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Success Story with a Leading Higher Education Organization


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Overview and the Importance of Data

A prestigious institution, founded in the 1800s encompasses multiple degree-granting campuses, numerous schools, research programs, and global sites. It stands as a significant contributor to the local economy and education sector as the largest employer in the area. In recent years, data has emerged as a vital asset, playing a central role in the institution’s decision-making processes across various academic and administrative units.

Data assumes heightened importance, especially in enrollment procedures, where its role is crucial for ensuring student success. The significance of data has been further underscored during activities related to the COVID pandemic when precise information was essential for making well-informed decisions promptly. However, the existing legacy architecture posed multiple challenges for efficient data management at the institution.

Top 5 Legacy Pain Points

Here are the top most challenges they were facing:

1. Technical Debt

They were running on legacy systems built 12-15 years ago. It became a challenge to manage both the projects and operation work at the same time. They kept building on other systems without going back to look at sustainability in the future.

Previously, they had people seating through the night with batch processes which were failing. The decision to move to newer platforms and concepts eased up efforts from the data team and shifted their focus to management of processes that they are now able to sustain.

2. Multiple Data Warehouses

This is also related to technical debt. Every time they built a data warehouse, they didn’t discard the old one which led to duplicative processes and information collision. This resulted in data silos and made it difficult to get a holistic view of the organization’s data. Creating different kinds of versions of data which also leads us to the next pain point, no SVOT.

3. No Single Version of Truth (SVOT) for Data Sources

Disparate data sources that are not integrated will result in inconsistencies and inaccuracies in data. This in turn will make it challenging to make informed business decisions. There always has to be a plan or method on what is considered universal, otherwise data is perceived without common understanding.

They wanted to spend some time defining and contextualizing the data so they initiated their data governance platforms. They began to improve and expand on standards and processes. They also started to work on the business glossaries so that the data definitions are understood by everyone.

4. Convoluted Processes

Accessing data was a convoluted process. Their ETL processes were unmanageable and prone to failures. Since they were majorly batch dependent, they wanted to get out of a batch mode of operations.

5. Rigid Design

The technical processes were too rigid to be handled in an Agile fashion. It was difficult to adapt to changes quickly. The goal was to change the whole foundation of the design with Data Vault, to make it more flexible and manageable, even for future changes.

Eon Collective's Approach in Addressing Legacy Architecture Challenges

We stepped in to help them overcome these challenges by implementing Data Vault 2.0 methodology.

Some of their data team had attended training sessions and conferences on this approach. This involved investing in a platform for real-time data ingestion using change-data-capture (CDC) processes while utilizing serverless computing and API platforms for injection procedures.

1. Implementing Data Vault Concepts: Business Vault & Raw Vault

They built an S3 data lake and implemented the Data Vault concepts of business vault, raw vault, hubs, and links. The business vault stores enriched data that has been processed for easier consumption by end-users. In contrast, the raw vault contains unprocessed data from various sources in its original format. Hubs and links are used to establish relationships between different datasets within the Data Vault.

2. Adopting Automation Technology

To reduce time and resources spent on managing their new architecture, they adopted automation technology for a metadata-driven approach. This involved investing in an automation engine that allowed them to create reusable templates and standards for data-related processes while ensuring transparency through a robust data governance platform.

3. Data Marketplace: Shopping Experience & Auditability

They also developed a data marketplace that provided users with a shopping experience when searching for relevant datasets while maintaining auditability of all transactions within the system. This innovative approach helped streamline access to information across their various departments.

Business Value from Leveraging Data Vault Methodology

The implementation of EON Collective’s Data Vault 2.0 methodology brought significant benefits to their IT infrastructure and overall decision-making process across different departments. 

  • Agility

The new architecture provided agility and flexibility in managing vast amounts of information while reducing technical debt associated with legacy systems.

Faster and more flexible data ingestion processes allowed them to adapt quickly to changes without impacting their entire model significantly. 

  • Improved Transparency

Furthermore, improved transparency through robust governance platforms enabled better collaboration between IT teams responsible for managing infrastructure as well as business units relying on accurate information for decision-making purposes.

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Satya Kunta, Chief Data Architect NYU

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