The gold standard in qualitative data analysis, enhanced by AI for deeper sentiment and thematic discovery.
NVivo, developed by Lumivero, is the industry-leading qualitative data analysis software designed for researchers, evaluators, and market analysts. In 2026, NVivo maintains its market dominance through the integration of the Lumivero AI Assistant, which automates the initial phases of thematic coding and sentiment detection across massive unstructured datasets. Its technical architecture supports complex cross-tabulation of qualitative data with quantitative variables, facilitating high-rigor mixed-methods research. NVivo 15 (2026 version) focuses on the 'REFI-QDA' standard, ensuring seamless project interoperability between different QDA tools. The platform excels at ingesting diverse data types—including social media APIs, streaming video, and GIS data—and converting them into a structured environment for nodes, cases, and relationships. Its 2026 market position is defined by its transition from a pure desktop application to a hybrid cloud model, allowing for real-time collaboration via the NVivo Collaboration Cloud while maintaining the data sovereignty and security required by government, healthcare, and high-level academic institutions.
Uses NLP models to scan text and automatically generate nodes based on identified themes and sub-themes.
Verified feedback from the global deployment network.
Post queries, share implementation strategies, and help other users.
Maps relationships between cases using degree, closeness, and betweenness centrality metrics.
Algorithmic detection of positive, negative, and mixed sentiment at the sentence or paragraph level.
Full compatibility with the Qualitative Data Exchange Format for cross-platform project transfer.
Two-way sync between Citavi reference management and NVivo analysis nodes.
Advanced Boolean search and cross-tabulation of codes against case attributes.
Integrated 24-language AI transcription with speaker identification and timestamping.
Analyzing thousands of patient interviews to identify barriers to healthcare access.
Registry Updated:2/7/2026
Distilling feedback from video recordings of focus groups regarding new product UI.
Identifying patterns of intent or knowledge in massive email datasets (E-discovery).