In the area of data products, the concept of products is applied to the data itself. This means that there are producers who offer data as their product and consumers who are treated similarly to customers. This results from the implementation of the data mesh principle "data as a product" in the way companies handle data.
In the data mesh approach, responsibility for data is distributed among the specialist departments in order to avoid silos and improve data quality. Data products are often domain-specific, which means that they are tailored to the specific needs and context of a specialist department and are managed by the respective specialist departments. Before they are prepared and made available to all employees in the company in a self-service marketplace, data products undergo a quality check. Business users can then find and use these data products for their specific application purposes.
The benefit of data products is that they enable companies to manage, structure and use large volumes of collected and generated data quickly and efficiently. This makes it easier for data users to gain insights, apply them efficiently and make more informed decisions on this basis.
Characteristics of data products
- Producers and consumers: Data products have clearly defined producers, who are responsible for creating and maintaining the data, and consumers, who use this data for their business decisions.
- Quality check: Before data products are made available on a self-service marketplace within the company, they are subject to strict quality checks. These checks ensure that the data is consistent, accurate and relevant.
- Self-service marketplace: Data products are offered in a user-friendly marketplace that enables business users to find and use the data they need independently. This promotes an agile way of working and reduces the effort required to procure data.
- Diverse use cases: The flexibility of data products allows different business areas to use them for different purposes, whether for analysis, reporting or to support operational decisions.
- Metadata and documentation: Data products should contain comprehensive metadata, including descriptions, data provenance and usage information.
- Standardized interfaces: The provision of APIs or other access mechanisms makes it easier for consumers to access and integrate data products.
- Security and compliance aspects: Data protection and regulatory requirements should be taken into account to ensure compliance with laws such as the GDPR.
Advantages of data products
- Efficient management of data: By structuring and organizing data in the form of products, companies can manage and use large amounts of information more efficiently.
- Facilitated knowledge acquisition: The preparation of data in an understandable form enables users to quickly gain valuable insights without the need for in-depth technical knowledge.
- Better decision making: With access to high-quality, well-structured data, companies can make informed decisions based on up-to-date information.
- Agility and responsiveness: The ability to develop and adapt data products quickly allows companies to react flexibly to changing market conditions and internal or regulatory requirements.
- Scalability: Data products make it easier for companies to scale data solutions and adapt them to growing requirements.
- Promote innovation: Easy access to high-quality data enables employees to develop new ideas and drive innovative solutions.
Implementation of data products
The implementation of data products requires a strategic approach that comprises various steps:
- Data identification: Determination of the relevant data sources and the information required for the creation of data products.
- Data preparation: cleansing and transformation of the raw data into a usable format.
- Product development: Creation of data products taking into account user needs and the required quality standards.
- Quality assurance: Performing tests and checks to ensure that the data products meet the requirements.
- Marketplace integration: Provision of data products on an internal marketplace where business users can access them.
- Feedback loops: Setting up mechanisms for user feedback to enable continuous improvements and adjustments to data products.
Challenges during implementation
- Cultural change: The introduction of data products often requires a cultural change in the company towards a data-driven mindset.
- Technological requirements: Implementation may require new technologies and tools that necessitate changes to existing systems.
Conclusion
Data products are a central component of modern data-driven companies. They enable decentralized data ownership, promote efficiency and support better decision-making. By applying the concept of "data as a product", companies can not only increase their efficiency, but also take their decision-making to a new level. In a world where data is having an ever-increasing impact on business success, well-designed data products are essential for sustainable growth and innovation. Data quality remains a remains a critical factor and should be continuously monitored and improved.