- Visual analytics turn pattern discovery into a process that does not necessarily require programming, although automation often helps. These tools empower data scientists to explore massive data lakes of history and match up models that can be used in real time to analyze conditions.
- Analytic applications put simple point-and-click interface atop sophisticated math so non-data scientists can visualize the effects of, for example, clustering customers with a variable importance algorithm.
- Streaming analytics inject algorithms directly into streaming data as it flows into or across a company to continuously monitor live conditions like watching for patterns of fraud as transactions happen.
- Predictive analytics networks help data scientists crowdsource the best algorithms that, when checked in real time, can help reduce billions of events to the few that matter. The Comprehensive R Archive Network (CRAN) repository of more than 7,800 R packages helps crowdsource expert statistical and graphical techniques.
- Continuous streaming data marts can be used to monitor an algorithm’s behavior in real time, with feedback used to tweak their behavior.
- Machine learning helps accelerate the fitting of models and continuously retrains analytics to constantly refine parameters, allowing the analysis to always improve.
Source: The emerging Darwinian approach to analytics and augmented intelligence | TechCrunch