Given the immense daily activities in the digital workplace, the ability to efficiently search and analyze log data from various business applications can bring a competitive edge. Log data is a “gold mine” of insights that are critical to IT operations, security, business analytics, and Internet of Things (IoT).
Commercial log analytics software offers ease of use, advanced features, and a large ecosystem of plug-ins. Alternatively, high-performing, cost-effective open source log analytics solutions can enable enterprises to:
We’ve helped clients implement powerful log search and analytics solutions to address enterprise needs, including risk management, compliance, and security analysis, to market intelligence, e-commerce personalization, and healthcare analytics.
For businesses, especially systems administrators, DevOps, and business analysts, a well-implemented log search and analytics solutions can deliver operational agility and peace of mind.
Reduced time to identify root causes of problems
Lower failure rates of risk calculation jobs
Reduced security risk through fraud signal identification
Higher uptime and fewer system outages
Lower licensing costs and total cost of ownership (with open source log analytics solutions)
Flexible on-premise or cloud deployment
We help enterprises build customized log search and analytics solutions using leading commercial or open source technologies like the Elastic Stack (Elasticsearch, Kibana, Beats, Logstash).
Extensive log analytics expertise and a vendor-agnostic approach, from strategy to implementation and support.
Ability to leverage NoSQL and big data technologies for faster results and more accurate decision-making.
Rapid deployment with immediate visibility into events and operational trends.
Our experts discuss the latest trends and best practices for using AI-powered search and analytics to unlock more insights and achieve greater outcomes.
How AI technologies like Natural Language Processing and Machine Learning can help improve insight discovery and decision making in data-driven enterprises.
Natural Language Processing (NLP) techniques for deriving insights from unstructured data – text documents, social media posts, mail, etc.
