This blip is not on the current edition of the Radar. If it was on one of the last few editions it is likely that it is still relevant. If the blip is older it might no longer be relevant and our assessment might be different today. Unfortunately, we simply don't have the bandwidth to continuously review blips from previous editions of the RadarUnderstand more
AdoptWe feel strongly that the industry should be adopting these items. We use them when appropriate on our projects.
Machine learning, semantic analysis, text mining, quantitative analytics, and other advanced analytics techniques have steadily matured over the past 15 years. They offer incredible potential for prediction, forecasting, identifying repeatable patterns, and providing insight into unstructured data. Historically, our ability to store and rapidly analyze large amounts of audio, video and image data has been severely limited. This placed constraints on sample size, as well as the time it would take to validate analytical models and put them into production. Now, using a spectrum of new technologies like NoSQL, data harvesters, MapReduce frameworks, and clusters of shared-nothing commodity servers, we have the power necessary to make truly effective use of these techniques. Combined with the massive increase in global data available from sensors, mobile devices and social media and we see this as a field with tremendous opportunity.