Read this like a CEO:
If your immediate answer to fragile data pipelines is “let’s hire more engineers”; stop! That reflex masks the real problem: production operations designed as fragile engineering projects rather than repeatable products. Below is why the old choices fail, what a modern data foundry actually delivers, and the specific playbook we use with customers to change the math on cost, risk, and velocity.
The problem In One Sentence
When executives stop counting licenses, and start counting people and recurring operational labor, the economics change.
For decades, teams had two bad options: build brittle, costly homegrown systems, or buy heavy platforms that still leave engineers stuck in production ops. The real cost is operational; the people, the manual toil, the tribal knowledge; not just license lines on a spreadsheet.
We built BettrData to change that math: move capacity from people to product so unit costs fall as volume grows.
Why “build vs buy” gets the wrong focus
Licenses and features are visible; operational toil is not. But the latter is what compounds: night-time re-runs, one-off scripts, non-repeatable runbooks, spreadsheets of exceptions, and audits that expose no lineage. Buying a heavy product often reduces some burden, but not the labor required to maintain production. Building in-house is cheap at first, but fragile at scale.
A better framing: ask who will run the pipelines, day-in and day-out; people or product? If people, your unit economics will always be tied to headcount.
The promise: three clear outcomes
A modern data foundry should deliver three simple, measurable results:
Fewer people in production ops. In typical engagements, organizations move from 7–10 engineers running production ops to 1-2 non-technical operators overseeing the same workloads – the operational model that changes the cost base.
Falling unit economics. When operations are productized, unit costs decline: software and compute scale far more cheaply than people, turning million-dollar run rates into low-hundreds-of-thousands run rates.
Production-grade reliability & compliance. The goal isn’t “it worked yesterday”, it’s “it works every day and is auditable.”
The three pillars of a modern data foundry
1. Productized operations (control plane). Operational functions like SLAs, retries, lineage, schema evolution, belong in a control plane, not in one-off scripts. ETL is for transformations; the control plane handles operations.
2. Role design & people economics. Senior engineers should be inventing, not babysitting production. Design a staffing model where junior ops analysts and non-technical operators deliver throughput using low-code templates and governance.
3. Automation + selective AI. Use automation and AI where it reduces repetitive work like field mapping, anomaly detection and runbook generation, to speed time-to-value and reduce human toil.
Six signs you have a systemic ops problem
If two or more of these are true, you have a systemic problem, not a hiring problem: large ops teams still firefighting; ETL treated as the full control plane; half-built homegrown frameworks; long backlogs; data quality breaking business outcomes; engineers doing routine onboarding. For the full, CEO-friendly checklist with measurements and immediate fixes, see our companion piece: “6 Warning Signs Your Data Operations Are Costing You Money.”
The economics, in plain numbers
The debate isn’t “which tool”, it’s “who runs the pipeline.” Our benchmarking shows productized operations lower annual run-rates dramatically while improving throughput and compliance. If you want the math, we’ll walk your leadership team through a comparison, just the numbers.
Final word
If your immediate reflex to an operational failure is “let’s hire,” you’re solving the wrong problem. Treat data ops like a product: productize, automate, and redesign roles. If two or more of the checklist signs apply to your organization, act now. Read the checklist for concrete measurements and a prioritized roadmap.
