Build Vs. Buy for Faculty Activity Systems: A CIO’s Decision Framework
CIOs are often asked to build a homegrown faculty activity system after years of data living in spreadsheets, shared drives, or learning management shells never intended for institutional records. The request seems simple: keep what works, fix what doesn’t. In practice, it raises concerns about:
Security: exposing information to people who shouldn’t have access.
IT time and opportunity cost: tying up resources for years on a non-strategic rebuild.
Knowledge loss: the risk when the one developer who understands the code leaves.
Complexity: departments differ dramatically, from medicine to performing arts.
This framework helps CIOs weigh build versus buy through the lenses of security, total cost of ownership (TCO), and long-term resilience.
Security first: from ad hoc access to governed evidence
When reporting runs through ad hoc tools—Learning Management System shells, shared drives, email attachments—access controls and audit trails are brittle. Files move, permissions linger, and confidential narratives for promotion and tenure can be exposed to unauthorized users.
A modern environment consolidates faculty data into a governed system with:
Role-based access and permission inheritance.
Audit-ready exports with predictable refresh cycles.
Central logs for activity and access.
Build reality: your team must continuously harden authentication, retest permissions, and maintain logs.
Buy reality: you evaluate a vendor’s control model, map it to institutional policy, and co-manage governance.
In both cases, the CIO owns the risk—the question is where the expertise and labor reside.
The hidden time tax—and why your CEO cares
A CEO’s directive is clear: don’t tie up IT building a system that isn’t strategic. A full campus software build can easily extend 24–36 months once requirements, integrations, and documentation are included.
That timeline carries a real opportunity cost as security backlogs grow, innovation projects stall, and one or two developers become single points of failure.
Buying doesn’t erase effort, but it shares it. You reduce bespoke maintenance and the brittleness that appears when institutional knowledge sits with one person or team.
TCO with real complexity, not a feature checklist
A credible Total Cost of Ownership model extends beyond licenses and salaries. Over a three-to-five-year horizon, model four buckets:
Build and sustain: engineering time, QA, security reviews, documentation, on-call coverage.
Integrations: SIS, HR/ERP, LMS, course evaluations, and research systems; plus maintenance as vendors update.
Operations and governance: access management, change control, audit readiness, policy alignment.
Adoption and change management: training, communications, and rework when college-level needs diverge.
A buy decision doesn’t eliminate these costs; it makes them predictable and shared.
One system, many departments: honoring differentiation
Campus complexity is real, and a system must support differentiation across colleges without reverting to unit-level deployments that recreate silos.
Implementation should be collaborative: CIOs should sit with academic leaders to identify what truly matters for promotion, tenure, and annual reviews, map common fields and export formats that fit governance expectations, and configure flexibility within a single institutional deployment rather than allowing separate systems to multiply.
The goal is alignment, one governed system that respects diversity without fragmenting it.
Academic Analytics faculty evaluation environment relies on three connected sources of data:
Academic Analytics data — externally validated indicators and peer context.
Institutional data sources — SIS, HR/ERP, and course evaluations.
Faculty-specific data — narratives, artifacts, and updates that only faculty can provide.
A build path must be designed for ingestion and reconciliation across these streams.
A buy path should make it straightforward to merge them with:
Consistent identity resolution
Predictable refresh cadence
Exportable, auditable evidence
Unified data prevents disputes and builds trust in the review process.
The knowledge gap when key developers leave
Homegrown systems often rely on a single engineer. When that person leaves, upgrades stall, bugs linger, and institutional memory evaporates. Buying doesn’t remove turnover risk, but it distributes it; documentation, release notes, and roadmap continuity become a shared vendor responsibility.
A pragmatic recommendation for CIOs:
Start with governance goals: security, audit readiness, retention.
Quantify the time tax of a 24–36-month build and socialize it with the CEO.
Insist on one system that allows differentiation without fragmentation.
Design around the three data streams so leadership and faculty use the same validated evidence.
Choose partners who map policies and exports collaboratively—implementation quality defines true TCO.
Leadership takeaway
CIOs don’t need another overly individualized customized system to maintain; they need governed, interoperable evidence that reduces operational risk and respects campus diversity. Whether you build or buy, anchor the decision in clear security goals, a complete TCO model, and unified data across Academic Analytics, institutional sources, and faculty contributions. When those principles guide the choice, decisions move faster and IT capacity stays focused on innovation.
