What you don’t know can hurt you. It is entirely possible to get by just fine with a blind spot if you know it is there. That’s why we glance over our shoulders when we change lanes. The true risks (and by the same token opportunities) are the gaps that we are unaware we have, the “unknown unknowns”. They are the missing insights or sudden bottlenecks that can catch you entirely unprepared.
So, how do you find something you don’t know to look for? You have to regularly set aside the time to be on the lookout.
From “Unknown unknowns” to Opportunities
Day-to-day, you and your team are focused on what’s directly in front of you—the “knowns.” The problem is, you stay in that lane until something forces you out. To see the bigger picture, you need to actively set aside time to ask a critical question: “What assumptions are connecting our data?” Focusing on these connections forces you to see beyond individual datasets and uncover the hidden logic that ties your business operations together.
Consider a simple example.
Your company has perfectly functional Inventory, Purchase Order, and Sales datasets:
| Inventory | Purchase Order | Sales |
|---|---|---|
| Item | Item | Item |
| Count | Date | Date |
| Shelf life | Cost | Amount |
| Price | Vendor | Customer ID |
Each dataset serves its purpose well. But when you start questioning how they connect, powerful new insights emerge:
- Question: Can we tell the story of an item from Purchase Order all the way through Sales?
- Insight: We’re assuming the process happens smoothly but are there bottlenecks or un-captured efficiencies? We need better data on stocking labor to connect our Purchase Orders with our active Inventory. This could reveal staffing inefficiencies or hidden costs.
- Question: How can we know based on Sales the perfect moment to place a new Purchase Order?
- Insight: We’re assuming our timing is good enough. By capturing Purchase Order lead times and connecting that data to Inventory shelf life, and Sales data, we can forecast the optimal reordering point to minimize waste and prevent out-of-stocks.
These examples might be straightforward, but the principle is universal. Datasets are always created with a specific purpose in mind. Their true power, however, is unleashed when you look beyond their specific domain. By intentionally exploring the connections between different areas of your business, you can start to see the complete story your data is telling—from end to end.
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