For modern businesses faced with increasing volumes and complexity of data, it’s no longer efficient or feasible to rely on analyzing data in BI dashboards. Traditional dashboards are great at providing business leaders with insights into what’s happened in the past, but what if they need actionable information in real time? What if they want to use their data to estimate what may happen in the future? Companies are taking notice.
When organizations track metrics by the thousands, millions, or even billions, it’s helpful in many ways to understand which metrics have close relationships, meaning when one metric behaves in a certain way, one or more additional metrics can be expected to behave in a similar or opposite way.
AI-based monitoring and anomaly detection is the key to ensuring that businesses can keep pace with the high level of service required for mission-critical applications. Early, contextual detection is a basic requirement for speedy resolution. AI-based monitoring creates more visibility and provides the agility needed to mitigate the outages, blackouts, glitches and issues that do and will happen.
According to a recent Capgemini research, fewer than half (48%) of consumers feel that the connectivity services that they have today adequately meet their remote needs. Still, many CSPs openly admit that they often hear about service issues via social media and sites like Downdetector. And with the fixed/mobile convergence, a negative home broadband experience now has the potential to cause churn for CSPs’ mobile customers too.
The financial world is being increasingly digitized and decentralized with many more networks, data sources and movements that need to be reconciled and monitored — not to mention satisfy compliance requirements with various regulatory bodies. The digital transformation and decentralization of the Fintech segment has resulted in increasing channel complexities, more third party applications and a higher volume and velocity of payments data that need to be monitored in real-time.
Payment gateway analytics tracks the payment processing journey and related event data across all payment gateways. When used efficiently, payment gateway analytics can benefit businesses by providing insights into their revenues, payment trends, and customer behavior. Payment gateway analytics provides much needed visibility into the payments environment to enable the fast detection of transaction performance issues, anomalies or trends.
In 2020, the gaming market generated over 177 billion dollars, marking an astounding 23% growth from 2019. While it may be incredible how much revenue the industry develops, what’s more impressive is the massive amount of data generated by today’s games. There are more than 2 billion gamers globally, generating over 50 terabytes of data each day.
Businesses are flooded with constantly changing thresholds brought on by seasonality, special promotions and changes in consumer habits. Manual monitoring with static thresholds can’t account for events that do not occur in a regularly timed pattern. That’s why historical context of influencing events is critical in preventing false positives, wasted resources and disappointed customers.
How close can CSPs come to realizing the zero touch network vision, and what are the best next steps for getting there? To discuss this question Anodot brought together a panel of experts, including Kim Larsen, CTIO of T-Mobile Netherlands; Ira Cohen, co-founder of Anodot and the company’s chief data scientist; Fernando Elizalde, analyst at GSMA Intelligence; and moderator Justin Springham.
For many CSPs, increasingly complex networks and immature technological solutions result in a typically long time to the detection and resolution of incidents that impact the customer experience, the brand’s reputation, and the bottom line. With RAN, Mobile and IP core, transport and applications and dozens of other integrated components, the network is one of the most complex areas to monitor.
