Competitive advantages through Big Data Analytics
Big Data Analytics has a fundamental role in Industry 4.0, where it support the digital transformation of business processes through descriptive and predictive models based on machines and deep learning.
Big Data Management represents the strategic resource to obtain a competitive advantage in the global market, since it guides the business strategies by decision-making dashboards and multidimensional analysis, as well by predictive tools and intelligent automation.
Big Data Analytics enables the knowledge discovery from large volumes of data, providing the information necessary to support decisions, predict phenomena and automate operations, thereby increasing the competitiveness of organizations.
Big Data Analytics value
Big data analysis helps organizations to exploit their data and use it to identify new opportunities. This, in turn, allows the improvement of business strategies, to make operations more efficient, to increase profits and for customer satisfaction.
Data Analytics advantages
Cost reduction
IBig Data management through cloud-based big data technologies means significant cost advantages both in terms of storing large amounts of data and in the ability to support more efficient business models.
New products and services
Customer profiling through Big Data management allows the personalization of the offer, thereby increasing customer satisfaction.
Best decision support
Through distributed systems and in-memory analysis mechanisms, combined with the ability to analyze new data sources, companies can analyze information in real-time and make decisions based on data mining models.
Big Data Analytics and Artificial Intelligence
Revelis build multicloud solutions by using prescriptive and predictive analysis on Big Data, providing organizations with software platforms that allow to maximize the competitive advantage of Artificial Intelligence.
Simply and fast
The expertise of our AI Specialists and the AI tools used allows us to reduce the time for the development and start-up of products.
Data preparation
We use technologies for the data integration and cleaning from heterogeneous sources, storing big data on scalable and highly reliable storage systems
Model induction
We use descriptive and predictive machine learning techniques, combining state-of-the-art algorithms and proprietary techniques for predicting phenomena. We adopt deep learning approaches for the induction of neural networks
Model actuation
Machine learning and deep learning models can be automatically executed, allowing for interoperability with customer applications
Operations
We develop monitoring systems that allow organizations to quickly check the operating status of the solutions
