In a previous post I discussed how the Internet of Things (IoT) will radically change your big data strategy. Massive amounts of data from sensors, wearable devices, and other technologies are creating new and exciting opportunities to make better business decisions in real time. However, harvesting all of this data is only half of the equation. Making the data actionable is where real value lies. Traditionally, companies have mined data to look for trends and opportunities.

I am happy to announce that Prelert and Elastic are joining forces. Ever since we started Elastic, our goal has been to allow users to easily find relevant data or insights within large amounts of data. Search is a wonderful way to do it, and the ability to slice, dice, and aggregate the data in an unconstrained way allowed users to feel they are in control of the data, compared to the other way around. But we can take it a step forward, and with Prelert, we just did. Prelert has developed an unsupervised machine learning engine that can plow through large amounts of data and automatically find those insights our users today have been proactively finding using search.

Hi folks, this meetup will be held on Thursday 19th May at Sainsbury's HQ at 33 Holborn, London EC1N 2HT. If you are interested in speaking at an upcoming event, please contact me on Twitter @YannCluchey. In this talk we'll describe some of the data characteristics which make anomaly detection for real world problems challenging and describe some of the techniques we use at Prelert for anomaly detection. As the complexity of IT systems and the quantity of data people gather increases, proactively managing the health and security of these systems requires increasingly sophisticated monitoring tools. Rule based approaches are either becoming unmanageable or in need of augmentation, and the complexity and scale of the data pose significant challenges.