Siren, the investigative intelligence platform, today announced it had been named a 2020 Cool Vendor in Analytics and Data Science report published on May 7, 2020, by Gartner, Inc. According to the report, “organizations fail to achieve the potential of augmented analytics due to a lack of the foundation required to enable more people with siloed data.” It identified that “to harness the power of augmented analytics, data and analytics leaders must facilitate analytics collaboration with an innovative semantic layer.”

Recognizing Siren among Cool Vendors, Jim Hare, Julian Sun, Rita Sallam, James Richardson, and Afraz Jaffri Gartner wrote: “By 2022, augmented analytics will be ubiquitous, but only 10% of users will use it to its full potential. By 2022, 40% of machine learning model development and scoring will be done in products that do not have machine learning as their primary goal. By 2023, graph technologies will facilitate rapid contextualization for decision making in 30% of organizations worldwide.”

The report continued: “Augmented analytics is the future of analytics. The proliferation of augmented analytics causes the collision of analytics and business intelligence and DSML products. The converged capabilities will enable a more empowered class of analytics consumer — the augmented consumer — to add more business value by exploring their own data.”

John Randles, CEO of Siren, said: “To us, being recognized as a Cool Vendor by Gartner is further validation that our platform is a market leading investigative intelligence technology which is shaping the future direction of law enforcement, cybersecurity and financial fraud. The demand for large-scale interconnected data analysis is constantly accelerating and our platform uniquely enables this in real-time at a scale that companies and government organizations require.”

The Siren platform merges functionalities that were previously disconnected, such as big data dashboards, link analysis, search engines, and operational monitoring. It extends data analysis with unique associative and multimodal analytic capabilities, finding relationships between data sets that were simply not visible before.

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