General considerations
General Considerations for Serving Data
When serving data, the primary goal is effectiveness and adoption. Regardless of the specific technical architecture employed, several foundational considerations must be addressed to ensure that data delivers value to the organization.
1. Trust as the Foundation
The most critical element of data serving is stakeholder trust. Even the most sophisticated architecture is useless if end users do not believe the data accurately represents reality. Once trust is lost, it is exceptionally difficult to regain and often leads to the failure of data projects.
Quality Assurance: Trust is built through rigorous data validation (checking accuracy against reality) and data observability (monitoring pipeline health).
SLAs and SLOs: Engineers must establish Service Level Agreements (SLAs) with users. These contracts define expectations regarding uptime and data quality. Service Level Objectives (SLOs) are the specific metrics used to measure adherence to these agreements (e.g., "99% uptime").
2. Focus on Use Cases and Users
Data engineers should avoid "building in a vacuum" by focusing solely on tools. Instead, the process should work backward from the user and the use case.
Action over Observation: The highest ROI comes from data that triggers automated actions or strategic decisions, rather than passive reporting.
Target Audience: Engineers must identify exactly who the user is (executive, analyst, or machine) and what specific problem they are trying to solve.
3. The Data Product Mindset
Data should be treated as a product designed to facilitate a specific end goal.
Jobs to be Done: Successful data products are built to help a user complete a specific "job."
Feedback Loops: Good products incorporate feedback loops where increased usage generates metadata or insights that improve the product further.
Adoption: If a data product is not adopted, it has failed. Engineers must ensure product/market fit, whether the "market" is internal stakeholders or external customers.
4. The Challenges of Self-Service
While self-service data is a common aspiration, it is difficult to implement effectively.
Audience alignment: Not all users want self-service; executives often prefer finished metrics, while analysts prefer raw access. Self-service works best for "data-savvy" business users.
Guardrails: Successful self-service requires a balance of flexibility and restrictions to prevent users from generating incorrect insights.
5. Data Definitions and Logic
Correctness involves more than just copying source data; it requires agreed-upon business logic and definitions.
Explicit Definitions: Terms like "Churn" or "Gross Revenue" must have single, codified definitions to prevent discrepancies across departments.
Semantic Layer: Relying on institutional (tribal) knowledge is dangerous. Logic should be formalized in a semantic layer or data catalog so that business rules are written once and reused consistently everywhere.
6. Data Mesh Architecture
Modern data serving is moving toward a decentralized "Data Mesh" approach. In this model, domain teams (e.g., sales, marketing) are responsible for two things:
Consuming data from other domains to suit their specific needs.
Serving data as a polished product to the rest of the organization.
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