From Ore to Ornaments
Why You Can’t Monetize Data You Haven’t Refined
In the hypercompetitive e-commerce and SaaS worlds, data monetization is the new gold rush.
Whether it’s an e-commerce platform selling global insights back to its vendors or a Point of Sale (POS) provider offering premium analytics to merchants, the vision is the same: turning "digital byproducts" into high-margin revenue streams.
However–as Data, Analytics, and AI Consultants–we often see our clients attempt to make “fine jewelry” from their data when they can’t even “smelt their ore.” We’ve seen POS clients try to ship customer-facing dashboards when—paradoxically—they couldn’t tell you how many customers they have on their own platform. This is fine for an MVP but leads to inconsistent metrics and wasted developer time as they try to scale to new customers or launch new features.
DATA SOURCE
Operational DB, Sales/POS, 3rd Party Apps (Meta/Google Ads)
Extraction
DATA ASSET
Raw Data, Data Lake, Data Warehouse
DATA MONETIZATION
Premium Data Products. Data-as-a-Service. Customer facing analytics.
To help our clients navigate this, we have developed a simple, four-stage framework: Mining for Gold with Your Data.
1. Data Source: Gold Mine
Owning an operational database or a stack of third-party marketing tools (Meta, Google Ads, etc.) is like owning the rights to a gold mine.
It’s a prerequisite, but it isn’t wealth. Just because you have the "land" doesn't mean you have the gold. At this stage, your data is trapped in silos, often messy, and completely inaccessible to the average business user. You own the rights to your data, but functionally, it might as well belong to the vendor(s).
2. Data Asset: Gold Ore
Once you perform the "extraction" (your ETL/ELT processes), you have gold ore. The data is now in your warehouse or data lake. You possess it, but it’s still raw.
It’s full of "dirt": duplicates, null values, and inconsistent schemas. While it technically has value, it isn't ready for the market. You cannot hand a bag of dirt and rocks to a customer and call it a premium product.
3. Data Product: Gold Bullion
This is the "missing link" for most companies. You need to refine your ore into gold bullion—pure, standardized bars, before you can make jewelry and other premium products.
In data terms, this is your Internal Business Intelligence (BI). It looks like:
Data Marts: Organized, subject-specific sets of data.
Data Mesh: Distributed ownership of clean data.
Internal Reporting: Knowing your own total customer count and churn rate.
The Hard Truth: If you haven’t refined your data enough to run your own business, you aren't ready to sell it to someone else. Internal BI is the "stress test" for the quality of your data. It’s the processing that makes data monetization scalable.
4. Data Monetization: Gold Jewelry
Finally, we reach the jewelry. This is where you take your refined gold bars and craft them into something consumer-friendly: a sleek dashboard, a trend report, or a "Premium Tier" analytics suite.
When you try to jump from Ore (Step 2) directly to Jewelry (Step 4), the product breaks. The dashboards show conflicting numbers, the data is stale, and your customers lose trust.
DATA SOURCE
Operational DB, Sales/POS, 3rd Party Apps (Meta/Google Ads)
DATA ASSET
Raw Data, Data Lake, Data Warehouse
DATA PRODUCT
Cleaned, Transformed Data. Data Marts, Data Mesh. Internal BI.
DATA MONETIZATION
Premium Data Products. Data-as-a-Service. Customer facing analytics.
Why the "Refinement" Phase is Non-Negotiable
Building your internal BI (Data Products) first isn't just about being "thorough"—it’s about scalability and customer value.
When you build a customer-facing data product on top of well-structured data marts, you are building from a single source of truth for clean, valuable data. If your "gold" is pure, you can make 100 different types of jewelry from it. If you try to build each jewelry piece directly from raw ore, you'll spend all your time cleaning the same dirt over and over again. This translates to longer development time for new features, inconsistent reporting, and loss of trust. In short: you pay your developers more for a worse end product that churns customers.
More simply put, adding a standardized, business intelligence layer between your data assets and your customers gives you simpler, faster debugging tools; quicker feature delivery; and smart decision making on new customer facing data products. You can’t even prioritize new data monetization features if you don’t know anything about your customers.
The Lesson: Before you look for external buyers, look at your internal dashboards. If you can't trust your own numbers, your customers won't either.