QUICK FACTS

HEAD OFFICE

The Bonhill Building, 15 Bonhill Stree
London EC2A 4DN
United Kingdom

COUNTRIES SUPPORTED

Global

LANGUAGES OFFERED

English

category

CATEGORIES

Big Data, Analytics, Big Data Analytics, Databases, Business intelligence, Predictive analytics, Business analytics, Artificial Intelligence, Machine learning, Social Media Analytics

INDUSTRIES

Financial Services

PRICING

Subscription

Available on request

TYPE OF CUSTOMERS

For profit

SIZE OF CUSTOMERS

Large, Global Enterprise, Small, Medium

 
Year founded:11/15/1995
Funding received:€ 0
Investors:
 
#Employees:165
Awards won:0
Vendor size:medium
 

Overview

ITRS Insights performs an impressive range of analytics across both real-time and historical data at a scale required by today’s big data world. This makes it suitable for running analytics on your business activities, your underlying technology or a combination of both.

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THE 3 KEY BENEFITS

  • 01 Powerful queries
  • 02 Simple integration
  • 03 Easy-to-use

THE 3 KEY BENEFITS

  • 01 Powerful queries
  • 02 Simple integration
  • 03 Easy-to-use
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PRODUCT FEATURES

  • Data storage and analysis

    ITRS Insights' ability to analyse and store data in both time series and semi structured databases means more information is readily available to query and compare. It’s not just about historical data, however. Real-time data can be compared against your historic data. For instance, by comparing historical trades to current messages you can track trade flows that go through multiple hops, in different formats, from one place.

  • Pattern discovery and anomaly detection

    Advanced algorithms, including machine learning, enable you to interpret periodic patterns in your data, so you can identify anomalies and future trends, and automate processes to protect against the occurrence of issues.

  • Clustered architecture

    Both the data and processing are spread across a cluster of nodes, with no data or processing reliant on a single node only. Hence, if a node goes down, the cluster can continue without issue, ensuring constant availability of both data access and analysis. In addition, the cluster is near linear scaling allowing fast computation of huge numbers of queries on the streaming data.





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