Introduction to Data Products: Everything you need to know

Introduction to Data Products: Everything You Need to Know

"Data product" is quickly becoming a staple term in modern data teams, but it’s still used in different ways depending on who you ask.  If you’re a data engineer, analytics leader, or analytics engineer looking to understand what a data product is, where to define one, how to monitor it, and how to roll them out effectively, this is your guide.

What Is a Data Product?

At its core, a data product is any output of a data team that delivers value to a user, whether that user is a person or a system. This can include:

• A dashboard used by the finance team
• A set of dbt models that power operational analytics
• A machine learning model that makes real-time decisions
• Customer-facing analytics embedded in a product

A data product packages data, logic, and context into a usable form. What makes it a "product" is the intentionality behind its creation: it has clear ownership, quality expectations, and downstream consumers. It’s not just about generating a dashboard or a dataset. It’s about creating something reliable, discoverable, and maintainable, with the same mindset you’d apply to shipping a software product.

Why Are Data Products Trending Now?

Several trends are converging:

• Complexity at scale: The median analytics deployment now includes hundreds of dbt models. Defining data products helps teams focus on what’s most critical.
• Ownership aligned to business outcomes: Teams need ownership boundaries drawn around data as it’s used by the business. The makes escalation paths and stakeholder communications much more strraightforward”
• Business-critical data: Data is no longer just powering internal dashboards — it’s driving customer experiences, revenue decisions, and machine learning outcomes.

This evolution is forcing data teams to borrow principles from software engineering: encapsulation, observability, SLAs, and incident management. Think of a data product like a piece software. If you wouldn’t ship software without monitoring, you shouldn’t ship a data product without observability.

Where Should You Define a Data Product?

A good rule of thumb: define a data product as close to where it’s used as possible. If the product is a dashboard used by finance, the data product should include the dashboard and the final data marts feeding into it, not layers further upstream.

Common options include:

• dbt: Use folder structures or exposures to define data products. E.g., everything in a folder like models/marts/analytics can represent the analytics data product.
• Your BI tool: With tools like SYNQ, you can define data products at the dashboard level, regardless of whether you use Looker, Tableau, or Omni. SYNQ will infer the upstream lineage.
• Data catalogs: If you have a tool like Atlan or Collibra, you can define data products centrally, and tools like SYNQ will ingest those definitions and activate observability.
• Data observability platforms: Within some data observability platforms like SYNQ, you can define data products and see a quality score for each of the products.

In short, there’s no single “right” place to define a data product, it depends on your tooling, team structure and end users. What matters most is consistency and discoverability.

Who Owns Data Products?

There are two types of data products:

• Producer data products: Upstream assets like users or transactions tables, typically owned by platform or data engineering teams.
• Consumer-facing data products: Dashboards, ML models, and forecasts, often owned by domain-aligned data teams.

Ownership should be defined at the team level — e.g., "Marketing Data" instead of a single analyst. That way, responsibility is resilient to personnel changes.

More importantly, make the responsibilities of ownership explicit. At SYNQ, being an owner means:

• Notifying impacted stakeholders if something breaks
• Managing and resolving incidents
• Documenting fixes and implementing preventative tests

Without clarity, expectations vary and data quality suffers.

How Do You Monitor a Data Product?

Monitoring starts with defining the full lineage of the data product, not just the final dashboard or table, but everything upstream. That includes raw ingestion, transformations, joins, aggregations, and business logic.

Effective observability means understanding:

• What’s broken?
• Who is impacted?
• Where did it originate?
• How do we fix it?

Testing Strategy

Use a layered testing approach:

• Source Layer (dbt sources): Most tests should go here. These catch upstream issues early.
• Staging Layer: Apply intentional, logic-aware tests (e.g., checking a derived field after a transformation).
• Marts Layer: Add business logic checks (e.g., revenue should never be negative).‍
• Known unknowns: Tests for nulls, uniques, and schema changes — best handled in dbt or SQLMesh.

Pair this with anomaly detection:

• Unknown unknowns: Anomaly monitors for volume, freshness, and outliers — SYNQ’s built-in monitors catch these automatically.

Severity Tiers

Not all data products are created equal. Classify them (e.g., P1, P2, P3) and align monitoring expectations accordingly. A revenue dashboard deserves more stringent SLAs than a weekly social media report.

• P1: Business-critical. Revenue dashboards, billing metrics, ML fraud detection models. Require real-time alerting and fast incident resolution.
• P2: High visibility, medium impact. Executive KPIs, operational dashboards.
• P3: Low criticality. Social media reports, internal experimentation reports.

This classification also informs how you respond to incidents. A P1 failure should trigger immediate action. A P3 issue might just be logged for review.

How to Get Started with Data Products

You don't need to sort everything into data products to get started. We recommend you start small and expand from there:

1. Identify your most critical data products. Ask business leaders and align on top priorities.
2. Define them in your modeling layer, BI tool, or catalog.
3. Assign clear ownership and severity tiers.
4. Deploy layered monitoring using tools like SYNQ.
5. Track incidents and iterate. Review test effectiveness and adjust your strategy over time.

We recommend following SYNQ’s data product observability workflow. It’s built for this exact process.

Real-World Examples of Data Products

From internal dashboards to embedded analytics to ML-driven fraud detection, data products are already everywhere. Check out our real-world examples of data products to get inspired.

Data products bring clarity to modern data work. They help you focus on what matters, build trust with stakeholders, and scale quality across your stack.

If you're ready to operationalize data products, SYNQ can help.