To ensure continued growth and increased market share, Trility helped this client engineer a centralized data model that integrates with modern and legacy systems to create new customer-centric applications and experiences. The data lake also helps ensure future state solutions with enhanced security and the ability to scale with repeatable integration patterns.
A century-old manufacturing company has grown significantly due to increased sales and acquisitions. This growth identified critical challenges for the client to continue meeting growth goals. Data is not centralized and is inaccessible to new solutions and legacy ones. New mobile-first applications require customer data to increase user adoption. The client cannot enhance operational efficiencies and customer service workflows. An existing Azure Data Lake lacks standardization and governance. Due to its manual configuration, it prevents scalability and repeatable integrations with existing and future applications.
Using an integrated team approach, Trility is working alongside the client's team members to ensure knowledge transfer on future-state skills – including a security-by-design mindset and Everything as Code / DevOps implementation to increase scalability.
By helping architect and build a centralized operations model with a secure enterprise cloud framework, the client can increase data enablement across the enterprise for both business users and customers.
Other planning and implementation aspects include DataOps in the data lake, AI services and integrations, and Machine Learning Operations (MLOps).
The client seeks to achieve modernized, data- and AI-driven solutions that leverage a hybrid model for on-prem and Azure. Trility is helping architect and build a Centralized Operations Model using an Everything as Code approach, DataOps in the lake, AI services and integrations, and Machine Learning Operations (MLOps).
The solution also includes automation and control of Azure deployments, data flows, and policy; enables rapid scalability of the data environment across future and modernized applications; and provides a holistic enterprise data model.
The solutions also support the evaluation and implementation of the following AI use cases:
AI landing zones to manage AI services and model
Order tracking and forecasting
Optimize training and enterprise knowledge with centralized AI services
