Customer Success Story with Leading Manufacturing Company
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Their existing data infrastructure – an outdated combination of legacy data warehouses and data lakes – presented serious limitations.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
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.
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:
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
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.
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.
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.