Financial Services

Stock Market

Unlocking Value Through Data; Transforming Data Management at Leading Financial Company

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efficiency gains which improved institutional performance.

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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.


Data Solutions in Healthcare

Innovating Healthcare through Data Transformations




efficiency gains which improved institutional performance.

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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—laying 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 technologie 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.
Our Technology Partners

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%


Retailing in a Digital Age: A Data-Driven Transformation



Assessment Toolkit

Tech Stack


Reduction in Cost 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. 


Customer Success Story with Leading Manufacturing Company



Tech Stack


Reduction in Supply Chain Costs

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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.

Higher Ed

New York University

EON Collective Data Vault 2.0 Implementation Services - Customer Success Story with NYU



efficiency gains which improved institutional performance.

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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’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 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.