Data as a Product: Cultural Resetting of Data Perspective
Beyond the traditional data pipeline engineering, this is a fundamental shift in how we build, own, and ensure trust in data.
🚨 Chaos in the Dashboard: A Familiar Scenario
It’s Monday morning. Leadership is reviewing the quarterly business dashboard when a problem emerges:
- The revenue numbers don’t match between Finance and Sales dashboards.
- A column is missing from the data feed used by Marketing for campaign analytics.
- A critical ML model retraining job has failed due to nulls in the target variable.
Everyone turns to the data team, but the questions begin piling up:
“Who owns this table?”
“Why did the schema change?”
“Was this data validated before the refresh?”
“Can someone fix it—now?”
No one has a clear answer. Teams scramble through Slack threads, stale documentation, and broken lineage links. Meanwhile, decisions get delayed, trust erodes, and engineers burn out firefighting yet again.
🎯 Where Did It Go Wrong?
At the heart of the issue is this:
Data is being treated as an exhaust of systems — not as a product that people rely on.
Unlike APIs, microservices, or user-facing apps, data lacks ownership, governance, usability standards, and quality guarantees. And yet, everyone—from analysts to ML engineers to executives—depends on it.
This misalignment is why the industry is turning toward a new cultural paradigm:
Data as a Product
đź“š Origin of the Term
The idea of “Data as a Product” gained popularity through Zhamak Dehghani, who introduced it in her Data Mesh whitepaper (2019). The concept emphasized that in a distributed data architecture, each data domain should treat its datasets as discoverable, reliable, and usable products.
Though the term has been floating around since the early 2010s, it’s post-2020 that the industry began adopting it at scale, especially as Data Mesh and decentralization models challenged traditional centralized data architectures.
🚀 Why Is It Gaining Traction Now?
Several industry shifts have created the perfect storm:
| Trend | Impact on Data Needs |
|---|---|
| Explosion of data volumes | More producers, more consumers, more complexity |
| Rise of ML & analytics | Data needs to be accurate, timely, and reusable |
| Domain-oriented architectures | Teams need autonomy but also quality standards |
| Shadow IT & data chaos | Businesses creating their own pipelines in the absence of governance |
| Real-time & self-service demands | Consumers expect reliable, fast-access data |
The realization is clear: data can’t be a side effect anymore. It must be treated like a first-class product.
🔄 Shifting Perspective: From Pipeline to Product
| Traditional Data Practice | Data as a Product Approach |
|---|---|
| Central team manages all data | Domain teams own and publish data products |
| Pipelines are built ad-hoc | Data is versioned, documented, and designed for reuse |
| Testing is patchy and reactive | Embedded validation, observability, and contracts |
| Consumers are passive recipients | Consumers are customers with needs and expectations |
| No clear ownership or escalation | Every dataset has a defined product owner |
✅ How It Solves Today’s Data Challenges
1. Improved Trust & Quality
- Data products come with quality guarantees, validations, and lineage.
- Reduces data debt and breakage anxiety in downstream systems.
2. Clear Ownership & Accountability
- If something breaks, it’s clear who owns the fix.
- Enhances collaboration between producers and consumers.
3. Scalability
- Different domains can create and manage their data products autonomously.
- Platform teams can standardize tooling, not content.
4. Faster Time to Insight
- Discoverable, documented data products accelerate analytics and ML development.
⚖️ Pros and Cons of Data as a Product
| Pros | Cons / Challenges |
|---|---|
| Clear ownership & reduced chaos | Requires organizational and cultural change |
| Higher data quality & consumer trust | Upfront effort for documentation, contracts, testing |
| Scalable, reusable assets for analytics & ML | Tooling maturity varies across organizations |
| Enables domain autonomy & self-serve analytics | Can lead to inconsistent standards without governance |
| Easier incident response & root cause analysis | Hard to retroactively apply to legacy monoliths |
🧰 Tools That Help Enable This Mindset
Treating data as a product doesn’t only require a mindset change—it needs platform and tooling support too.
| Capability | Tools / Platforms |
|---|---|
| Data Catalog & Discovery | DataHub, Amundsen, Atlan, Collibra |
| Data Quality & Testing | Great Expectations, Soda, Deequ, Monte Carlo |
| Data Contracts | OpenMetadata, Tonic.ai, or custom schema validation |
| Orchestration | Airflow, Dagster, Prefect |
| Lineage & Observability | OpenLineage, Marquez, Databand, Datafold |
| Governance & Access Control | Immuta, Okera, built-in cloud platform policies |
🧠How to Get Started
If you’re just starting out, here’s a simple phased approach:
- Pick 1 or 2 critical datasets and assign a data product owner.
- Define expectations (schema, freshness, consumers).
- Implement basic validations and documentation.
- Register it in a catalog with metadata and contact info.
- Start capturing feedback from consumers like a real product.
You don’t need a full Data Mesh architecture to apply this.
You just need to own your data like it matters — because it does.
✨ Final Thoughts: This is a Cultural Reset
“Data as a Product” isn’t just a technical change — it’s a cultural reset.
It’s about shifting how we value, manage, and deliver data in a world where every business decision depends on it.
If data is the new oil, it’s time we start refining it like a product — not letting it spill all over the place.
Have you started thinking of your datasets as products? What roadblocks or wins have you experienced? Drop your thoughts or share your journey with your team. This reset begins with small, intentional steps.