From Data Standards to Data Strategy: Why MortarCAPS is Necessary But Just the Start

“Our systems don’t talk to each other.” We’ve been hearing this sentence at conferences for years.

At Re-University, the hallway talk was “Our systems still don’t talk to each other.”

At best, this statement is a networking conversation starter providing cringe-worthy stories bond over. At worst, it’s a sign of how the challenges of data, privacy, technical debt, and fear of change keep higher ed institutions reporting and data management strategies stuck in the past. This stagnancy has downstream impacts that become more profound over time as strategic decision-making, collaboration, and future planning information sources decline in meaning, accessibility, and currency.

By default, student information systems, learning management systems, CRM platforms, finance tools, and reporting environments coexist. They typically aren’t built with integration in mind.

And when they do integrate, the connections are minimal, fragile, only partially automated, or dependent on time-consuming manual interventions.

This is why initiatives like the MortarCAPS Higher Learning Data Standard are so important for the future of higher ed.

A data standard is foundational to progress. It’s not the same thing as data integration, data warehousing, or a lakehouse architecture. But it makes achieving those things easier.

Why a Data Standard Matters

If Banner, Colleague, PeopleSoft, Workday or any other system align to a shared data model, integration becomes cleaner. APIs become more predictable. Reporting definitions become more consistent. Cross-institution collaboration becomes possible.

In some Canadian provinces and American states, community colleges and universities already do a version of data standardization work preparing data submissions to their governing bodies, which enable government-driven research and reporting, and sometimes drive funding allocations. Imagine what sector association collaborations could do if institutions not only had their data standardized internally, but with each other: share process documentation, share BI frameworks, tools, and scripts (with data stripped), or how we could collaborate and advocate better by building our own body of evidence for sector advocacy to influence policy.

A data standard that is collaboratively developed between institutions is an important piece of enabling student-focused, institution-led collaborative responses to the significant regulatory changes the sector has experienced in recent years (funding reviews, international student and immigration policy change, layoffs and campus closures, DEI practices reversals, etc.). All-purpose-built datasets are inherently (though not always intentionally) biased: what is not collected cannot be measured and cannot be reported on.

A data standard that is collaboratively developed between institutions is an important piece of enabling student-focused, institution-led collaborative responses to the significant regulatory changes the sector has experienced in recent years (funding reviews, international student and immigration policy change, layoffs and campus closures, DEI practices reversals, etc.). All-purpose-built datasets are inherently (though not always intentionally) biased: what is not collected cannot be measured and cannot be reported on. When data standards and data sets are developed, controlled or managed by governments with little or no institutional involvement, they end up biased towards to governing parties agenda. As the agendas of governing bodies change, biases change, and what is prioritized for collection and reporting changes.

But this is also true whether government is involved or not – external influences like shifts in student perspectives and expectations of higher ed learning, or unexpected but widespread events like the COVID-19 pandemic change educational culture, also shift reporting priorities and therefore data sets.While data standards like MortarCAPS provide stability and reduce potential government bias, they also enable institution-led sector dataset development for research and faster sector-wide responses to developments as institutions often can identify cultural changes that will impact student success before governments recognize it. With inter-institutional data standards, collaborative evidence gathering, developing and actioning a strategic multi-pronged, multi-institutional advocacy plan becomes possible.

In theory, a collective data standard reduces one-off integrations, endless reconciliation exercises, and reinventing what should be standard and common definitions of "student", "completion", or "retention" mean.

But if you talk to registrars or institutional research teams, you’ll hear a healthy mix of optimism and skepticism.

They love the idea. They also know the reality is more complicated.

Rational Skepticism

In my experience, when institutions hear “data standard” they fall into one of two camps:

1. Hope: “Finally, this will solve our integration problems.”
2. Skepticism: “This won’t account for our customizations; it’ll just create more work.”

Both reactions are understandable. Registrars in particular understand that half the battle is change management. Systems have been heavily customized over years or decades, and when software providers launch major upgrades that break or erase customizations*, IT and data reporting teams scramble to find a replacement for the lost workflows and to fix broken or replace missing reports. Data stewards, data entry clerks, and governance leaders also know that processes matter as much as schemas. Governance determines success more than data architecture alone does.

A data standard does not automatically refactor your student information system. It doesn’t automatically modernize your pipelines, nor automatically create an AI-ready data environment. This still takes labour (even with the help of AI).

But what a data standard does accomplish is something more subtle: it creates a shared starting point.

Where Integration, Warehousing, and Lakehouses Fit

So, let's separate these layers.

Data Integration

• Action: Builds API's, pipelines, event streams and sync processes.
• Answers: How does this data move and how is it transformed?
• Data Standards Role: Reduces friction points across systems.
• Efficiency gains with a a shared data standard: Building integrations is faster. Improves knowledge sharing, security control and visibility.

Data Warehouse / Lakehouse

• Action: Provides scalable storage, analytical modelling, version control, historical tracking and an AI-ready infrastructure.
• Answers: How do we analyze and activate this data?
• Data Standards Role: Aligns systems to a consistent model. Creates shared understanding and source of truth for common reports, metric definitions, and data uses.
• Efficiency gains with a shared data standard: Fewer transformations, less reconciliation tasks, improves data literacy and enables collaborative conversations (scenario modelling, predictive analytics, automated reporting)

None of the efficiencies happen because of the data standard alone.

A data standard makes modern architecture viable. It reduces the obvious barriers and helps eliminate the merry-go-round conversations of where should we start, and whose system gets priority. The starting place is a foundational standard for data across all systems. Then, any decisions or changes made to improve the data architecture will improve it for every area.

We've Been Working with Standards for Years

MortarCAPS is our first formal participation in a collaboratively defined, sector-led data standard. But it’s not our first experience with standards.

In fact, much of Plaid’s work over the years has revolved around data standards, just in different forms. We’ve worked with various government and system-level standards, including:

• California Community College’s Chancellor’s Office MIS reporting and attendance accounting
• British Columbia’s provincial education and post-secondary data warehouse submissions
• Statistics Canada’s Post-Secondary Student Information Systems (PSIS)

These are standards, too, and are often used to support compliance and funding. The MortarCAPS data standard is significantly different. It’s sector-led, collaborative, and designed for interoperability, not just reporting. That's why MortarCAPS matters.

MortarCAPS aims for operational interoperability and innovation enablement. To me, that’s ambitious.

Students pursue education because they want to change their trajectory. Faculty invest years building knowledge communities. Researchers push into unanswered questions. IT teams experiment and refine systems that make complex institutions run. Analysts model scenarios. Report writers iterate until a visualization communicates clearly. Strategic planners try to see around corners.

In different ways, each role is ambitious; in higher ed we work together towards something better.

A collective data standard like MortarCAPS reflects that same instinct. It assumes institutions can coordinate, clarify, and align - and that doing so matters.

The Real Opportunity

If institutions adopt MortarCAPS (or similar standards), and pair that with thoughtful integrations, a modern lakehouse, clear governance, automated pipelines, and AI-ready modelling layers, then a shift happens that has domino effects for every ambitious role in the institution.

One of the early dominos occurs for analysts. Instead of spending half their time reconciling spreadsheets, they can focus on:

• Scenario modelling (planning analysts)
• Capacity planning (operational analysts)
• Financial sustainability analysis (finance analysts)
• Student success optimization (SEM analysts)

The dominos that follow include:

• Simplified processes and clear policies
• Discussions remain on-topic and constructive (no arguing over conflicting reports)
• Leadership across areas are on the same page
• Faster, evidence-based decision making

This results in institutions and their people spending more time on transformation:

• Revised and new program offerings and modalities where needed
• Student service and support changes tailored to current student body and culture
• Budget and resource allocations are future-focused, not reactive
• Communication and marketing that is targeted to fill enrolment gaps

These all start with data transformation. The collective result is overall institutional transformation.

What Plaid Adds to the Conversation

At Plaid, we do more than advocate for standards. If you’ve been following us for some time, you already know we help institutions translate standards into working systems.

We:

• Map legacy SIS customizations to shared models
• Design integration strategies that minimize disruption
• Build automated pipelines into warehouse and lakehouses
• Align standardized data to enrolment, retention, and funding analytics
• Ensure definitions used for government reporting align with operational decision-making

Final Thoughts

A data standard is not a data warehouse. It is not an integration layer. It’s not strategic analytics and reporting program.

But without a shared data standard, all three become more difficult.

MortarCAPS represents a meaningful step forward for the sector.

The institutions that benefit most will be those who treat it not as a compliance exercise, but a catalyst for architectural modernization. And that’s the conversation we’re excited to be a part of.

Modern Data Governance is Live

Webinar: April 2nd 10:00am (PT)

This post is the first in a series on modern data governance that lead up to a free webinar April 2nd at 10am Pacific. If you're a data governance committee member, data steward, Registrar, IT leaders, Dean, or manager interested in taking a modern approach to data governance, this is for you.

We'll cover how static data dictionaries and handbooks decline in accuracy over time, and how live metadata increases accuracy over time and makes your institution action-ready. We'll talk about efficiency improvements, lineage mapping, the importance of visualizing data integration and transformations between systems, and how a modern approach to data governance is key to scaling up your information technology and institutional research infrastructure. We'll show practical examples of how metadata management is used by IR, IT, HR, and/or Registration offices. Andrew will also take questions from the attendees at the end of the webinar.

All registrants will receive a free Active Data Governance Self-Assessment tool and an Identity Lifecycle Mapping worksheet. By working through the assessment and worksheet (alongside reading this series as we lead up to the webinar), you'll not only have an understanding of where on the scale of static-to-live governance your institution is, but what gaps exist in a process map and identify priorities to take back to your governance or leadership team. Note down any questions that come up for the webinar's live Q&A.

The webinar will be recorded if you are unable to attend in person.