Sumo Logic, an operational & machine data intelligence company, have announced the next evolution of their product. Their platform will now include a new component, Transaction Analytics, which provides users with insights into deep casual relationships across incredibly complex IT architectures.
Sanjay Sarathy, CMO of Sumo Logic, discussed with Dataconomy how features of the latest update were focused around key pain points for their customers. “One of the first challenges our customers are facing once they’ve deployed a, say, Hadoop ecosystem is deep discovery- identifying the shape and size of data in that cluster so they can really start to cleanse and distill that data down for analytic purposes. So what we’ve done is to apply the same principles around machine learning and visualisation that were an integral part of our predictive interaction technology, to streamline this stage of the process”.
Businesses today face a critical challenge in finding the links, patterns and connections in their data- whether that data is focused on revenue, security, or compliance. Sumo Logic’s Transaction Analytics uses sophisticated machine learning algorithms to find these correlations as they occur.
Transaction Analytics also adds the ability to visually profile complex transactional relationships in real-time. Traditionally, visualisation is viewed as the end-result of the data science pipeline; Transaction Analytics makes visualisation an integral part of real-time analysis, meaning you don’t have to wait until the end of your workflow to have a visual, comprehensible insight into your transactional relationships.
The abilities to unlock the causal relationships in your data & to visualise complex transactional relationships on the fly are both powerful tools for the modern enterprise. The power of helping businesses to make better-informed decisions in a more timely manner cannot be overstated, and Transaction Analytics certainly delivers on this front.
(Image credit: Sumo Logic)
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