1.0 Fundamental set or types of Analytics – which are core to our business and our analytical applications2.0 Customers use a combination of analytical techniques – for example data mining and text mining.3.0 On the front-end Data Management is important because end users spend lot of time and effort in preparing data for analytics.4.0 On the downstream-end, sharing of analytical insights through easy-to-use visualization/BI tools is important. 5.0 Integrated set of components
Use all the entire suite together? Adding our highly optimized advanced analytics to the process can help you generate answers to questions you never thought you could ask. (Note: at this point you should tie back to the “so what if you could” story you started before.Our suite allows an organization to move to “now you can”!With solid Information Management principals in place, each gear can independently make the decision circle move…but they can and should all be put together too.
1977 TukeyExploratory Data Analysis1996 ShneidernamInformation Visualization Seeking Mantra1996 Fayyad KDD Pipeline 2009 KeimVisual Analytics Mantra1977 Tukey - Exploratory Data Analysis “practitioners are encouraged to visually examine their data with the assistance of summarizations and descriptive statistics “Shneidernam -Information Visualization Seeking Mantra Overview first, zoom and filter, then details on demand 1996 Fayyad - Knowledge Discovery in Databases (KDD) pipeline Selection Preprocessing Transformation Data mining Interpretation 2005 Cook defines Visual Analytics - “Visual Analytics is the science of analytical reasoning supported by interactive visual interfaces.” 2009 Keim – Visual Analytics Mantra