Redefining Education at Yale Through Digital Transformation‚Äč

6 Months

From conceptual data architecture to fully loaded and tested master data hub with 2 ETL developers.


Data Attributes


Base Tables and 8 Integration Views

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Redefining Education at Yale Through Digital Transformation

Yale Overview and the Role of Data

With a long-standing reputation for academic excellence and a rich historical legacy, this esteemed university has acknowledged the pivotal role of data in effective decision-making and operational efficiency. Specifically, data is harnessed to gain valuable insights into various aspects of the institution, including student demographics, academic performance, and emerging trends. This wealth of information serves as the bedrock for informed strategic planning and the continuous enhancement of academic programs.

Furthermore, the university’s forward-thinking approach extends to preparing students for a world increasingly reliant on data. To achieve this, data science and analytics are integrated into various academic programs, equipping students with the skills needed to thrive in a data-driven environment. The university’s dedication to data extends to its sustainability initiatives, employing data-driven strategies to monitor and mitigate the university’s environmental footprint which actively promotes sustainable practices throughout its campus. This multifaceted approach showcases the university’s commitment to harnessing the power of data for both educational and ecological advancement.

Top Challenges Faced

  • Chart of Accounts Alignment (Cost Account Hierarchy)

Aligning the chart of accounts presented challenges due to the diverse accounting practices and systems that existed. It required substantial collaboration and analysis to establish a standardized framework for financial transactions. Creful consideration of various cost account hierarchies were made to ensure accurate representation of the organization’s financial structure.

  • Supervisory Organization Alignment 

Achieving consistency in reporting structures was also a challenge as unique reporting relationships and departmental hierarchies needed to be navigated. It involved engagement with stakeholders to establish a cohesive and standardized reporting system across the organization, allowing for clearer communication and streamlined managerial processes.

  • Academic Structure Alignment

Harmonizing academic structures posed challenges due to variations in curriculum, programs, and the organization of academic departments across different colleges. Efforts were made to establish consistent terminology, program codes, and academic hierarchies while accommodating the unique characteristics and offerings of each college, resulting in a more streamlined and unified academic structure.

  • ABAC Security Integration 

Integrating Attribute-Based Access Control (ABAC) security required overcoming challenges presented by varying security policies, roles, and access levels. The project involved aligning access control policies with the overall security requirements of the organization while accommodating the specific needs of different colleges, ensuring data privacy, compliance, and secure access control across the organization’s systems.

Strategy and Approach

1. Assessment

Through our tool, ADEPT, we began with a comprehensive assessment of the current landscape, identified assets that needed migration, and conducted an impact analysis to ensure a successful and informed modernization process.

Their transition from on-premise and campus solutions to the cloud involved aligning Workday concepts. They also had to be agile in their approach during the migration process, as the project required moving over 3000 data attributes from these source systems. This needed a comprehensive understanding of the business perspective to ensure accurate data integration into the new cloud environment.

2. Utilize Automation Technology for Talend Jobs

By leveraging automation technology, we automated Talend jobs, enabling efficient and streamlined data processing. This automation eliminates manual intervention and accelerates data workflows.

3. Implement Automated Unit and Quality Tests

We built automated unit and quality tests as part of our strategy. These tests ensure the reliability and robustness of the data load process by detecting errors and verifying data integrity. Additionally, they facilitate graceful restart and recovery processes, minimizing disruptions and ensuring smooth data flow.

4. Incorporate Web Services for Seamless Integration

Through the integration of web services, we provided seamless communication and data integration capabilities. This facilitated efficient access to data services and enabled smooth interaction between different system components.

Transforming Data Into Profit

  • Robust Technology and Futuristic Roadmap

We assisted them in implementing a robust technology platform and developing a futuristic roadmap. This allowed them to stay ahead of the curve, ensuring they have a solid foundation to support their evolving business needs and leverage emerging technologies for innovation and efficiency.

  • Ability to Scale and Handle Changing Business Requirements

Our approach enabled them to adopt scalable solutions, facilitating their ability to accommodate changing business requirements. This scalability empowers them to expand operations, handle increased data volumes, adapt to market demands, and ensure a seamless and agile business environment. 

The solution is designed to handle both daily and intraday loads into People Hub, with the capability to handle Big Data capacity. It also facilitates web service access, data service integration, and utilizes a scalable data model, enabling easy addition of new tables and columns with minimal downstream impact.

  • Re-usable Components and Streamlined Development

By leveraging reusable components and platforms like HCM and RE, we streamlined their development processes, resulting in significant time and effort savings. The scalable data model and easy integration allowed for seamless addition of new tables and columns with minimal downstream impact, enhancing operational efficiency.

The global big data in education market size is expected to reach $68.5 billion by 2027, reflecting the growing importance of data analytics in higher education.


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Data-Driven Mastery in Finance

Stock Market


Assets Under Management

Data Integrity

For accurate and reliable financial data and reports.

Financial Data

A strategic asset for smarter investments.

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Data-Driven Mastery in Finance: A Leading Financial Company's Journey

Setting the Stage: An Introduction to Financial Evolution

A premier financial services provider, with trillions in assets, powers a cutting-edge investment management platform that enhances data management and reporting for a wide array of financial institutions. This platform testifies to the company’s commitment to innovation, furnishing users with real-time investment intelligence that sharpens strategic decision-making. Its adoption not only accelerates growth but also empowers financial entities to significantly refine their operational workflow, achieving remarkable agility in an ever-evolving marketplace.¬†

As companies seek for growth and operational efficiency in rapidly evolving markets, Eon Collective stands as the strategic partner of choice, providing the expertise and technology to navigate data management complexities and capitalize on the growing demand for data and technology services.

Clearing the Path: Confronting Hurdles

As market dynamics evolved and client demands grew more complex, the company saw an opportunity to further strengthen their technological infrastructure. The original architecture, which had proven to be robust and dependable, was in need of refinement to better align with the agility required to rapidly deploy new features and adapt to changing data needs. This is an indication of the platform’s success and the increased expectations that accompany market leadership. 

Any limitations could potentially slow down their response to market opportunities and client customization requests, hindering their ability to maintain a competitive edge. Clients, especially in the financial sector where speed and accuracy are paramount, might experience delays in service delivery, reduced functionality in the face of new financial regulations, or challenges in accessing the latest investment strategies and insights.

Recognizing these challenges, the company’s initiative for data management was not just a strategic move for internal efficiency, but also a step to uphold and enhance the client experience and ensure their service offerings continue to meet and exceed the high standards of a fast-paced industry.¬†

Collaborative Solutions

Our ongoing efforts are centered on boosting platform’s flexibility and responsiveness, aiming to expand its capabilities without compromising the reliability and performance trusted by clients. Our team provides end-to-end technical support for their asset management platform, enhancing user interfaces, streamlining middleware for efficient data flow, and fortifying backend processes to ensure secure, real-time data management and robust transaction handling.  Our solutions revolve around the following components:

1. Agile Integration and Service Optimization

We support an agile framework by combining the integration microservice with service optimization. This streamlined service layer enhances the platform’s capability to rapidly deploy new features and adapt to evolving data requirements.

Clients experience a more dynamic platform, capable of quicker updates and a tailored approach to data management, leading to faster innovation and a competitive edge in the market.

2. Proactive Monitoring and Data Flow Management

By supporting the integration, monitoring, configuration, and data orchestration on this platform, we help maintain a system that can continuously self-optimize for peak performance.

The result is a significant increase in system reliability and uptime, ensuring that their client operations are efficient and uninterrupted.

3. Data Integrity and Precision Reporting

The alignment of reference and ELT processing with automated data validation and reporting ensures the accuracy and reliability of financial data and reports.

This integration empowers clients with dependable insights for strategic decision-making and maintains the platform’s integrity, which is crucial for client trust and regulatory compliance.

The Impact: Sustaining Financial Excellence

Through our collaboration, we are continuously evolving key elements of their modern financial management platform. Our skilled teams collaborate on front end, middleware, and backend maintenance and improvements. This ongoing work simplifies complex data environments, enhancing transparency and fueling confident decisions for their clients. We implement resilient solutions to consolidate data access, smooth platform operations, and remove roadblocks to actionable insights.

Our agile approach allows us to adapt to emerging requirements and deliver rapid enhancements that position the platform as an enduring market leader. Together, we share a commitment to making financial data a strategic asset, empower smarter investments and power the next generation of financial services.

According to research by the World Economic Forum, the financial services industry could unlock over $1 trillion in additional revenue if firms work together to share data and analytics at the same level as leading data-driven sectors.


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Pioneering Data Innovations in Healthcare

Pioneering Data Innovations in Healthcare

Strategic Plan

Synchronize data management with the broader enterprise objectives.


Boosted operational flexibility, lowered TCO, and ensured data governance compliance.

Change Management

Comprehensive training and support with co-building opportunities, merging client & development teams.

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Setting the Stage: The Landscape of Healthcare Data

Faced with a rapidly evolving healthcare landscape, a prominent healthcare organization within the Pennsylvania tri-state area, recognized the need to modernize its data management capabilities.  This includes efforts to dramatically disrupt the status quo and enhance the ability of Primary Care Physicians and specialists to provide the highest level of service to patients. 

The mission of the company is not only to improve healthcare, but to make it more affordable for everyone. As such, they partner with physicians in the delivery of patient-centric, quality care that helps those receiving medical services to get and stay healthy.  As part of meeting the mission, the company is undergoing multi-dimensional, fast paced growth, and has engaged Eon Collective to revamp its operating model and data strategy.

Obstacles in Healthcare Data Management

In this data transformation journey, we explore some of the challenges they encountered, common in the dynamic and evolving healthcare industry:

1. Legacy Data Sources

Their existing data landscape had an array of data sources with varying structure and quality standards. It was an indicator of their long-standing history, presenting opportunities for standardization and quality enhancement for more consistent and reliable insights.

2. Manual Analysis

Initially, their data processes were manual, slow and error-prone. They recognized the potential for enhanced efficiency and accuracy through automation, paving way for faster and more scalable decision-making.

3. Historical Data Storage and Auditability

Secure storage and easy retrieval of historical data presented a challenge. They acknowledged the need for enhanced audit trails and security, to strengthen compliance and data integrity.

4. Embracing Modern Data Practices

In the face of rapid technological advancements, we encountered natural hesitations in adopting new methodologies. Understanding the importance of staying current, we focused on demystifying these new practices and fostering a culture of embracing change for continuous improvement.

5. Tooling Dilemma

Choosing suitable data tools from a saturated market was daunting, compounded by concerns over integration with established systems and processes.

The Strategic Path: Our Approach to Modernizing Healthcare Data

1. Strategic Development

  • Strategic Alignment – The project’s goal was to synchronize data management with the broader enterprise objectives, emphasizing service delivery and operational efficiency.¬†
  • Data Strategy Launch – We initiated the development of an Enterprise Data Strategy, focused on modernizing the operating model, creating a strategic data management framework and uninterrupted information flow to empower enterprise operations.
  • Data Modernization Initiative aimed at transitioning to advanced technology platforms, implementing robust data governance and quality controls, and minimizing manual processes to boost operational efficiency and cut costs.

2. Execution

  • Data Vault 2.0 Methodology¬†

We helped them adopt the Data Vault 2.0 methodology, a modern and agile approach to building efficient data repositories and support the evolving business needs of the company. This methodology is integral for enabling efficient operational support for multi-source Data Acquisition, Data Management, Business Intelligence, Analytics, and Data Science requirements. 

The agile and modern design principles of the Data Vault 2.0 framework ensure scalable, flexible, and consistent data systems that enhance operational flexibility and reduce total ownership costs (TCO), while also ensuring accountable and compliant data governance.

  • Cross-Functional Teamwork¬†

Our work serves as a blueprint by providing a framework to ensure consistent execution, strategic alignment, and collaboration across various business functions, platforms, and IT architecture‚ÄĒlaying the groundwork for a cohesive and future-ready technological ecosystem. Continuous monitoring and governance mechanisms have also been put in place to evolve with business and operational priorities.

  • Change Management

Acknowledging the magnitude of organizational change, we have provided extensive training, support, and hands-on co-building opportunities, integrating client resources with the development teams.  We have designed communications to build awareness of the business rationale for the revamped data strategy and to reinforce the vision.  Managing this inherent change is one of Eon’s core competencies and key to client readiness to become a data-driven work culture.

  • Partner Technologies

Over the years, we have forged strong relationships with our partner technologies which have proven invaluable in client engagements. This deep-rooted alliance enables us to seamlessly integrate these technologies in client environments, crafting a tailored solution to address each unique challenge. Here’s a glimpse into how each technology partner was used to address the distinct challenges faced:

  • Snowflake – Transitioning to Snowflake facilitated a cloud-based data management environment, significantly improving scalability and performance which was crucial to handle data from diverse legacy sources.
  • VaultSpeed was instrumental in supporting Data Vault methodology, enhancing data quality, governance, and trust among stakeholders.
  • Collibra simplified data management and governance, replacing manual analysis with structured data handling processes for consistency and compliance.
  • was key in integrating various tools and methodologies into a coherent framework, simplifying the tool selection dilemma and ensuring seamless operation.

The Transformation Beyond Modernization

1. Governance and Leadership Enhancements:

  • Establishment of a centralized, enterprise-wide Data Governance Office and an Executive Steering Committee for strategic oversight.
  • Implementation of data governance metrics and comprehensive business glossary to standardize and measure data management practices.
  • Increased role clarity and interdepartmental collaboration, supported by targeted resource funding to enhance governance effectiveness.

  1. Operational Efficiency and Quality Control:

  • Business Data Steward roles to ensure data is managed effectively and accurately. The adoption of clear data stewardship and quality remediation processes is expected to significantly reduce operational inefficiencies and data-related errors, directly enhancing service delivery and reliability.
  • Improved visibility into data quality remediation processes, coupled with well-defined proactive processes, to preemptively address potential data issues.
  • Regular communications and training from the Data Governance Office (DGO) designed to enable an informed, data centric workforce, ready to execute on the company’s strategy and drive business value.

  1. Risk Management and Scalability:

  • Enhanced data access controls to mitigate regulatory risks and ensure compliance with industry standards.
  • Development of scalable solutions for a multi-payer environment, catering to the complex needs of healthcare payers.
  • By funding departmental resources for data management and emphasizing scalable solutions, the company is positioning itself for sustainable growth and adaptability.  This scalability is critical in a complex, multi-payer environment, and ensures the company can respond quickly to market changes and patient needs.

The global big data healthcare market has seen significant growth, increasing from $20.31 billion in 2022 to $22.73 billion in 2023. This represents a compound annual growth rate (CAGR) of 11.9%


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Data-Driven Retail: Modernizing Commerce in a Digital Era



-32,000 Reports

Identified and removed unused reports, cutting data migration, transformation, and storage costs.


Reduction in costs and manpower

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Company Overview and the Importance of Data

An iconic global fast-food chain has etched an impressive footprint in the history of the fast-food industry. Recognized for offering a foreign-inspired cuisine with a unique American twist, the brand has expanded significantly over the years with thousands of locations globally and more than 350 franchises.

In the era of digital transformation, the company has leveraged data analytics to enhance customer experience and streamline operations. They use data to understand consumer behavior, preferences, and trends, which aid in menu development, promotional strategies, and operational efficiency. The use of data analytics underscores their commitment to innovation and adaptability, securing their position as a leader in the fast-food industry.

Navigating the Data Maze: Top Challenges They Faced

One of their major challenges was a centralized data management system that caused operational inefficiencies. This in turn impacted inventory management, supply chain operations, and overall performance. 

Limited data autonomy was also a challenge. Sharing data with the parent company restricted their ability to make independent data-driven decisions and leverage their data assets effectively for business insights and decision-making. It also limited localized decision-making which made it a challenge to analyze local customer behavior, preferences, and market trends. 

Due to operating centrally, there was also limited differentiation from their mother brand which hinders competitive advantage in local markets. A lack of agility and innovation with their operations prevented quick responses to market changes and experimentation with new ideas. Specific data insights give companies the ability to develop unique customer experiences, personalized marketing campaigns, or loyalty programs, as opposed to standardized strategies and offering. It enables distinct brand identities that give companies a competitive edge.

Turning Tides: How Eon Unlocked Their Potential

1. Assessment and Modernization Planning

We use ADEPT, our proprietary tool, to conduct a comprehensive assessment at the beginning of every modernization journey. It allows us to understand the existing setup, identify assets requiring migration, and perform an impact analysis for an informed transformation process. We are also able to determine the cost of modernization, create a modernization schedule year by year, and utilize automation within our toolset to significantly reduce costs.

2. Data Warehouses

  • eCommerce Data Warehouse

We developed a Greenfield Data Vault warehouse for the eCommerce Division, along with creating all Tableau reports to gain insights into customer behavior, product performance, and revenue trends.

  • Financial Data Warehouse

We transitioned their financial data warehouse into a Redshift environment, previously housed on legacy systems and a traditional data warehousing solution, facilitating better data management and financial reporting.

  • Technology Transition

We executed a shift to a cloud-based AWS platform, ensuring a smooth handoff of data between different divisions of the company and developing a new data warehouse using the data vault methodology. This transition also supported better data management across the organization through modernized ETL processes.

3. Supply Chain Management Systems Modernization

We migrated and updated the supply chain data into Redshift and AWS, and partnered with MicroStrategy to enhance supply chain decision-making capabilities.

4. ETL Transformation

We streamlined the ETL process which led to a remarkable reduction from 64,000 reports to less than 32,000. Through ADEPT, we got a detailed analysis of report usage, identifying over 30,000 reports that had not been utilized in years, thus preventing unnecessary data migration and reducing both transformation and data storage costs.

We also transitioned from a traditional ETL tool to Talend, through automation tools within our technology toolkit. We realized a 50% reduction in cost and manpower and expedited a process that typically would have taken twice the time and resources.

Talend, one of our technology partners,¬† enables the extraction of data from various sources, transforms it into a consistent format, and loads it into target systems or data warehouses. Talend’s graphical interface and extensive connectors make it easy to manage complex data integration workflows, addressing data integration challenges, ensuring data quality, and automating the data pipeline process.

5. Analytics and Reporting Optimization

ADEPT allowed us to identify data patterns within their technology environment and offer MicroStrategy cloud solutions to further optimize data management and reporting capabilities.

Transforming Data Into Profits

Implementing these data solutions brought significant benefits such as:

  • Improved Decision-Making

Access to consolidated and real-time data enabled them to make informed, data-driven decisions. This led to better menu optimization, pricing strategies, marketing campaigns, and operational planning, enhancing customer experience, loyalty, sales, and profitability.

  • Operational Efficiency and Cost Optimization

Improved data processes and ETL optimizations enhanced operational efficiency and reduced costs. The transition to modern data warehousing and cloud solutions, along with better financial data visibility, led to effective resource allocation, and sustained profitability. The use of automated transformation within our ADEPT TOOLKIT further cut costs and manpower by 50%,  causing a significant operational and financial improvement.

  • Targeted Marketing and Sales

Data solutions allow them to segment customers based on demographics, purchase history, and preferences. More targeted marketing campaigns and promotions result in higher conversion rates and increased sales.

  • Competitive Advantage

By using data to personalize customer experiences, optimize operations, and deliver targeted marketing campaigns, they gained a competitive edge. This resulted in increased market share, customer loyalty, and brand recognition.

  • Scalability and Growth

The strategic transition to a cloud-based AWS platform and a Redshift environment for data warehousing provided enhanced scalability to meet the company’s expanding data needs, supporting effective data management across multiple locations and facilitating business growth.

The QSR/retail industry is making substantial strides in advanced analytics and Big Data adoption to maintain competitiveness in the face of growing e-commerce and customer loyalty challenges. A survey by NASSCOM indicates that 70% of companies in this sector are focusing on revenue growth through investments in AI and related technologies. 


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Manufacturing Excellence through Digital Integration


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Significant Mergers and Acqusitions


Frameworks to establish blueprints that allow standardization and automation. 

Data Pipelines

Efficiently designed and implemented data pipelines for transferring historical data from one MPP system to Snowflake.

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Manufacturing Excellence Through Digital Integration: A Success Story

Building the Future: Redefining Industries through Innovation

In the following case study, we will explore our journey with a leading global corporation recognized for its commitment to innovation and quality across diverse industries, including manufacturing, consumer goods, energy, security, and healthcare.

They recognize the immense value of data in driving their business operations, enhancing product development, optimizing supply chain management, and creating personalized customer experiences.

Using the power of data, they have reshaped their data operations and practices, leading to accelerated growth and a culture of continuous innovation.

Challenges: Breaking Barriers

With a long history of growth through mergers and acquisitions (M&As), they struggled with diverse systems, processes, and standards across the company. This fragmentation led to increased costs from inconsistencies and inefficiencies. 

Their existing data infrastructure – an outdated combination of legacy data warehouses and data lakes – presented serious limitations. Maintaining this system drained IT resources while performance lagged behind modern solutions. 

Slow response times and inefficient data retrieval prevented a satisfactory experience for business users. With data difficult to access and leverage, user productivity suffered along with decision-making capabilities. 

They recognized the need for a strategic overhaul on their data foundation to achieve standardization, boost IT agility, and empower users with timely insights. Only then could they transition from a decentralized conglomerate to an integrated enterprise. Having guided dozens of organizations through M&As, we understand these data pitfalls intimately. Our team has extensive experience with assessments, strategically planning and implementing the technical integration work required to unify data and systems.

Eon's Approach: Innovate, Automate, Elevate

1. Assessment

Using our internal toolkit, ADEPT, we began with a comprehensive assessment of their current landscape, identified assets that needed migration, and conducted an impact analysis to ensure a successful and informed modernization process.

2. Frameworks

We utilized key frameworks to establish blueprints within the organization that allow standardization and automation. Here are some of the frameworks that we used for this modernization process:

  • Ingestion Framework

Our team updated the ingestion framework and templates to serve as a blueprint for modernization efforts, facilitating the smooth movement of data and accelerating the adoption of modern technologies. The template allowed for the creation of a robust staging area within a Snowflake database. Snowflake, one of our technology partners, is a cloud-based data platform known for its scalability, performance, and ease of use.

  • Generic Data Processing Framework

To address the challenge of data processing, we employed a generic data processing framework to allow users to consume and build additional data assets from ingested data.

  • Data Mart Framework

To optimize data organization and presentation, we established a data mart framework, enabling the creation of easily consumable data marts for specific business functions or user groups. We also implemented data operations practices, utilizing metadata to automate data operations, including lineage, quality checks, and cataloging.

3. Data Migration

We handled data migration through the design and implementation of efficient data pipelines. This ensured seamless and reliable transfer of their historical data from one massively parallel processing (MPP) system to Snowflake. The ingestion framework we used above also handled historical data. 

The Aftermath: Driving Results, Empowering Industries

  1. Improved Performance and Cost Optimization

    • By modernizing their data infrastructure and migrating to Snowflake, they experienced enhanced performance and cost optimization capabilities.
    • This move allowed them to have faster query speeds and improved data access, leading to more efficient data processing, transformation, and analytics. The adoption of Snowflake also facilitated faster decision-making processes.
  2. Standardized Frameworks and Processes:

    • We were able to establish consistent frameworks, processes, and practices on their data platform. This standardization improves efficiency, reduces errors, and enables better collaboration.
  3. Efficient and Scalable Data Operations:

    • Adopting dbt provided a standardized framework and repeatable processes for managing data transformations and analytics pipelines.
    • Data operations practices enabled better management of the data lifecycle, increasing efficiency, scalability and reliability.


According to Statista in 2021, It is estimated that by 2025, there will be over 75 billion Internet of Things (IoT) devices worldwide, many of which will be used in manufacturing to collect and analyze data for process optimization.


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Healthcare Reinvented: A Journey in Data Analytics Transformation

Data Solutions in Healthcare


Reduction in data platform costs including license, storage and computing

+750 ETL Jobs

Successfully repointed to Talend in production, ensuring minimal operational disruption.

3 Months

Migrated customer-specific ETL to Talend Cloud, streamlining onboarding and reducing complexity.

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A New Era in Healthcare Analytics

This transformation journey is for a healthcare CRM, marketing automation and data analytics company that helps healthcare providers optimize their marketing strategies and improve patient engagement. They leverage data to drive insights and targeted marketing campaigns. 

The integration and analysis from a variety of data and various sources such as marketing data captured in CRM systems, electronic health records, claims, and socio economic attributes, etc., enables healthcare organizations to gain a comprehensive understanding of their patient population, opportunities for patient engagement, and provider network insights to manage referral leakages.

Challenges: Complexity of Legacy Systems

Here are some of the challenges they faced, prompting the need for a platform modernization initiative. 

The legacy system’s high cost of maintenance and updates, coupled with scalability limitations, hindered their growth potential. Additionally, the system’s performance resulted in slow data processing, making it difficult to deliver timely insights to customers.

Another was the lack of real-time data ingestion and serving capabilities. The legacy system struggled to handle real-time data streams efficiently, preventing the company from providing real-time analytics and updates through its API layer. Inconsistent data quality practices also posed a challenge due to unreliable analytics.

The company also grappled with a lack of central data governance and relied on customized data models and ETL processes for individual customers. This decentralized approach led to data silos, inefficiencies, and increased complexity when onboarding new customers.

Eon’s Approach: Clouds of Innovation

1. Assessment

Employing our methodology and tool, ADEPT, we embarked on the process with a comprehensive assessment of their data environment, which informs our migration and modernization strategy.

2. Cloud Data Platform Migration to Snowflake

We conducted a migration project, transitioning from their existing onsite database to the cloud-based Snowflake data platform hosted on AWS. We managed to migrate over 100 client production and non-production databases to Snowflake. We also repointed multiple internal and customer-facing applications to Snowflake, ensuring uninterrupted operations. 

The project also involved migrating their previous data management, ingestion, and analytical applications to Snowflake, resulting in a more streamlined and efficient data ecosystem. A significant achievement was the establishment of real-time HL7/FHIR data feeds from customers using Redox, Snowpipe, and S3, allowing for the ingestion and processing of data in JSON models. 

3. Implementing Talend Cloud

To address the challenges related to legacy ETL and data ingestion processes, the company adopted Talend Cloud, a cloud-based data integration platform. 

We successfully repointed more than 750 ETL jobs to Talend in production, ensuring a smooth transition and minimizing disruption to operations. Migrating all customer-specific ETL code to Talend Cloud within three months further streamlined the onboarding process for new customers and reduced complexity.

Business Impact: Thriving in a Transformed Landscape

The implementation of modern cloud-based platforms brought about significant improvements in various areas:

  • Cost Optimization and Risk Reduction

The migration to Snowflake and Talend Cloud resulted in substantial cost savings, reducing license costs by 60% year over year. This cost optimization allowed them to allocate resources more efficiently. The modernized platform reduced data management risks by addressing challenges such as scalability limitations, inconsistent data quality practices, and lack of central data governance. This in turn improved data reliability, security, compliance, and facilitated better data management practices.

  • Real-Time API Integration

The modernization efforts enabled the company to ingest and process data in real-time. The establishment of real-time data feeds and APIs allowed for timely insights and updates, meeting customer demands and supporting their need for up-to-date information. Real-time capabilities improved customer satisfaction, decision-making, and the company’s competitive edge. Security measures were also implemented to ensure secure data sharing with customers.

  • Enhanced Data Quality Controls

The implementation of standardized data quality practices across the modernized platform improved the reliability and accuracy of analytics results. This included validation rules, data profiling, and data cleansing techniques. By addressing data quality challenges and establishing solid controls, the company inspired customer confidence and the overall value derived from the analytics platform.

Addressing the challenges of inconsistent data quality practices and establishing robust data quality controls enhanced the trustworthiness of its analytics solutions. This in turn boosted customer confidence, facilitated better-informed decision-making, and increased the overall value derived from the analytics platform.

According to IBM, medical knowledge is said to double every 73 days. The rapid growth of medical data presents both opportunities and challenges for healthcare professionals.


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NYU Case Study in Cutting-Edge Data Solutions


Efficiency gains that improved institutional performance

Data Vault 2.0

Enhanced agility and flexibility in handling large data volumes with the new architecture, while minimizing legacy system technical debt.

Data Marketplace

A shopping experience when searching for relevant datasets while maintaining auditability of all transactions within the system. 

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Data Vault 2.0 at NYU: A Higher Ed Case Study in Cutting-Edge Data Solutions

NYU Overview and the Importance of Data

New York University (NYU) is a prestigious institution founded in 1831, boasting three degree-granting campuses, 18 schools, 25 research programs, and 11 global sites. As the largest employer in New York City, NYU plays a significant role in the city’s economy and education sector. In recent years, data has become an essential product and asset at NYU as it plays a pivotal role in decision-making processes for schools and business units.

Data is particularly crucial during enrollment processes to ensure student success. The importance of data has been further highlighted during COVID-related activities when accurate information was needed to make informed decisions quickly. However, legacy architecture at NYU presented several challenges that hindered efficient data management.

Top 5 Legacy Pain Points

According to their Chief Architect, 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 that 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‚Äôt 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 that made it difficult to adapt to changes quickly

The technical processes were too rigid to be handled in an Agile fashion. 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 Role in Addressing Legacy Architecture Challenges

Eon Collective stepped in to help NYU overcome these challenges by implementing Data Vault 2.0 methodology.

Some of NYU’s 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

NYU 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, NYU 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

NYU 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 various departments at NYU.

Business Value from Leveraging Data Vault Methodology

The implementation of Eon Collective’s Data Vault 2.0 methodology brought significant benefits to NYU’s IT infrastructure and overall decision-making process across different departments. 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 NYU to adapt quickly to changes without impacting their entire model significantly. 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.

Higher education institutions generate vast amounts of data. In fact, it is estimated that a single large university can produce more data than the Library of Congress.


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