Leverage Big Data technologies and our expertise applied to your existing operational data to optimize your business and gain actionable insights to drive value in your business.
To overcome data processing and storage challenges and craft an effective data strategy, we work towards several goals: innovation, addressing the needs of users, risks, and regulations. Correct data strategy provides an in-depth market and competitor analysis view, enabling stable business growth.
Data Platform Design
The design of a data platform is not a single step and requires data lakehouse architecture, database modeling, and ETL (Extract, Transform, Load) data pipeline design. Standard programming is not a solution when handling large amounts of data due to the data size. Opinov8 team analyzes and considers all aspects before even a single diagram is drawn.
Data EcoSystem Guidance
To achieve the best solution and business goals in ecosystem development, an in-depth analysis of the business is required as the data procession solution options are endless, and data engineers may achieve the same task in multiple ways.
Data modeling is the process of correct identification of the data origins in the system. We document the architecture in the format of UML Entity Relation Diagrams or, if applicable, JSON representation. The diagrams serve as the main guideline for Opinov8 data engineers and scientists to operate the system correctly.
The BA formulates the core needs of the product and resolves any ambiguities to create a roadmap for the developers. Opinov8 Business Analysts work with strategic, functional, release roadmaps and create a unified strategy.
Build & Deploy
AI & ML
Opinov8 team has an experience in both integrations of cloud ML solutions and the building/training process of the neural networks. Our expertise is recognized by IEEE white papers for deep image compression and image super-resolution, authored by the Opinov8 engineering team.
As data volume, variety, and velocity has dramatically grown in recent years, architects and developers have adapted to “big data.” Data pipelines enable data to flow from an application to a data warehouse, from a data lake to an analytics database, or into a payment processing system.
MLOPS & Infrastructure
With Machine Learning Model Operationalization Management (MLOps), we want to provide an end-to-end machine learning development process to design, build and manage reproducible, testable, and evolvable ML-powered software. MLOps aims to unify the release cycle for machine learning and software application releases.
Business intelligence (BI) combines business analytics, data mining, data visualization, data infrastructure, and best practices to help companies make data-driven decisions. You know you’ve got modern business intelligence when you have a comprehensive view of your organization’s data and use it to drive change and quickly adapt to the market.
Data analytics is the process of analyzing raw data to draw out meaningful, actionable insights. It helps you make sense of the past and predict future trends and behaviors; rather than basing your decisions and strategies on guesswork, you’re making informed choices based on what the data tells you.
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