For a long time, we have treated data like a byproduct of doing business. It was something that happened in the background, eventually getting dumped into massive data lakes or warehouses, where it often became a stagnant swamp. In the financial world, this led to a familiar headache: analysts spending 80% of their time just trying to find and clean data before they could even start a risk assessment or a fraud investigation.
But we are seeing a fundamental shift. Organizations are moving away from simply collecting data and are instead building data products. A data product is not just a raw table or a PDF report. It is a self-contained, reusable asset that combines clean data with the metadata, code, and infrastructure needed to make it immediately useful for a specific business goal.
Think of it as the difference between giving a chef a basket of random, unwashed vegetables versus a prepped, gourmet meal kit. One requires hours of labor before the work even begins; the other is designed for a specific outcome.
The Real-World Payoff
In highly regulated industries like banking and insurance, the benefits of this approach are concrete. One multinational banking group found that treating its risk data as a product reduced regulatory reporting time by 41%. Because the data was already standardized and its lineage was clear, they didn't have to scramble every time an auditor asked where a number came from.
The benefits ripple out to other areas, too:
- Fraud Detection: In insurance, correlating data across previously siloed domains into a single product improved fraud identification accuracy by 37%.
- Healthcare: Clinical domains that created patient data products saw a 29% improvement in care coordination.
- Retail: Unifying customer data allowed retailers to increase promotional effectiveness by 32% by understanding shoppers' actual journeys across digital and physical stores.
| Industry | Data Strategy Implementation | Impact / Result |
|---|---|---|
| Insurance | Correlating data across siloed domains into a single product | 37% improvement in fraud identification accuracy |
| Healthcare | Creating dedicated patient data products within clinical domains | 29% improvement in care coordination |
| Retail | Unifying customer data to understand journeys across digital and physical stores | 32% increase in promotional effectiveness |
Why Governance is the Secret to Success
There is a common fear that decentralizing data ownership leads to "analytical chaos," where everyone has their own version of the truth. This is where Federated data Governance comes in.
Modern governance isn't about being the "data police" who say no to everything. Instead, it acts like a federal constitution. It sets global standards: for example, how we define a "Customer ID" or how we encrypt sensitive PII (Personally Identifiable Information): while allowing individual teams the autonomy to build their products their way.
By using "policy-as-code," we can automate these rules. If a data product contains sensitive financial information but lacks the required compliance labels, the system can automatically block its deployment. This ensures that doing the right thing becomes the default path.
Getting Data Products Right: Best Practices
To avoid getting stuck in endless design meetings, the best teams follow a few simple rules:
- Work Backwards from the Goal: Don't start with what data you have. Start with the problem you are trying to solve. If you need to predict customer churn, identify exactly what information is needed for that prediction and build the product to serve that need.
- Define a "Job Description" for the Data: If you can't describe what a data product does in one or two simple sentences, it is probably too bloated. A good product represents a single cohesive concept, such as "Historical Purchases" or "Credit Risk Exposure".
- Use Data Contracts: These are formal agreements between the people providing the data and the people using it. They define the schema, quality standards, and service-level objectives (SLOs). If the data is late or a field changes, the contract makes it clear who is responsible for the fix.
The Challenges and the "Hidden" Risks
Transitioning to this model isn't easy. The biggest hurdle is rarely technical; it is cultural. Breaking down the silos between IT and the business units requires teams to take on new responsibilities. Domain teams often face a skills gap and suddenly need to understand data engineering and product management.
There are also real dangers if we get it wrong. Weak governance can lead to "data anarchy," where privacy is compromised. For instance, if subscriber data is copied across domains without clear lineage tracking, complying with deletion requests, such as those required by the GDPR, becomes impossible.
Perhaps most importantly for finance, there is the risk of unconscious bias. One major financial institution deployed an AI model for credit limits that ended up discriminating against women. The root cause was that the team used a data product without understanding its context: the historical data they used contained biased legacy decisions. This highlights why a data product must be self-describing, i.e., including documentation that explains not just what the data is, but where it came from and its limitations.
Moving Forward
Ultimately, scaling a data product model is about building trust. When your teams can find, understand, and use high-quality data without waiting weeks for a central IT team, you unlock a new level of agility. Whether you are optimizing a supply chain or personalizing a banking app, treating data as a first-class product ensures that your business decisions are powered by reality, not intuition.
