Directory Data & AnalyticsBI & Data VisualizationPeriscope Data (Sisense)

Periscope Data (Sisense)

About Periscope Data (Sisense)

Periscope Data by Sisense is a comprehensive business intelligence and data analytics platform designed to empower data teams within large enterprises. It enables users to connect, analyze, and visualize complex data sets from multiple sources in a unified environment, facilitating data-driven decision-making across organizations. The platform supports SQL, Python, and R, allowing data scientists and analysts to build sophisticated models and custom visualizations.

Targeted primarily at enterprise-level organizations with mature data strategies, Periscope Data offers robust data integration, advanced analytics, and collaborative features that streamline workflows between data teams and business stakeholders. Its primary value lies in accelerating the analytics lifecycle, improving data accuracy, and enabling scalable insights that drive operational and strategic outcomes. The platform’s SaaS deployment ensures ease of access and scalability without the overhead of managing infrastructure.

How to evaluate BI & Data Visualization

This is how the CIOPages Research Team evaluates this category. It is not an assessment of Periscope Data (Sisense). It comes from our Business Intelligence & Analytics buyer guide.

25%

Semantic Layer & Governed Metrics

How metric definitions are authored, versioned, and certified; whether the model is tool-locked or interoperable (LookML, dbt/Cube via SQL or MCP); row- and column-level security inheritance; lineage; and prevention of conflicting definitions across content

20%

Self-Service & Analytical Depth

Authoring experience for analysts vs. business users, drag-and-drop vs. spreadsheet vs. search paradigms, calculation expressiveness (DAX, LookML, set/associative logic), depth of ad-hoc exploration, and how far a non-technical user gets without IT

15%

AI & Natural-Language Analytics

Quality of NL-to-query and agentic features (Copilot, Pulse, Spotter, Qlik Answers, Gemini, Joule), whether answers are grounded in the governed semantic layer, explainability and citation of results, hallucination guardrails, and forecasting/insight automation

15%

Data-Platform Fit & Performance

Live query-down vs. in-memory extract behavior on your warehouse (Snowflake, Databricks, BigQuery, Fabric/OneLake), concurrency at your real user count, caching/acceleration, freshness, and whether one governed copy of data is preserved

10%

Embedding & Extensibility

White-label embedding, multi-tenant isolation and per-tenant row-level security, developer SDKs and REST/GraphQL APIs, infrastructure-as-code for content, and headless/API access for products you ship to customers

10%

Governance, Security & Administration

SSO/SAML/SCIM, granular RBAC, certified-content workflows and usage analytics, deployment options (multi-cloud, sovereign, on-prem), audit logging, and the operational burden of capacity, version, and content sprawl management

Total Cost & Ecosystem Alignment

Licensing unit (per-user vs. capacity vs. consumption) and how it scales to thousands of viewers, bundling with an incumbent cloud, migration and retraining cost, and lock-in to a single vendor’s data stack

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