Despite years of progress, the gap between data collection and actionable insights remains a significant challenge. According to the NewVantage Partners Big Data and AI Executive Survey, while 92% of organisations are investing in data-driven projects, only 27% consider themselves truly data-driven. This disconnect highlights a critical opportunity: transforming raw data into data products that deliver tangible business value.
What Are Data Products?
Data Products are curated, trusted data insights designed for specific business domains. Unlike traditional reports or dashboards, they're built with a product mindset focusing on usability, reliability, and continuous improvement. They sit at the intersection of technology and business strategy, designed to answer specific questions and drive measurable outcomes.
Case Study: Supply Chain Transformation at a Global Fashion Retailer
Let's examine how a large online fashion retailer leveraged data products to tackle a costly business challenge: supply chain lead time optimisation.
The Business Challenge
Despite having advanced data infrastructure in place, the retailer struggled with supply chain logistics. Bottlenecks in the process were causing delayed deliveries, increasing costs, and frustrating both internal teams and customers. Leadership tasked the supply chain analytics team with identifying and reducing these lead times.
The Approach: SunBeam Framework
Rather than jumping straight to technology solutions, the team applied the SunBeam framework - a methodical approach to data product design that aligns business needs with technical capabilities.
1. Start with Why
The strategic goal was clear: reduce lead times and improve supply chain performance. This "why" grounded every design decision moving forward.
2. Domain Identification
With supply chain as the focus, the team identified "Logistics" as the primary domain, while also exploring adjacent areas like "Sourcing and Procurement" for potential collaboration.
3. Value Distillation
Drilling deeper, they identified three high-impact events in the logistics process:
- Supplier books carrier
- Company raises Advanced Shipping Notice (ASN)
- Supplier hands over product
These weren't just process steps, they were controllable events where the retailer had influence and could drive improvement.
4. Data Product Design
The team mapped these events with business stakeholders using accessible language - avoiding technical jargon like "fact tables" and instead focusing on questions like:
- What happened?
- When did it happen?
- Why did it happen?
- How many units were involved?
This collaborative approach ensured everyone, both technical and business stakeholders, shared an understanding of what the product needed to deliver.
The Outcome: Ubiquitous Analytics
The result was a curated data product that delivered:
- Interactive Dashboards in Power BI
- Filtering by supplier or carrier
- Comparison of actual performance vs. KPIs
- Visualisation of how missed targets impacted units or cartons
- Conversational Analytics with Databricks Genie
- Natural language interactions ("What's the impact of late confirmations?")
- Auto-generated queries available for review and learning
- Accessible analytics without SQL knowledge
- Excel Integration for Familiar Workflows
- Same governed data accessible in familiar tools
- Consistent with numbers from Power BI and Genie
- Support for analysts and legacy processes
Why This Matters: Beyond Dashboards
The most powerful aspect of this approach is that it transcends dashboards. By supporting multiple interaction modes from a single source of truth, the data product delivered:
- Clear insights into supplier and carrier performance
- Lead time KPIs tied to individual events
- Cross-slicing capabilities (e.g., "Show me all carriers who consistently deliver late for these suppliers")
Most importantly, business users accessed data in their preferred format (reports, Excel, or conversational analytics) driving adoption and extracting maximum value from the investment.
Start Your Data Product Journey
Transforming your organisation's approach to data doesn't happen overnight, but it can begin with a single, high-value data product. The key is starting with business needs rather than technology, and designing for the actual questions your stakeholders need answered.
At Advancing Analytics, we help organisations design, build, and launch data products that drive measurable business outcomes. Our approach combines deep technical expertise with business domain knowledge to create solutions that deliver immediate value while building toward a data-driven future.
Explore the use case in more detail through the accompanying video.
Ready to transform your approach to data? Let's talk about how data products can solve your most pressing business challenges.
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Author
Ust Oldfield