For telecommunications companies, big data is at the heart of improving both customer experience and network utilization. A well-managed Hadoop data lake provides telecoms operators with a cost-effective, scalable architecture for collecting and processing massive volumes of disparate data types.
Already in production with enterprise telecommunications providers worldwide, Zaloni’s Platform is the industry’s only fully-integrated data lake management platform. Its intuitive UI makes it easy for telcos to ingest, organize, enrich and extract data within the Hadoop data lake.
Telecoms struggle to understand what subscribers do with their devices or with their data plans. What mobile apps, streaming services are popular? Understanding subscriber usage can capture more revenue by creating tiered pricing or specific packages for specific social media, video streaming or map applications. Unfortunately, capturing and aggregating this information involves analyzing millions of source and destination IP addresses with subscriber data to capture individual data usage.
The Zaloni Data Lake Management Platform manages the ingestion of millions of network data points (IPFIX, DPI, Bulk Stats) and correlates this to individual subscriber data in near real-time. It creates historical profiles of individual subscribers that can be vertically aggregated or segmented for marketing analysis. This enables operators to increase revenue by creating data plans based on apps or services that are relevant to their targeted customer profiles.
Despite all the data available from network elements and data collectors such as probes, it’s still difficult to identify which wireless network elements are overloaded or where there are network bottlenecks (dropped calls, slow data connections). Poor network utilization results in wasted resources and increases customer churn.
Zaloni’s Platform manages the ingestion of millions of records (CDR, DPI, SNMP, etc.) from wireless network elements into a data lake and correlates this with network utilization. Network load can be analyzed in near real-time, enabling planners to reallocate network elements dynamically using SDN and NFV, increasing utilization and reducing customer churn.