Prelert Partners with Decision Lab
May 17, 2016
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Prelert announced a partnership with Decision Lab, a big data consultancy and systems integrator with proven expertise in automating and deploying solutions on the Elastic Stack.

The partnership builds on Decision Lab’s core competency of rapidly deploying technology that provides business users with the tools and information they need to make data-driven decisions. Prelert’s behavioral analytics engine extends this capability by automating the analysis of massive data sets to quickly identify IT operations, business operations, and IT security issues.

“Organizations are creating more data than ever before, but they struggle to get value from it. We know how to eliminate the complexities of gathering, analyzing, and consuming data so any user can get the insight they need in just a couple clicks,” said Nathan Necaise, CEO at Decision Lab. “We’re focused on delivering the best solution for each client and we’re finding that the majority of systems we’re building require Elasticsearch. Partnering with Prelert to run within the Elastic Stack, gives us another very powerful tool to speed time to insight.”

Through the partnership, Decision Lab will begin deploying Prelert’s Behavioral Analytics for the Elastic Stack to new and existing customers, including those who are already using Elastic and those who are looking to migrate from other Big Data tools. Decision Lab customers include organizations in the healthcare, cybersecurity, finance, cloud computing, and commerce industries, as well as the Department of Defense and other civilian Federal agencies.

“Decision Lab has the knowledge and expertise to help customers architect and deploy cost-effective big data solutions, which provide better performance and enable key insight into the information that matters,” said Mark Jaffe, CEO of Prelert. “We share a lot of the same goals with Decision Lab – namely giving organizations the ability to get tremendous value out of massive amounts of data. Working with Decision Lab on Elasticsearch big data deployments of Prelert’s machine learning-based solution will enable us to better serve our joint customers.”

Behavioral Analytics for the Elastic Stack is powered by Prelert’s machine learning algorithms and capabilities, which have been proven in Prelert’s other analytics products including its widely deployed Anomaly Detective solution. Using automated analysis of each customer’s data, Prelert’s analytics create highly-accurate, always up-to-date statistical baselines of normal behaviors. From these baselines, Prelert’s software detects, scores, and links unusual activity that could indicate IT operations problems, IT security incidents, or business interruptions. The automated analysis flags real issues in near real-time, providing much greater insight than traditional data monitoring rules and thresholds that return false positives if set too strictly, miss activity if set too loosely, and become outdated over time. Prelert’s analytics also include statistical influencer tracking, which provides critical contextual data for each detected anomaly so the root cause of issues can be identified and resolved quickly.

Some of the key benefits of Prelert’s Behavioral Analytics for the Elastic Stack include:

- Easy Use Case Deployment – parameterized use case configurations that don’t require programming or data science knowledge, and a library of pre-configured use cases that can be easily modified to enable quick time to value.

- Early Problem Detection – automated baselining, continuous online learning, and accurate anomaly detection identify IT operations problems, IT security incidents, and business interruptions quickly, often before users report them.

- Root Cause Discovery – statistical influencer tracking to provide critical context for investigating root causes of cyber attacks, IT operations issues, or business operations issues.

- False Positive Reduction – the additional context provided by statistical influencer tracking also drastically reduces false positive alerts compared to traditional monitoring and alerting approaches.

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