Data product

Data products are structured, curated, and packaged data sets that are made available for specific purposes or target groups.

Meaning in detail Contact us now

A data product is a collection of data that has been prepared and structured for a specific purpose. Data products can take various forms, such as datasets, reports, APIs, or dashboards.

Which roles are involved in data products?

Creating and using data products requires the collaboration of various roles:

Data Owner: The data owner is responsible for defining and strategically aligning the data product. He has overall responsibility for the success of the data product.
Domain expert: The domain expert has deep knowledge of the business area from which the data originates. It helps identify data requirements and ensure the quality and relevance of the data.
Data product engineer: The data product engineer is responsible for the technical implementation of the data product. He develops and maintains the data pipelines, APIs, and other technical components.
Data analyst: The data analyst analyses the data and creates reports and visualizations that convey value to users.
Data product manager: The data product manager (data product manager) is responsible for the life cycle of the data product. He defines the roadmap, monitors performance and communicates with stakeholders.

What are the benefits of data products?

Improved data usage: Data products make it easier for users to search, understand, and use data.
Accelerated decision making: Data products enable companies to make data-based decisions faster.
Increased data transparency: Data products promote transparency and understanding of data across the organization.
Fostering innovation: Data products can enable new business opportunities and innovations.
Reduced data costs: Data products can save costs by avoiding duplication and inefficient use of data.

How are data products created?

Creating data products involves several steps, including:

Data collection: Identification of data sources and needs.
Data collection: Extract and collect relevant data.
Data cleansing: Clean and transform data to eliminate errors and inconsistencies
Data modeling: Organize the data in an appropriate format.
Data enrichment: Supplement the data with relevant information and metadata.
Documentation: Create documentation that describes the data and how to use it.
Deployment: Provision of data products via APIs or other interfaces.
Maintenance: Monitor and update data products to ensure their quality and relevance.

What are examples of data products?

Customer profiles: Records that include information about customers, such as name, address, purchase history, and preferences.
Product catalogs: Data sets that contain information about products, such as description, price, availability, and technical specifications.
Market data: Data sets that contain information about market conditions, trends, and competitors.
Financial data: Data sets that provide information about sales, costs, profits, and others Financial figures included.
Sensor data: Data sets that include data collected by sensors, such as temperature, pressure, humidity, and movement.

Data products enable companies to make better use of their data, make data-based decisions and open up new business opportunities. Creating and using data products can give companies a competitive advantage and help them succeed in today's data-intensive world.

Feel free to leave us about Data products in the organization und Company culture talk. And also how to put them in the Enterprise architecture integrates.

Note: Our team benefited from the support of AI technologies while creating and maintaining this glossary.