Systems | Development | Analytics | API | Testing

Anodot

Business Monitoring for Gaming: Catch More Profit Opportunities with AI

Anomalies don’t have to be a fear factor; they could even present an opportunity to make money. Imagine detecting positive spikes in in-app purchases, conversions, or gaming activity in real-time and then having your business monitoring system identify what caused them 10x faster than you can now – autonomously. With 95% accuracy in the root cause analysis you could replicate and capitalize on the deviation immediately.

Organizations Grapple with Skyrocketing Cloud Costs, Anodot Survey Finds

The pandemic upended business for many or at the very least cast a grim shade of uncertainty, so, as many took to working from home, they also were commissioned with cutting waste. Among the biggest sources of misspend in 2020 – cloud services. And remote work may have actually spurred the problem, as organizations migrate more applications to the cloud to support these workers.

Anodot vs. AWS: Which Has the Most Accurate Cloud Cost Forecasts?

The move to cloud computing has been a no-brainer for many enterprise companies. But cloud computing is an expense that, unlike many other operating costs, is largely variable. Many companies — including the fastest-growing startups, largest enterprises, and leading government agencies — choose AWS to help them streamline fragmented processes, reduce costs, become more agile, and innovate faster.

Automated Anomaly Detection: The next step for CSPs

Today’s telecom engineers are expected to handle, manage, optimize, monitor and troubleshoot multi-technology and multi-vendor networks, in a competitive and unforgiving market with minimal time to resolution and high costs for errors. With the ongoing growth in operational complexities, effectively managing radio networks, current and legacy core networks, services, and transport and IT operations is becoming a radical challenge.

Bridge the gap in your OSS by adding an AI brain on top

Telecom companies monitor their network using a variety of monitoring tools. There are separate fault management and performance management platforms for different areas of the network (core, RAN, etc.), and infrastructure is monitored separately. Although these solutions monitor network functions and logic – something that would seem to make sense — in practice this strategy fails to produce accurate and effective monitoring or reduce time to detection of service experience issues.

Powering Algorithmic Trading via Correlation Analysis

Finding relationships between disparate events and patterns can reveal a common thread, an underlying cause of occurrences that, on a surface level, may appear unrelated and unexplainable. The process of discovering the relationships among data metrics is known as correlation analysis. For data scientists and those tasked with monitoring data, correlation analysis is incredibly valuable when used for root cause analysis and reducing time to remediation.

The Route to Automated Remediation

An abundance of information can be daunting for any company. If internal teams do not know where the data is, it might hamper their efficiency at the cost of data quality and cleanliness. From a cost-effectiveness viewpoint, organizations are likely to waste excessively by hanging on to redundant data or storing varied data in one location irrespective of their sensitivity level.