Omniscope Enterprise: Scalable Reporting and Self-Service Analytics

Modified on Tue, 16 Dec at 11:41 PM

Omniscope Evo Enterprise is designed to enable organisations to harness data at scale, empower end users, and simplify the delivery of insights, while maintaining full control, security, and customisation. Unlike other Omniscope plans, the Enterprise edition provides advanced self-service, publishing, scaling, viewer, AI, and support capabilities that improve how teams collaborate and make decisions.

Feature	Problem Solved	Value Proposition	Key Highlights
Project Templates	Analysts spend time building data transformation models or reports from scratch; non-technical users depend on their output	Templates serve as a self-service tool that removes the need for building data models or reports. Users can simply drag and drop data files and, within seconds, generate either a cleaned/transformed dataset or an interactive report.	- Templates act as a shared, reusable models combining data transformation and reporting, as standardised asset that the team can build, share, and improve - Templates can output interactive reports or data files - the end users just drop their input data for instant result Demos on Visokio sandbox: - Data Quality Inspector - Instant Dashboard
Multitenant Reporting	Different stakeholders need access to different slices of the same dataset, requiring multiple report versions	Personalise reports automatically for each user while maintaining a single report source	- Single URL delivers personalised content - Managers/ administrators see full dataset; viewers see only their data
Batch Publishing	Manual report distribution is time-consuming and error-prone	Automates report distribution at scale with multiple formats and user-specific content	- Data-driven batch publishing to data files, images, PDFs, and emails
Clustered Multi-Node Installation	Single-instance deployments can become bottlenecks under heavy usage or large datasets	Ensures high availability and horizontal scalability	- Docker-based clustered deployment - Multiple instances as nodes for high availability - Horizontal scaling with load-balancing
Pluggable External SQL Data Engine	Built-in engines may not handle extremely large datasets or high concurrency	Leverage external engines for unlimited data size, concurrency, and redundancy	- Integrates engines like Avalanche, Redshift, or Snowflake - Powers queries for reports/dashboards - Improves scalability, performance, and redundancy
Saved Shared Explorations	Users want to explore reports and share insights, without altering the published report	Empower report viewers to save, share and resume their exploration	- Preserve filters, selections, variables, and tab states - Return to a custom subset anytime or share direct links - Pause and pick up where they left off
Local LLMs	Data privacy regulations prevent cloud-hosted AI use	Use AI securely on-premises with full data privacy	- Supports self-hosted models like DeepSeek-R1 or Qwen via llama.cpp - All AI features work with local models - Ideal for regulated or on-prem deployments - Supports OpenAI-compatible models including Azure OpenAI
Cloud AI via OpenRouter / Azure OpenAI	Teams want to test/mix advanced AI models without building multi-model infrastructure	Flexible, unified access to a wide range of leading AI models through a single configuration, making it easy to switch models, control costs, and experiment with different capabilities	- Use single OpenRouter API key - Models include Grok, Anthropic, Google Gemini, Mistral, and open-source cloud models - Demo: AI-powered Ninja visualising any dataset
Priority Support	Organisations need fast resolution for critical issues	Up to four hours response time for urgent issues	- Priority assistance from Visokio support