Data as a Product: Why Treating Data Like Code Isn’t Enough

For years, organizations have been told to treat data like code. The logic made sense: apply DevOps practices—version control, CI/CD pipelines, testing frameworks—to manage the complexity of modern data ecosystems. While this approach has helped automate workflows and improve governance, it falls short of addressing a bigger truth: data is not just an engineering artifact—it is a product with consumers, value, and lifecycle management needs.

Enter the concept of Data as a Product (DaaP)—a mindset shift that goes beyond tooling and process, requiring organizations to reimagine how they produce, deliver, and maintain data for real-world consumption.

This blog explores why treating data like code isn’t enough, what it really means to think of data as a product, and how businesses can adopt this paradigm to maximize the value of their data investments.

The Limits of “Data as Code”

The “data as code” philosophy has shaped much of modern data engineering. By borrowing practices from software engineering, teams introduced:

• Infrastructure as Code (IaC): automated provisioning of cloud environments.

• DataOps: continuous integration and deployment for data pipelines.

• Testing frameworks: validating schema, lineage, and pipeline logic.

• Versioning and reproducibility: ensuring repeatability of transformations.

These practices provide rigor and reliability. However, when data is viewed only as “code,” critical dimensions get overlooked:

1. Consumer perspective is missing. Code is written for machines; data is consumed by people and applications. Treating it solely as code ignores usability and trust.

2. Focus on pipelines, not outcomes. Data teams optimize for delivering pipelines instead of ensuring that business teams can actually use and derive value from the data.

3. Invisible ownership. Code typically has an owner, but in many organizations, no one “owns” the dataset once it’s produced. This leads to orphaned, stale, or poorly maintained datasets.

4. Quality vs. utility. Code can pass tests yet still fail to deliver meaningful insights if the data it outputs is incomplete, poorly documented, or hard to access.

In short, engineering efficiency doesn’t automatically translate into business value. That’s where the product mindset comes in.

What Does “Data as a Product” Mean?

Treating data as a product means applying product management principles to data assets. Instead of pipelines being the end goal, data itself becomes the deliverable—one that is discoverable, usable, and valuable to its consumers.

A data product can be:

• A clean, curated dataset (e.g., a customer 360 view).

• A feature set for machine learning models.

• An API delivering real-time insights.

• A dashboard powered by governed, validated data.

Key characteristics of a data product:

1. Ownership and stewardship. Each data product has a clear owner accountable for quality, availability, and lifecycle.

2. Consumer-centric design. Data products are built with user personas in mind—analysts, data scientists, business teams—ensuring relevance and usability.

3. Discoverability. Like software products in an app store, data products must be searchable and easily accessible.

4. Documentation and metadata. Consumers need context—what the data means, how it’s sourced, how often it’s updated.

5. Quality and reliability. Beyond passing pipeline tests, data must meet standards for accuracy, completeness, and timeliness.

6. Lifecycle management. Just like apps, data products evolve, get updated, and eventually get deprecated.

This shift makes data teams think less like engineers shipping code and more like product managers delivering value to stakeholders.

Why Treating Data as a Product Matters

1. Build trust and adoption. Business users are far more likely to trust and use data when it is packaged, documented, and owned like a product.

2. Reduce duplication and waste. Without productization, organizations often rebuild the same datasets repeatedly in silos. Data products provide reusable, single sources of truth.

3. Accelerate time-to-value. Instead of waiting for engineering teams to build ad hoc pipelines, users can self-serve from a catalog of ready-to-use data products.

4. Enable scale. As organizations adopt architectures like data mesh, treating data as a product ensures federated teams can independently produce and consume trusted datasets.

5. Bridge business and tech. Product thinking forces alignment between business needs and data outputs, eliminating the gap between “what was built” and “what was needed.”

Data as Code vs. Data as a Product: A Comparison

Aspect	Data as Code	Data as a Product
Primary focus	Automation, reproducibility, pipeline reliability	Usability, value, and consumer adoption
Success metric	Build pipelines that run without failure	Deliver trusted, discoverable, and valuable datasets
Ownership	Engineering team responsibility	Product owner accountable end-to-end
Consumer perspective	Often ignored	Central to design
Lifecycle	Focus on deployment	Includes maintenance, updates, and deprecation
Tools & frameworks	Git, CI/CD, IaC, DataOps	Data catalogs, governance, product roadmaps, SLAs

How to Implement Data as a Product

Transitioning from “data as code” to “data as a product” requires more than just new tools—it’s an organizational mindset shift. Here’s how to begin:

1. Define Clear Ownership

• Assign data product owners responsible for each key dataset or domain.

• Ensure accountability for quality, governance, and consumer satisfaction.

2. Establish Data Product Design Principles

• Create templates for what every data product should include: documentation, quality metrics, lineage, and intended consumers.

• Treat metadata and documentation as part of the deliverable.

3. Invest in Data Discovery and Cataloging

• Implement a data catalog that allows teams to search, evaluate, and request access to data products.

• Include ratings, usage stats, and user feedback like a true product store.

4. Shift from Projects to Products

• Replace one-off “data pipeline projects” with ongoing data product roadmaps.

• Track adoption, iterate based on user feedback, and improve continuously.

5. Apply SLAs and Observability

• Define service-level agreements (SLAs) for freshness, accuracy, and uptime of data products.

• Implement observability tools to monitor quality and proactively address issues.

6. Foster Cross-Functional Collaboration

• Encourage data producers (engineering teams) and data consumers (business, analytics, data science teams) to collaborate regularly.

• Introduce “data product councils” to prioritize which products matter most to the business.

Case Example: Customer Data in Retail

A global retailer wanted a single customer view to support personalized marketing. Initially, engineers treated it as a pipeline project: ingest CRM data, transform it, and expose it in a warehouse.

The result? The data was incomplete, poorly documented, and adoption was low.

When the company pivoted to data as a product:

• A data product owner was assigned to the customer dataset.

• Documentation included definitions (e.g., what counts as an “active customer”).

• An SLA was established: daily refreshes with >98% completeness.

• A data catalog entry made it discoverable to analysts and marketing teams.

Outcome: adoption increased 3x, campaigns became more targeted, and customer churn decreased by 12% within a year.

Data as a Product in the Age of Data Mesh

The data mesh architecture popularized the idea of treating data as a product within domains. Each domain team is responsible for producing high-quality, discoverable data products that others can consume.

• Without product thinking, data mesh fails. If datasets aren’t owned, discoverable, and reliable, federating responsibility just spreads chaos.

• With product thinking, data mesh scales. Each domain produces trusted, usable datasets like building blocks in a larger ecosystem.

Thus, Data as a Product is the cultural foundation that makes architectures like data mesh sustainable.

Common Challenges and How to Overcome Them

1. Cultural resistance.

   • Challenge: Engineering teams may resist shifting to a product mindset.

   • Solution: Start with small wins—choose a few high-value datasets, appoint owners, and showcase impact.

2. Undefined ownership.

   • Challenge: No one wants to take responsibility for long-term stewardship.

   • Solution: Align ownership with domain teams and incentivize adoption metrics.

3. Tooling gaps.

   • Challenge: Legacy warehouses lack support for catalogs, metadata, and observability.

   • Solution: Adopt modern platforms that embed product features (e.g., Snowflake, Databricks, Alation, Collibra).

4. Measuring success.

   • Challenge: How do you prove ROI?

   • Solution: Track adoption rates, data trust scores, reduced duplication, and business outcomes influenced by data products.

The Future of Data as a Product

As organizations mature, data products will become as standard as APIs or microservices. We’ll see:

• Data product marketplaces. Internal catalogs that look like app stores, complete with ratings, reviews, and usage stats.

• AI-driven discoverability. Automated tagging, lineage, and quality scoring to reduce friction.

• Composable analytics. Teams building solutions by stitching together reusable data products instead of building pipelines from scratch.

• Monetization opportunities. External-facing data products offered to partners, customers, or even as standalone revenue streams.

Ultimately, organizations that treat data only as code will optimize for efficiency but leave value untapped. Those that embrace data as a product will turn data into a competitive differentiator.

Conclusion

Treating data like code was an important evolutionary step—it brought discipline, automation, and rigor to a chaotic field. But in today’s landscape, where business teams demand self-service, trust, and usable insights, it isn’t enough.

Data as a Product elevates data from a backend artifact to a consumable, valuable asset. It requires ownership, consumer-centric design, lifecycle management, and continuous improvement.

Organizations that embrace this mindset will not only streamline operations but also unlock the full business potential of their data—creating ecosystems where data isn’t just delivered, but truly adopted, trusted, and valued.