Sisense recently announced the release of Sisense Version 7.0, a major update to its flagship business intelligence solution. The product is highlighted by its visual drag-and-drop interface for doing data preparation in non-technical environments, allowing line-of-business users to find, add, and combine data from disparate sources. The news comes on the heels of the company’s Honorable Mention in the 2017 Gartner Peer Insights Customer Choice Awards for Business Intelligence and Analytics.
Sisense 7.0 features advanced design and visualization concepts that are commonly used in the creation of dashboards and analytics. Dubbed ‘dynamic data mapping’, the approach allows data analysts to interact with source data visually using computer-aided clustering to map and understand source data types, sizes, and connections. Dynamic data mapping also enables users to zoom in to a data source to get deeper understanding, or zoom out to consume a holistic view.
The solution now offers machine learning recommendations that help guide users through data preparation by recommending use of specific fields to mash up data sources. The unification of machine learning among solution providers in this space is a growing trend. Sisense believes that leveraging field suggestions presents a new step in making analytics accessible to wider swath of users.
In a statement to Solutions Review, the company’s CEO Amir Orad said: “Every BI company is talking about their ability to make simple data easy to analyze, but our goal with 7.0 is much bigger than that. We want to make all levels of analytics accessible to everyone, regardless of their technical skills or expertise. With the rise of machine learning and other innovative technologies, there’s no reason that BI shouldn’t be intuitive and easy for anyone to make sense of.”
Latest posts by Timothy King (see all)
- The 9 Best Embedded Analytics Tools for 2020 and Beyond - November 15, 2019
- Information Builders Unveils Cloud WebFOCUS BI and Analytics Platform - November 14, 2019
- The Top 30 Best Data Visualization Books on Our Reading List - November 12, 2019