Complex business needs require thoughtful translation into sustainable data solutions. This post outlines how our team approached an institutional use case by designing and implementing a secure, layered data platform aligned with long-term goals. For a closer look at how we addressed the security and infrastructure aspects of the same project, see our companion piece by Rick Ross, “Layer by Layer: Turning Priorities into Scalable Platforms.”
The focus: how we transformed a business use case into a modular architecture using Snowflake, S3, and Informatica Cloud.
Every project starts with understanding what the institution truly needs.
In this case, the objective was to enable data-driven insights for new student intake processes that span multiple systems, have inconsistent workflows, and vary in data definitions. We were tasked with unifying this fragmented landscape into a single, integrated view that could support operational reporting, student readiness monitoring, and future predictive analytics.
Our first step was discovery. We conducted working sessions with stakeholders from Admissions, the Registrar’s Office, Institutional Research, and the IT department. These sessions clarified how new student intake processes and milestones were defined, tracked, and reported across systems. We captured user expectations, documented known data quality challenges, and identified both strategic questions and operational constraints.
To balance long-term strategy with near-term deliverables, we applied a dual approach:
Our response: a medallion-based architecture (Bronze → Silver → Gold) engineered to support data quality, transformation traceability, and output flexibility. Bronze served as the immutable landing zone aligned with source structures. Silver transformed and validated subject-area data into conformed, query-ready models. Gold prepared analytics-ready outputs with clearly defined dimensional patterns and business logic.
For this project, we built the architecture as a modular framework that could evolve alongside the institution’s. This structure was not only technically efficient, but it also directly aligned with our client’s planning cycles, governance expectations, and long-term analytics roadmap.
The Bronze layer serves as the foundation of the data platform, designed to capture raw data exactly as it arrives from source systems, without altering its structure. Think of it as the digital equivalent of an archive room: everything is logged, preserved, and organized for easy retrieval, but nothing is changed just yet.
To support this, we built secure, repeatable ingestion pipelines that collected data from a range of systems, including admissions, student portals, and learning platforms. These files were stored in Amazon S3 in a structured and predictable way, making it easy to track where data came from and how it was processed.
We used the Parquet format for storage, which strikes a balance between file size and performance. Parquet supports fast and efficient querying while minimizing storage costs, which is crucial when working with large volumes of institutional data. These files were made accessible through Snowflake External Tables, which allowed users to query the raw data directly without moving it, simplifying access while maintaining control.
To enhance the value and traceability of the data, we enriched every file with metadata. Each file included key details such as its source system, batch ID, load timestamp, and processing status. This helped us support full lineage and auditability, critical for compliance and data validation.
We also incorporated incremental load patterns, ensuring that new data could be processed without reloading entire datasets. This design choice reduced processing time and supported more timely updates while minimizing strain on source systems.
By setting up the Bronze layer this way, we provided the institution with a governed, scalable entry point for all downstream data processes, ready for transformation, validation, and analysis.
Once raw data landed in the Bronze layer, the next step was to make it meaningful, consistent, and ready for institutional use. That’s where the Silver layer came in.
We designed this layer as an Operational Data Store (ODS), a structured space in Snowflake where data from different systems is brought together and made consistent. Here, data was no longer just stored; it was transformed into something institutions could rely on to answer real questions.
At this stage, data quality rules played a key role. Each dataset was checked and cleaned for accuracy, completeness, and formatting. Whether it came from a student portal or an administrative system, the data had to meet shared standards to be useful across departments.
One of the most important efforts was the integration and alignment of records across systems. We applied business logic to connect records referring to the same student or course, even if those systems used different identifiers or formats. We also standardized keys, creating shared reference points that allowed different datasets to “talk to each other” in a consistent way.
To manage these transformations, we used Informatica Cloud (IDMC), creating modular pipelines that could be reused and updated over time. We enriched every record with lineage fields, allowing us to trace data back to its source, and added temporal tracking so users could distinguish between current and historical states.
The result was a trusted foundation of integrated, clean data, organized by domains such as student, application, and course, that supported reporting, planning, and decision-making. The Silver layer ensured that data across systems was not only collected but also aligned, enriched, and ready to use.
If the Silver layer is where data becomes structured and trustworthy, the Gold layer is where data becomes strategic and actionable. This zone was designed to transform detailed records into curated outputs that directly support institutional decision-making.
In contrast to the Silver layer, which preserves granular, transactional data, the Gold layer focuses on business meaning and context. Here, we translated raw events and records into key performance indicators (KPIs), aggregated metrics, and reporting-friendly tables.
We applied dimensional modeling techniques, particularly the star schema, which organizes data into fact and dimension tables. This design makes it easier to slice and explore metrics using business intelligence tools.
The Gold layer also introduced semantic models, which simplify complex data structures into terms and definitions familiar to end users. Instead of navigating table names and joins, analysts can explore pre-defined views built specifically for their roles.
Additionally, we designed support for various snapshot types, including daily, weekly, or term-based captures of data, to facilitate performance tracking over time.
By planning for the Gold layer with care and intention, we ensured that when the time comes to activate it, the institution will be able to move beyond operational reporting and into true analytics that are insightful, timely, and directly tied to strategic priorities.
Governance was not treated as an afterthought; it was part of the design from the very beginning.
As the data platform grew in complexity, we recognized the need to make it understandable, trustworthy, and usable by a broad set of users. To achieve that, we embedded governance practices directly into our development workflows, ensuring that every dataset, pipeline, and model was not only functional but also transparent and easy to maintain.
We applied consistent naming conventions, metadata tagging, and parameterization across all data assets. This made it easier to track, reuse, and update components over time, supporting both scalability and audit-readiness.
To enhance transparency, we implemented data lineage tracking, allowing users to see exactly where data originated, how it moved, and what transformations occurred along the way. This visibility is essential for change management, troubleshooting, and regulatory compliance.
We also emphasized the use of business glossaries and data classifications to bridge the gap between technical teams and business users. By defining shared terminology, we can reduce ambiguity and help ensure that everyone is speaking the same language.
Data quality scores were introduced to give users confidence in the information they accessed. These scores, visible alongside datasets, reflected validations such as completeness, consistency, and timeliness, allowing users to assess the reliability of the data before drawing conclusions.
Perhaps most importantly, we encouraged collaboration across teams by enabling shared comments and notes on assets. Analysts, stewards, and engineers could leave context, ask questions, or flag concerns directly within the platform, helping knowledge move with the data and reducing reliance on side conversations or email threads.
Together, these governance features created a foundation of trust and shared understanding. By making data transparent, traceable, and easy to navigate, we not only improved the technical quality of the platform but also made it more approachable and useful for the people who depend on it every day.
A key goal was enabling the client to own and evolve the platform.
We collaborated with the client throughout the build, providing documentation, walkthroughs, and embedded logic designed for ease of transfer. Our enablement strategy emphasized understanding not only what was built, but why and how it aligned with their data goals.
Each process flow, data pipeline, and model was delivered with future extensibility in mind. As a result, the teams are well-positioned to scale this architecture across new domains without having to start from scratch.
The strength of any data ecosystem lies in its foundation. This project demonstrates how designing architecture with purpose, rather than just meeting immediate needs, can unlock lasting value by anticipating future growth.
By investing early in scalable ingestion frameworks, modular transformation logic, and built-in governance, we created a structure that is not only functional today but also adaptable for tomorrow. This level of foresight reduces technical debt, accelerates onboarding, and minimizes rework as institutional priorities evolve.
Our success was grounded in true partnership with our client, aligning every decision with their operational goals, governance standards, and long-term vision. Together, we didn’t just build pipelines and data models; we built a durable framework designed to support confident, data-informed decision-making across the institution.
As institutions continue to modernize their data strategies, this approach offers a practical path forward, where well-architected systems become an engine for resilience, agility, and institutional insight.
Bianca Firtin is a Lead Data & Analytics Consultant at CTI Data.
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