SI Series, Part 6: Data conversion nightmares: Converting for the future vs. quick fixes

The project timeline shows green. User acceptance testing passed. Go-live was a success.

Three weeks later, your geology team discovers that half their stratigraphy data is unusable. The depths are there, but the complex geological structures that took years to map have been flattened into generic categories that tell them nothing about what's underground.

Welcome to the hidden cost of data conversion shortcuts.

The Quick Fix Trap

Under deadline pressure, it's tempting to flatten data models, skip reconciliations, or drop fields that "might not be needed." The logic feels sound: get the system working with essential data, fix the details later.

The reality: That 20% of "complex" data usually turns out to be what your most experienced people depend on to do their jobs.

Real example: We've seen integrations where data shortcuts created incomplete stratigraphy reports that took months to detect and longer to fix—after the system was already live.

What Gets Lost

Poor data quality in oil and gas isn't just inconvenient—it's a business risk that compounds over time.

When geological data loses fidelity: Drilling decisions get made with incomplete information
When production data is oversimplified: Optimization opportunities get missed
When experienced professionals can't trust the system: They maintain shadow spreadsheets, defeating the purpose of integration

Hidden Complexity in "Simple" Data

Take depth measurements. They seem straightforward until you realize different measurement standards, reference points, and correction factors can make the same physical location appear at different depths.

When conversion processes flatten these distinctions into a single "depth" field, the data might be technically correct but operationally useless.

The problem: Legacy system complexity usually exists for reasons that aren't obvious to conversion teams. What looks like redundant data often captures critical business logic.

Getting It Right

Map critical data usage, not just structure. Understand how data gets used, who depends on it, and what processes would break without it.

Preserve fidelity where it matters. Not all fields are equal. Get input from actual users to identify which data is essential for operational, regulatory, and financial decisions.

Test with real scenarios. Don't just test whether data loads—test whether converted data can support actual business processes.

Document trade-offs clearly. Migration involves compromises. Make these decisions consciously and document them so future teams understand what was decided and why.

The Bottom Line

Data conversion isn't a technical step—it's where integration projects succeed or fail. Quick fixes might get systems live on time, but they create expensive technical debt that haunts you later.

The choice between shortcuts and proper conversion isn't really a choice. It's an investment decision that determines whether your integration creates lasting value or ongoing frustration.

At Lease Analytics, we've seen too many "successful" go-lives turn into data quality disasters. The difference between success and failure happens in the conversion planning, not the celebration.

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If you’re planning an integration and want a partner who brings both competence and candor, schedule a consultation with us.