“We’re deciding between Snowflake and Databricks — can you help us design the right architecture?”
That’s how a lot of our first calls begin. Prospects want to talk tech: lakehouse vs. warehouse, dbt vs. Informatica, Power BI vs. Looker. But the moment we ask about business needs, data trust, or source system readiness, the picture changes.
One client told us they’d already captured all their business requirements and wanted to start with design. Weeks later, we were running multi-day working sessions with users across geographies just to gather the foundational use cases that had been missed.
We spent more time on requirements and data profiling in that project than almost any other — and because we did, it succeeded.
Here’s the reality:
The first 15–20% of a data project determines whether it will succeed — with 100% accuracy.
Do discovery right? You deliver on-time, on-budget, with something that solves the real problem and gets adopted.
Skip it? You will fail. Guaranteed.
Discovery Is More Than Requirements
It’s about aligning business needs to the right technical solutions — grounded in a clear-eyed understanding of your organization’s data, culture, and constraints.
That’s why every Datagize engagement begins with our Discovery & Data Health Framework:
| Phase | Focus | Key Deliverables |
|---|---|---|
| 1. Business Context | What decisions are we supporting? | Use case inventory, success metrics |
| 2. Data Profiling & Quality | Can we trust the data? Where are the gaps? | Data quality assessment, profiling summary |
| 3. Governance & Ownership | Who owns what? Is there clarity and stewardship? | RACI matrix, data domain model, lineage maps |
| 4. Security & Privacy | Are we compliant and protected? | Access controls, classification strategy |
| 5. Metadata & Lineage | Can users trace what they’re seeing? | Glossary, transformation lineage, semantic mapping |
| 6. Architecture Fit | What will work in your environment? | Stack recommendations aligned to your org’s maturity |
| 7. Organizational Readiness | Do we have the skills and buy-in to succeed? | Capability gap analysis, enablement roadmap |
| 8. Prioritization | What should we build first, and why? | Feasibility vs. value scoring, roadmap |
| 9. MVP Design | How do we learn fast before scaling? | Prototype or working PoC with feedback loop |
Tools Don’t Deliver Value. Clarity Does.
Architectural decisions — whether it’s Snowflake, Databricks, BigQuery, or Synapse — should come after you’ve clarified the problem you’re solving.
Too many teams burn time and budget chasing trends instead of solving the problems that matter.
With a strong discovery foundation, the right architecture emerges naturally.
Let’s Talk Before You Build
If you’re planning a data transformation, Datagize can help you lay the groundwork for success — and avoid the rework and regret that come from skipping discovery.
