Hourly consulting and data infrastructure don’t mix. A managed service model keeps upstream data clean over time, because its pricing rewards the provider when the data stays clean.
Data integration work in upstream oil and gas has often been priced the way legal work is priced: by the hour. A consultant comes in, cleans something up, bills for their time, and leaves. Then the data drifts back.
This isn’t a failure of effort. It’s a failure of model.
Why Hourly Consulting Doesn’t Work for Data
Upstream data is not a one-time problem. Production volumes update daily. Land acquisitions add new assets. SCADA historians accumulate new tags. The ERP gets updated. Every change is an opportunity for data quality to degrade.
When you hire a consultant at an hourly rate, you’re paying for a point-in-time fix. The consultant’s incentive is to complete the engagement and move on. There’s no structural incentive to build something that stays clean. In fact, a system that requires ongoing manual intervention generates more billable hours.
The result is a familiar pattern: a cleanup sprint before a transaction or an audit, followed by a slow drift back to fragmentation.
What a Managed Service Changes
A fixed-fee managed service can align the provider’s incentives with the operator’s outcome, but only if the contract writes the outcome down. Ask for service levels on failed loads and data checks, with fee credits when they are missed. With those terms, the provider wins when the data stays clean, not when it needs to be cleaned again. Without them, a flat fee only rewards doing less work.
That changes the architecture decisions. Instead of designing a system a consultant can come back and work on, you design a system that monitors itself, flags anomalies at ingest, and maintains quality continuously.
It also changes the pricing conversation. Instead of an open-ended engagement with unpredictable costs, the operator has a predictable monthly cost for data infrastructure. It’s a line item, not a variable. Check what the fee excludes, such as new sources and out-of-scope requests, since those are billed separately.
The Right Comparison
The right comparison for a managed data engineering service isn’t an hourly consultant. It’s a fractional VP of Data Engineering: someone who owns the infrastructure, maintains it, and is accountable for its quality over time. If you’re weighing that against hiring or hourly help, the break-even math for outsourcing data engineering shows where each model pays off.
At InFocus Data, that’s how we price and deliver: flat rate, continuous management, one line item.
The data asset stays clean between transactions, not just immediately after one.