Introduction
As a product manager, I frequently encounter questions about the definition of a digital or data product, its domain, and the roles and responsibilities of each team involved. This article is particularly relevant for those seeking to define a ‘Data Product’ within their professional sphere. In this paper, I aim to elucidate my understanding of a ‘Data Product’ to its lifecycle. A ‘Data Product’ can be defined as a structured or unstructured dataset or a data-driven dataset that provides actionable insights or facilitates decision-making throughout its lifecycle. It is developed, managed, and utilized in various forms, from raw data to processed information, within the stages of the data lifecycle. Regardless of the data type (structured/unstructured), there are six key stages that characterize a data product’s lifecycle. Each stage has unique characteristics and involves different roles and responsibilities:
1. Raw Data
- Domain Definition: The initial environment where data is collected and stored. This could be a relational database for structured data or a data lake or content management system for unstructured data.
- Product Owner Responsibilities: Defining the scope of raw data collection, ensuring adherence to data quality standards, and managing the backlog for data collection improvements.
- Product Manager Responsibilities: Developing the overall strategy for data acquisition, ensuring alignment with business objectives, and communicating the value of raw data assets to stakeholders.
- Users: Data engineers, ETL developers, and initial data analysts who need access to the initial data for further processing or analysis.
2. Cleaned Data
- Domain Definition: The processes and tools used to clean and prepare data for analysis. This involves data quality tools for structured data and may involve more complex data curation tools for unstructured data.
- Product Owner Responsibilities: Prioritizing data cleaning tasks, overseeing the maintenance of data quality, and managing the backlog of data issues.
- Product Manager Responsibilities: Planning the data cleaning roadmap, ensuring alignment with data governance policies, and overseeing the implementation of data quality tools.
- Users: Analysts and data scientists who require reliable data for accurate analysis and reporting.
3. Integrated Data
- Domain Definition: The framework within which data from various sources is combined and made consistent. This often involves a data warehouse for structured data; unstructured data integration could involve a complex data platform.
- Product Owner Responsibilities: Managing stakeholder requirements for data integration, ensuring integrated data meets user needs, and maintaining the product backlog.
- Product Manager Responsibilities: Developing the data integration strategy, coordinating cross-functional teams, and ensuring the integrated data supports business objectives.
- Users: Business intelligence professionals and data analysts who need a comprehensive view of data from multiple sources.
4. Processed Data
- Domain Definition: The set of operations that convert raw data into a more usable format. This might be SQL operations for structured data; for unstructured data, it could be AI-driven content analysis.
- Product Owner Responsibilities: Setting priorities for data processing, ensuring processed data meets the acceptance criteria, and managing the processing backlog.
- Product Manager Responsibilities: Defining the vision for data processing, overseeing the development of processing tools, and ensuring the processed data enables the desired applications.
- Users: Application developers and advanced analytics practitioners who need processed data for specific tasks.
5. Analyzed Data
- Domain Definition: The analytical environment where data is examined to generate insights. This could be a BI tool for structured data or an advanced analytics platform for unstructured data.
- Product Owner Responsibilities: Focusing on the iterative delivery of analyzed data, gathering feedback from users, and refining the analysis based on user needs.
- Product Manager Responsibilities: Setting the direction for data analysis initiatives, ensuring the analysis is actionable, and integrating insights into the broader product strategy.
- Users: Decision-makers and strategists who rely on insights to inform business actions, policies, or strategies.
6. Data Visualizations
- Domain Definition: The tools and methods used to create visual representations of data to communicate findings. This includes dashboarding tools for structured data and more creative visualization tools for unstructured data.
- Product Owner Responsibilities: Collaborating with users to define visualization requirements, prioritizing visualization features, and managing updates.
- Product Manager Responsibilities: Guiding the overall vision for data presentation, ensuring visualizations are user-friendly and align with business goals, and managing the visualization product lifecycle.
- Users: A broad range of end-users, from business executives to the general public, who consume the visualized data to understand the insights presented.
Conclusion
Understanding the lifecycle of a data product is crucial for leveraging its full potential. Each stage—from raw data to data visualizations—requires careful management and coordination between various teams. By clearly defining responsibilities and maintaining a strategic vision, organizations can unlock the true value of their data products, driving better decision-making and achieving business objectives.