Introduction to Data Product Hub¶
Welcome to the Data Products Consumer Learning Track! In this section of the module, you'll be introduced to the Data Product Hub to discover Data Products for your use case.
Scenario¶
Imagine you're a data analyst tasked with supporting a new marketing campaign aimed at boosting customer loyalty. To get started, you need access to reliable, up-to-date data on customer purchasing patterns, product performance, and historical campaign metrics. In the past, gathering customer data would involve reaching out to multiple teams and merging disparate datasets. But with the Data Product Hub, you have a centralized, comprehensive source where you can instantly locate a set of relevant, curated Data Products designed specifically for customer behavior analysis.
You find Metrics that give insights into purchase frequency and Perspectives that provide saved analysis views on product affinity trends. With these resources at your fingertips, you can dive straight into analysis, build machine learning models for predictive insights, and present actionable data to the marketing team—all while ensuring that the data is accurate, compliant, and well-documented.
What do you need to get started?¶
To make the most out of this content, you’ll need:
- Understanding of Data Products.
Quick concepts¶
- Data Products: Curated, structured data sets that provide valuable insights, automate processes, or enhance decision-making. They take various forms, such as enriched datasets, semantic models, dashboards, or machine learning models, and are designed to help users understand and act on data easily. Essentially, Data Products turn raw data into useful information that can drive actions and improve outcomes.
What is Data Product Hub?¶
The Data Product Hub is a user-friendly interface within DataOS where you can discover and access curated Data Products, Metrics, and Perspectives. It enables seamless integration of data for your use cases. By offering trusted data in multiple formats and access to Data APIs, the Hub accelerates time-to-value and bridges the gap between IT and business teams.
Highlights¶
- Self-Service Access: Users can independently discover, explore, and use Data Products tailored to specific needs.
- Centralized Access Layer: Data Product Hub provides a single layer for generating APIs, and accessing metrics.
- Contextual Metadata: Data products include documentation, semantic definitions, quality metrics, lineage, and usage guidelines.
- Data Governance: Ensures data accuracy, compliance, and reliability.
- Seamless Integration: Data Product Hub connects effortlessly with analytics, AI, and ML platforms for continuous insight flow.
The hub displays a list of Data Products, each designed to provide relevant data for analytics, AI/ML, and data-driven applications. Each Data Product is tailored to specific use cases that support key business objectives, such as brand affinity or customer engagement.
What is a Metric?¶
A Metric is an entity that, by default, provides a logical view of a logical table, containing only one measure and a time dimension. This means that a Metric represents a specific data point or calculation over time, focusing on one key measure, such as average spent per category, retention rate, or churn rate, and linking it to a time for analysis.
On Data Product Hub, you can access and analyze key business metrics in one place for a unified view of organizational performance and decision-making. Further details of the Metrics are provided in the later modules.
What is a Perspective?¶
A Perspective is a saved exploration of a Data Product. A Perspective can be created for any Data Product that has an exposed semantic model. After exploring the Data Product on the Data Product Hub, the exploration can be saved as a Perspective, which can then be accessed in the Perspective tab.
Click any Perspective to be redirected to the Data Product’s Explore page. Further details of the Perspectives are provided in the later modules.
Next step¶
To discover the Data Products of interest that can solve your use case, refer to the next module: