or semi-automatically tag and label raw data (e.g., identifying objects in images, annotating text with keywords).
Collaborate with Data Consumers to understand the type of annotations required.
Ensure annotation consistency and accuracy to maintain data usability and quality.
Create documentation for annotation guidelines and standards, ensuring annotations are interpretable by others in the network.
Example: An annotator who labels different vehicle types in a video feed for use in training a traffic-monitoring AI system.
5. Data Consumer
Role Description: Data Consumers are entities that access and use the processed data made available through the network. They may be businesses, researchers, or application developers who require specific datasets to derive insights, build applications, or train machine learning models. Data Consumers typically rely on the pre-processed and validated data provided by the network, but they may also perform additional analysis or transformation depending on their specific needs. They often acquire data through APIs or data marketplaces within the network.
Responsibilities:
Access, query, and utilize processed data for analytics, decision-making, or machine learning purposes.
Integrate network-provided data into their applications, services, or research projects.
Provide feedback to Data Producers and Data Mapping Developers on data quality and usability.
Adhere to data usage rights and restrictions defined by Data Producers or the network.
Example: A fintech company that consumes processed transaction data to develop predictive models for customer behavior or a marketing firm using enriched customer profiles for targeted advertising.
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