Omniscope
All-in-one data analytics platform for trusted insights and self-service dashboards
Acerca de Omniscope
Omniscope is a complete data analytics solution that enables users to build, run, and maintain analytics from raw data to final decisions. It supports self-service branded analytics apps with clear governance and expert support, deployable on-premises or in the cloud for data privacy. The platform integrates natural-language AI insights and extensible workflows for data integration, transformation, and visualization.
Preguntas frecuentes
Visokio offers Ninja Support, which provides educational, diagnostic, and performance assessment consultancy and support via email, video (screen sharing), and phone calls. The service is delivered by an expert team to address emergencies and complex installations.
Yes, Omniscope Training offers tailored training sessions delivered online. These sessions focus on specific use cases, scenarios, and data challenges relevant to your organization. No previous knowledge is required, and the training upskills business users or analysts to utilize Omniscope’s data toolbox.
Yes, Visokio's development team can help you add bespoke reusable data connectors, analytics, and preparation blocks created in R and Python. They can also build new functionality for custom data transformations, statistical analysis, or connecting to any API or data source.
Visokio experts can help transform the way your business handles information and communication. They will show you how to manage your data and produce instant insights for all departments, allowing real-time collaboration and access to accurate data for informed decisions.
Yes, you can start a free 30-day Omniscope trial. The download should begin automatically, and you can skip the registration if you only need the non-commercial Free edition.
Tailored workshops focus on reviewing your data sources, processes, and existing reporting strategy. They help you leverage Omniscope and best practices to automate data management, minimize time and effort, and set data quality validation mechanisms.
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