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Verix is a business intelligence software company headquartered in Los Altos, California. Founded in 2007, Verix focuses on providing business intelligence analysis software applicable to all industries as well as industry-specific solutions for the verticals of life science and pharmaceuticals and CPG.
Verix’s business intelligence software runs either as Software as a Service or on-site on the customer’s servers. The software integrates with existing business intelligence programs and company data warehouses. Verix solutions are used by sales, marketing and business development departments in identifying areas of the business that need improvement through business intelligence analytics.
Verix developed a proprietary technology for ETL and data analysis based on the statistical, mathematical, machine learning, and bioinformatics fields. Large amounts of data from both internal and external data sources are analyzed, and algorithms applied to identify outliers, statistically significant anomalies, in the intersections between data sets. Verix performs automatic segmentation of the detected exceptions, known as Intelligent Business Alerts (Hotspots), then analyzes the business drivers attributed to the phenomena. Verix users are alerted to the Hotspots and probable causes, and can use the software to investigate further and make business decisions.
The data storage and access of Verix is Oracle-based, and the application server uses JBoss and J2EE. The analysis server is Java-based.
Verix clients include P&G, Bayer, Roche and DSI.
"Intelligent Business Alerts (IBA), also known as "HotSpots", are terms coined by Verix to describe statistically significant anomalies in business data. HotSpots are revealed through analysis algorithms of business data using the Patent Pending Verix IBA technology. HotSpots reveal changes in business performance metrics and trend breaks. They provide points of focus in Verix’s BI analysis as they contain particular potential for increasing business success or preventing business harm.
Hotspots are identified by analyzing very large sets of external and internal sources of business data to discover statistically significant anomalies in the trend patterns, taking into account historical data and performance along with up-to-date data. The anomalies are analyzed to determine common characteristics and condense multiple exceptional points into noteworthy, business-relevant alerts. Analysis is then performed to identify potential causes for the exceptions. Alerts are categorized according to their application to the business, and delivered to users as actionable, categorized alerts. Hotspots facilitate a style of business management termed Management by Exception (MBE).
Hotspots are a purely data-driven alert system, without need for external input in the form of queries and rules to identify relevant alerts. The system is scalable and can manage data sets ranging from small to huge. The analysis algorithms were developed based on the Statistical, Mathematical, Machine Learning, and Bioinformatics fields.
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