Building Business Value from Data Lakes: Real-World Examples of Composed Data Products
Let me share something I’ve been thinking about lately—the shift from viewing data lakes as massive storage repositories to understanding them as active foundations for composed data products. It’s a transformation that’s reshaping how organizations actually use their data. My colleague Haricharuan recently wrote a good blog on the fundamental foundations of data products: Data Products 101: What They Are, Why They Matter, How To Begin? – SOLIX Blog
What We’re Really Talking About Here
When I say “composed data products,” I’m describing something pretty specific: curated, business-ready datasets that combine raw information from multiple sources within your data lake, then package it in ways that business and AI applications can actually consume. Think of it as the difference between having ingredients scattered across your pantry versus having pre-prepared meal kits ready to cook.
Real-World Examples That Actually Work
Customer 360 Views in Retail
I’ve watched several retail organizations build what they call their “Customer 360” data products. Take a major omnichannel retailer—they’re pulling together:
- Point-of-sale transactions from physical stores
- E-commerce clickstream and purchase data
- Customer service interaction logs
- Loyalty program engagement metrics
- Social media sentiment data
The composed data product centralizes all this in their data lake environment, creating a unified customer profile that feeds directly into their marketing automation platform, customer service dashboards, and personalization engines. The business application doesn’t need to query six different systems anymore—it accesses one enriched, validated data product.
The practical impact? Their marketing team can now trigger personalized campaigns based on actual customer behavior across all channels, not just what happened in one silo.
Predictive Maintenance in Manufacturing
Here’s a use case that really demonstrates the power of composition. A manufacturing company I’ve followed builds a predictive maintenance data product by combining:
- Real-time sensor data from IoT devices on factory equipment
- Historical maintenance records and work orders
- Parts inventory and supply chain information
- Production schedules and output quality metrics
- External factors like weather patterns that affect equipment performance
This composed dataset feeds their maintenance scheduling application and production planning systems. The beauty is that the data engineering team handles all the complexity—cleaning sensor data, normalizing maintenance records, enriching with contextual information—and the business application just consumes a clean, analytics-ready product.
The outcome? They’ve reduced unplanned downtime by identifying equipment degradation patterns weeks before failure.
Financial Risk Assessment Products
In financial services, I’ve seen some sophisticated risk assessment data products. A mid-sized bank creates a composed credit risk product that integrates:
- Transaction history from core banking systems
- Credit bureau reports and scores
- Market volatility indicators
- Customer demographic and employment data
- Economic indicators tied to geographic regions
This centralized data product powers their loan origination system, portfolio risk dashboards, and regulatory reporting applications. Each business application gets exactly the view of risk data it needs, without anyone having to understand the underlying data lake architecture.
The compliance team particularly appreciates this approach because they can audit and validate one data product rather than tracking down how each application transforms raw data differently.
Additionally, governance teams can review the data product results to ensure that these systems are free of bias. I’ve written about this before (The Missing Piece in AI Governance: Fighting Bias In, Bias Out – SOLIX Blog). In a system as potentially sensitive as risk assessment, eliminating any consolidated data product bias is essential.
A Healthcare Analytics Example
One of the more compelling use cases I’ve encountered involves a healthcare network building population health data products. They’re composing:
- Electronic health records from multiple hospital systems
- Claims and billing data
- Pharmacy dispensing records
- Social determinants of health from community data sources
- Patient-reported outcomes from mobile apps
The composed data product feeds care management applications, identifies high-risk patients for intervention programs, and supports value-based care reporting. The clinical applications don’t need data engineering expertise—they just consume the validated, privacy-compliant data product.
The key insight here: the data lake environment allows them to maintain detailed clinical data at rest while the composed data product provides appropriately aggregated, de-identified views for different analytical purposes. As mentioned earlier, ensuring governance teams are monitoring for any biases that may be appearing is essential in healthcare-related systems that are using AI fueled by composed data products.
Supply Chain Intelligence in CPG
Consumer packaged goods companies are building supply chain optimization data products for AI applications that combine:
- Supplier performance metrics and delivery data
- Raw material costs and commodity price indices
- Production capacity and scheduling data
- Distribution center inventory levels
- Demand forecasting signals from retail partners
This composed product powers their procurement applications, production planning systems, and logistics optimization tools. The business users interact with applications that reflect a complete supply chain picture, while the underlying data lake handles the complexity of integrating data from dozens of suppliers, manufacturing sites, and distribution partners.
What Makes These Work in Practice
Great data products are discoverable (cataloged, tagged, and owned), addressable (stable URIs and versioned endpoints), secure (least-privilege access, masking, encryption), understandable (business glossary, lineage, examples), governed (policies as code, SLAs, retention or legal holds), and trustworthy (quality SLOs, audit trails, reproducible reads).
But there are other key attributes to delivering successful composed data products:
- Clear business ownership: Each data product has a defined business owner who understands the use cases and can validate that the composed data actually serves business needs.
- Governed data quality: The composition layer implements validation rules, handling missing data and ensuring consistency before business applications consume the product.
- Version control and lineage: When source data changes or composition logic evolves, there’s clear tracking of what changed and how it impacts downstream applications.
- Performance optimization: The composed data product is structured and stored in formats that balance query performance for business applications with storage efficiency.
- Access controls and compliance: Security and privacy rules are enforced at the data product level, so business applications inherit appropriate access controls without implementing them independently.
The Practical Benefits I’m Seeing
Organizations that successfully implement these composed data products report some tangible advantages:
They reduce the time to develop new business and AI applications because the hard work of data integration is already done. Their business intelligence teams spend less time wrangling data and more time generating insights. Data consistency improves because multiple applications consume the same composed product rather than creating their own transformations. And perhaps most importantly, their data governance becomes more manageable because they’re governing curated products rather than trying to control every direct access to raw data lake contents.
Looking Forward
The pattern I’m seeing suggests we’re moving toward data lake environments that function less like passive storage and more like active product factories like the Solix Data Lake Plus (SOLIXCloud Data Lake Solution | Unify Your Data). The raw data lives in the lake, but what business applications actually consume are these carefully composed, validated, business-ready data products.
It’s a nuanced but important distinction—and one that’s proving to make the difference between data lakes that deliver business value and those that become expensive data swamps.
