In our previous post, Mission Alignment: From Reporting to Empowerment, we explored how aligning your institution’s mission with its data strategy can transform static operational reporting into deeper intelligence and insight that empowers timely, trusted decisions. Now we turn our focus to the essential foundation that must support that mission-driven strategy. That foundation must contain elements of data governance, architecture, and data quality. Without these in place, even the best-intentioned reporting, dashboards or analytics will struggle to deliver accurate and sustainable impact. 

Why the Foundation Matters 

Across higher education, institutions face an increasingly complex data landscape—multiple siloed systems, evolving student needs, shifting funding models, and a growing demand for timely, actionable insights. To meet these challenges, institutions must develop a comprehensive and unified approach to managing institutional data, resulting in a single, authoritative source of truth that enables consistent, accessible, and strategic decision-making. Think of this source of truth as the bedrock foundation of a skyscraper. You wouldn’t build a towering structure on shifting sand and expect it to stand; the same is true for data-driven decision making in higher education. Without a stable, reliable base, every report, dashboard, and strategic initiative built on top becomes vulnerable to cracks, inconsistencies, and risk. 

 Such a foundation must include:  

• Governance that gives structure – defining clear ownership, establishing standards, and creating accountability that guides institutional data management. 
• Architecture that enables flow – connecting systems, facilitating efficient data movement, and ensuring information is accessible when and where it’s needed. 
• Quality that builds trust – delivering insights that are accurate, consistent, and meaningful, and creating stakeholder confidence in the data-driven decision-making process. 

Together, these three pillars transform raw data into actionable information, empowering institutions to make informed decisions, respond to change, and support students, faculty, and staff with clarity and confidence. 

Let’s dig a little deeper into each of these. 

Governance: Ownership, Standards & Stewardship  

Data Governance is often the silent hero of data strategies—but it’s also one of the most important. It is of concern to any individual or group who has an interest in how data is created, collected, processed and manipulated, stored, made available for use, or retired. Effective data governance establishes clear decision rights, accountability, and standards for data, so information is accurate, consistent, and trusted across systems. With strong governance in place, organizations can rely on a single, well-defined version of the truth for reporting and analytics, rather than spending time reconciling conflicting numbers. This reduces operational and compliance risk, improves data quality, and enables leaders to make confident, data-informed decisions. This perspective is echoed in widely used frameworks such as DAMA DMBOK and in guidance from EDUCAUSE, which both emphasize data governance as a foundation for effective, data-driven transformation. 

Key elements of effective data governance include: 

• Clear policies and frameworks: What data gets collected? Who uses it? Under what rules? 
• Roles & stewardship: It’s not just an IT or IR concern. Academic Affairs, Enrollment, Finance, Student Services – –all must have a stake. This shared stewardship builds shared accountability. 
• Metadata and clear definitions: Before you build dashboards, you must agree on what “retention”, “net revenue”, “enrolled student”, etc. mean. Otherwise, metrics and reports will become contested or misunderstood.  
• Culture, trust and communication: Your data governance efforts fail if users don’t believe in the rules. Transparent processes, open communication, and visible accountability matter. 

While the concept of governance can feel overwhelming, it doesn’t have to be. If you don’t yet have a governance model in place, start small. Begin with one high-priority mission metric, such as first-year retention or course completion rates, and ask a few key questions: Who owns it? What definitions support it? Who uses it? How is it reviewed? 

This simple exercise can help spark meaningful governance discussions, surface gaps in ownership and definitions, and form a practical foundation for a broader, sustainable governance framework.  For institutions that want help putting this into practice, external partners can also provide facilitation and advisory support. 

Architecture: Connecting Systems & Data Flows 

Even the strongest governance framework loses its impact when data remains isolated or inaccessible in the form you need. For colleges and universities that rely on data to guide enrollment, student success, or financial decisions, architecture is what determines whether information can move across systems, be stored in a usable way, and ultimately become actionable insight. In this context, “architecture” refers to the structure of systems, integrations, and platforms that allows data to flow from source applications into trust reports, dashboards, and analytics 

Many institutions are working to break down longstanding data silos by connecting systems and focusing on specific business challenges, such as academic engagement, a critical factor in student retention. When data from the SIS, LMS, and other systems comes together, engagement signals can be used to identify students who may need support earlier in the semester. Solutions like Evisions Accelerators are designed to support this work by providing reusable data models and dashboards that sit on top of an institution’s architecture. Those models are grounded in architectural principles that can be applied with any modern data platform. 

When evaluating the key aspects of your data architecture, consider the following: 

• System Integration: Assess whether your SIS, LMS, CRM, and FIN/HR systems currently integrate with one another. For what purposes do they exchange data, and where does that data ultimately reside? It will be essential to map system interactions, data flows, and the technologies enabling them. You can begin with a specific problem or data set you want to ensure it is normalized, well-defined, and governed. 
• Data Platform and Strategy: Determine where your data will live. Are you leveraging a data warehouse, data lake, or lakehouse architecture? Consider how each option supports long-term scalability, agility, security, and adaptability to change. 
• Lineage, Transparency, and Accessibility: Institutional leaders and analysts must be able to understand where a given metric originates and how it was transformed along the way. Without validated lineage, trust in data erodes quickly, and adoption of dashboards suffers. 
• Governance Aligned with Architecture: Your technical architecture should reflect and reinforce the governance model you intend to implement. As definitions evolve or policies change, data pipelines and processes must be able to adapt accordingly, so governance decisions are consistently reflected in the data people use every day. 

A well-designed architecture does not need to be perfect from the start, but it does need to be intentional. By clarifying how systems connect, where data will live, and how information will flow from source to decision maker, institutions can create an environment where governance, analytics, and tools such as Accelerators are all working in concert to support student success and institutional resilience. 

Data Quality & Trust: Ensuring the Foundation Holds 

The old adage “garbage in, garbage out” remains as true as ever in data management. When information is inaccurate, incomplete, or inconsistent, even the most sophisticated analytics will fail to deliver meaningful insights. Across campus, decisions about enrollment, resource allocation, and student support are only as strong as the data that informs them. In practice, data quality is what makes the difference between dashboards that are debated and dashboards that are used.  

Key elements include: 

• Accuracy and Completeness: Are all essential fields populated, valid, and reliable? Legacy systems frequently harbor hidden errors, outdated values, or structural inconsistencies. As part of the Extract, Transform, Load (ETL) process, data may need to be validated, corrected, or augmented to ensure integrity before it ever reaches analytical tools. 
• Consistent Definitions: When Finance, Enrollment, and Student Services each operate with different definitions or naming conventions, the data story naturally fragments. Establishing shared terminology, business rules, and usage standards ensures everyone is interpreting information the same way, promoting organizational alignment. 
• Provenance and Lineage: Users must be able to trace every metric back to its origin—its source system, transformation logic, and calculation methods. Higher education will not place confidence in a “black box.” A “glass box” approach—transparent, governed, and fully traceable—creates trust and supports responsible decision-making. 
• Monitoring and Remediation: Ongoing visibility into data quality issues—such as missing fields, duplicates, latency, and anomalies—drives accountability and continuous improvement. Regular dashboards and exception reports make these issues actionable.  
• Trust Through Transparency: Trust grows when users see that issues are identified, communicated, and resolved quickly. Transparent processes and proactive communication with stakeholders foster confidence, which is essential to driving broad adoption of data-informed practices across the institution. 

Treating data quality as core operational work rather than a one-time project changes how people engage with information. When campus leaders and staff can see where data comes from, how it is maintained, and what happens when problems arise, they spend less time questioning the numbers and more time using them to shape strategy, support students, and demonstrate impact. 

Why the Foundation Unlocks Mission-Aligned Analytics 

When governance, architecture, and data quality are firmly in place, your reporting and analytics become both credible and actionable. Users trust the numbers, leaders rely on them, and the institution gains a shared understanding of what the data truly represents. 

From there, mission alignment deepens. Common definitions replace conflicting interpretations, and leaders can confidently connect metrics back to strategic priorities. 

This groundwork also begins to shift your culture. Conversations move from “Which number is right?” to “What does this number tell us—and what should we do?” The institution becomes genuinely data-driven. In essence, the foundation transforms data from a passive record of the past into a strategic asset that empowers decision-making. 

This is the journey from simple reporting to true institutional empowerment. 

In our next post, we’ll build on this foundation and explore how your institution can modernize its technology through the cloud, SaaS, and interoperability.