DataX vs. Custom ETL: The Real Math on Build vs. Buy

Every institution eventually has this conversation. Someone on the IT team, usually someone good, points out that a handful of Python scripts and a scheduled job could pull data out of Banner and Canvas well enough. And they're not wrong. It can be done. The question worth asking before that project gets a green light isn't whether your team is capable of building it. It's what building it actually costs, in dollars and in the things that quietly go undone while someone builds it.

We work with institutions on both sides of this decision every year, so we wanted to lay the math out plainly, without pretending the in-house option is a strawman.

What building it yourself actually requires

A custom ETL pipeline pulling from SIS, LMS, CRM, and ERP systems isn't a weekend project, even for a strong team. At minimum, it needs someone who can write and maintain the extraction and transformation logic, someone who understands the data model well enough to keep definitions consistent across systems, and someone available when a vendor changes an API and breaks the pipeline at 6 a.m. on a Monday.

The market rate for that skill set isn't cheap. National data on data engineer compensation puts the median base salary somewhere around $125,000 to $135,000 a year, with mid-level engineers commonly landing in the $119,000 to $150,000 range depending on location and experience. That's one salary, for one person, assuming nothing goes wrong and nobody leaves. Most institutions building this in-house end up needing at least a fraction of two or three roles across data engineering, integration, and ongoing maintenance, even if those roles are shared with other responsibilities.

Then there's time. We've written before about how the typical custom data integration project in higher ed can take up to two years to get from kickoff to something usable, and that estimate holds up in our own experience working alongside institutions that tried the in-house route first. Two years is a long time to wait for a compliance report you needed last semester.

The Meharry example

Meharry Medical College's IT team is lean, by design. When Meharry retired its Banner system as part of a move to Workday, the team faced a real, unglamorous question: what happens to years of financial, student, and compliance data that needs to be retained for Title IV purposes once the system it lives in goes dark?

Building a custom archiving and retention pipeline in-house wasn't a real option. The team was already stretched thin standing up Workday, and the budget for that project was already spoken for. Hiring or reallocating staff to build a parallel data pipeline, on top of an ERP migration already underway, would have meant delaying one project to save money on another.

Meharry used Scaffold DataX instead, retaining and operationalizing the Banner data without keeping the legacy system on life support just to preserve it. The team then expanded into ingesting Workday and Blackboard data as the value became clear. None of that required adding headcount.

That's the real comparison. It's not DataX versus a hypothetical perfect internal team with unlimited time. It's DataX versus what a real institution, with a real budget and a small IT staff, could actually pull off in the time available.

Where building in-house genuinely makes sense

To be fair to the build side of this: if your institution only needs to connect two systems that rarely change, if you already have a data engineer on staff with slack in their schedule, if you're only pulling a handful of data fields rather than a full institutional dataset, or if the use case is narrow enough that a single well-documented script can handle it, building it yourself can be the right call. Not every institution needs a full data warehouse platform, and we'd rather tell you that plainly than oversell a solution you don't need yet.

The calculation changes once you're talking about three or more source systems, larger volumes of data or more complex datasets, once compliance reporting is on the line, or once the person who built the original pipeline is the only one who understands it. That's when the maintenance burden and the single-point-of-failure risk start to outweigh whatever you saved on licensing.

The math, side by side

Building in-house typically means:

• Hiring or reallocating skilled data engineering staff, at a market cost of roughly $125,000+ per role annually
• A build timeline that can run up to two years for a multi-system integration
• Ongoing maintenance falling to whoever built it, with real risk if that person leaves
• Custom documentation (or none at all) that new staff have to reverse-engineer later

A managed platform like Scaffold DataX typically means:

• Implementation measured in months, not years, without adding headcount
• A neutral, documented data model that new staff can pick up without archaeology
• Maintenance and API changes handled by the platform, not by whoever's on call
• A predictable, budgeted cost instead of a variable one tied to salaries and turnover

Neither list is trying to be neutral for its own sake. But the comparison holds up whether you run it with our numbers or your own institution's actual salary bands and project history.

The real question to ask

Before your team commits to building this in-house, it's worth asking three things directly: What does it actually cost us in salary and time to build and maintain this ourselves? What happens to that pipeline if the person who built it takes another job? And what are we not doing, as an institution, while two engineers spend the next year building plumbing instead of doing the analysis that plumbing is supposed to enable?

For a lot of institutions, especially ones with lean IT teams and real compliance deadlines, the honest answer changes the conversation pretty quickly.

Curious what the math looks like for your institution specifically? Schedule a demo, and we'll walk through it with your actual systems, timeline, and budget, not a hypothetical one.