What is a data product?

Every application creates and stores data—sometimes temporarily, often permanently. This data is not just a byproduct; it's the lifeblood of modern business operations. From fueling error logs to powering health monitoring systems, data's role extends far beyond the application itself.

Traditionally, centralized data teams have been tasked with handling this data, implementing extract, transform, and load (ETL) processes to manage it. Meanwhile, operations teams have had their hands full with managing the data flows required for application health and key performance indicators (KPIs).

But this division of labor is where challenges start to emerge.

The Problem with the Traditional Approach

The traditional, waterfall methodology for data integration can lead to significant issues. Ever experienced these?

• Knowledge Gaps: Centralized teams often lack the deep domain knowledge needed to handle data intricately.
• Ownership Confusion: Who really owns the data? This question often goes unanswered.
• Communication Conflicts: Misalignment between teams can result in data quality degradation, untimely deliveries, and, ultimately, flawed decision-making.

These issues don't just slow down processes; they erode the very value your data is meant to provide.

Enter: Data as a Product

To tackle these challenges, a data mesh approach is gaining traction. This approach redefines how we treat data—viewing it not as a mere byproduct but as a fully-fledged product within its respective domain.

In this new paradigm, the application owners and their teams are directly responsible for the data their applications produce. They ensure it is fit for purpose, ready for analytical consumption, and maintains high standards throughout its lifecycle.

But what exactly does this mean?

Data Products: What Are They?

A data product is more than just raw data thrown over the fence. It is meticulously designed to meet specific analytical needs. Think of it as a product with:

• Predefined Shapes: Data is organized and formatted to align with consumption needs.
• Consumption Interfaces: Clearly defined ways for other systems or users to access and utilize the data.
• Maintenance Schedules: Regular updates and quality checks are baked in to keep the data reliable.

These data products are processed, shaped, cleansed, aggregated, and normalized. They’re not released into the wild until they meet stringent quality standards, ensuring that when they are finally available for use, they are both trustworthy and valuable.

What Makes a Good Data Product?

Not all data products are created equal. The best data products share some key characteristics:

1. Discoverable and Understandable
2. Addressable and Secure
3. Interoperable and Valuable

By adhering to these principles, you create a single source of truth within your organization—minimizing data duplication and maximizing reliability.

Designing and Implementing Your Data Products

Creating effective data products requires more than just a shift in mindset; it demands new skills and tools within your domain teams. Here’s how you can start:

1. Equip Your Teams: Invest in the right technology stack. For large domains, consider using a dedicated platform like Azure Synapse Analytics. For smaller domains, a shared platform like Azure Data Factory might be more appropriate.
2. Skill Development: Ensure your teams are trained not just in data management but also in product thinking. They need to understand the lifecycle of a data product, from creation through maintenance to eventual deprecation.
3. Iterate and Improve: Start small, with a few data products, and refine your approach based on feedback. Remember, the goal is to create data products that are not just useful but integral to your organization’s success.

The Future of Data Management

The transition to treating data as a product won’t happen overnight, but the benefits are clear. By aligning your data strategy with the principles of the data mesh, you’re not just improving the quality and reliability of your data—you’re transforming it into a competitive asset.

Are you ready to make the shift? Start today by rethinking how your organization handles its data. The results will speak for themselves.

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