Combine raw data (device data, CDR and VoIP records, network performance data) and stored data (billing and rating records, demographic data and call center feedback) to minimize loss revenue from call fraud and suboptimal routing, reduce customer churn with targeted service monitoring, and optimize network performance by accelerating all operational processes.

Example applications & questions answered

Network Operations

• Where is network congestion a problem, and from which traffic types?
• Which network equipment are showing indicators of failure or are causing service degradation?
• Which services and OTT providers are using the most bandwidth?
• Which subscribers and applications drive the most OTT traffic?

Service Assurance and Customer Experience

• Which services are failing most often?
• How can I relate service activation with the performance for an on-demand IP-based service?
• How can I avert service degradations before they become service impacting?
• How can I monitor my SLAs for corporate and OTT application providers?
• How can alert on bandwidth hogging?
• How can I identify complex patterns of dependencies that indicate a service impacting issue?

Revenue Management and Fraud

• Real-time rating and charging.
• Real-time fraud and roaming fraud detection.
• Real-time policy updates, charging, bandwidth updates and promotions.
• Real-time integration between billing, service assurance and customer management for real-time billing adjustments and proactive subscriber notifications.

Marketing and Product Management

• How can I get accurate service usage date in order to create improved pricing plans?
• I want to implement real-time pricing and policy updates. How can I implement my plans?
• Where are my top N customers by usage, revenue and roaming?
• Which offers and promotions have been most successful?

Real-time Executive Dashboards

• How do traffic trends by type and performance compare to forecasts?
• What traffic types and subscribers are the most profitable?
• Which of my customers drive the most volume on my network?

Real-time Intelligence from Subscriber, device, network and service data

SQLstream collects and mediates all data – structured, semi-structured and unstructured, on a single distributed real-time data management platform. A large range of adapters and remote collection agents are available, including:

• CDR, log files, sensors, GPS and M2M data
• Cells, base stations and micro cells
• IP routers and switches
• Service Delivery Platforms
• Devices, smartphones and CPE equipment

Real-time Decision-making

SQLstream offers real-time, streaming integration of analytics and intelligence across all lines of business and existing system silos. Network operations, customer management, revenue management, planning, IT and service assurance all benefit from real-time information and action, and the continuous sharing of streaming intelligence. Regardless of your business model, whether wireless carrier, fixed line operator, cable service provider, application service provide or virtual operator, SQLstream works with you to deliver real-time intelligence from your device, subscriber, network and service data, providing real-time operational intelligence applications for :

• CDR analytics for real-time rating, billing, QoS monitoring and fraud detection.
• Predictive analytics for network and service degradation.
• Optimization of network capacity utilization.
• Analytics of OTT service usage by subscriber and provider.
• Customer churn and Quality of Experience metrics.
• Location-based and geo-spatial services.
• IT operations and log file analytics.

Lowest cost of performance with rapid ROI

SQLstream eliminates the complexity and costs associated with traditional IT models for telecommunications network and service management. The SQLstream model is to analyze the live data before storing, enabling service providers to deliver real-time, low latency analytics and to drive automated actions, without the cost and latency overhead of first having to structure, map and store the data.

SQLstream therefore enables service providers to centralize their real-time data management, and to deliver actionable, real-time insights without expensive integration services, data scientists, hardware and administration overheads. With SQLstream, service providers are:

• Improving Quality of Service through predictive analytics on capacity bottlenecks.
• Improving consumer QoE through predictive analytics and automated actions to avert service degradation.
• Reducing the cost of real-time operational processes through improved operational efficiency, lower infrastructure, integration and IT lifecycle costs.
• Offering real-time promotions and improved location-based services through streaming integration of device location, promotions and subscriber profiles.
• Real-time fraud detection and prevention.
• Delivering real-time analytics across the organization on consumers, devices, networks and services.

Combine raw data (equipment and network, seismic and weather, social media and geolocation sensors) and stored data (performance and maintenance reports, demographics) to monitor operations and avoid hazards and equipment failures, deliver real-time pricing and optimization of energy utilization and consumption, and automatize processes to address the skills gap.

Example applications

• Real-time cost modeling and commodity pricing models
• Instant production volumes predictions
• Real-time compliance reporting
• Early identification of potential infill well locations

Combine raw data (online and on-site transaction records, banking volumes, online trading, network data, social media) and stored data (call logs, credit card history, demographics, performance reports) to increase customer acquisition and retention, detect and predict security threats in real-time, and eliminate costly network failures.

Example applications

• Customer churn prediction models
• Real-time staffing
• Responsive security and fraud monitoring
• Dynamic credit risk scoring
• Real-time compliance analytics

Combine raw data (from medical devices, dispensing systems, surgical implants, personal event recorders [i.e. CERs], mobile devices, medical networks) and stored data (personal history, network and security data, demographics, research data) to detect medical patterns sooner to reduce overall healthcare expenses, increase data completeness and accuracy for better patient outcomes, and reduce operational costs through a centralized data processing system.

EXAMPLE APPLICATIONS

• Real-time patient monitoring
• Dynamic inventory tracking
• Real-time conditions monitoring (fridge and warehousing, radiation levels, etc.)
• Real-time data security and fraud monitoring
• Real-time medical robotics operations and control
• Geolocation- based services (i.e. fall detection)
• Dynamic dispensing systems

Combine raw data (online and on-site transaction records, in-store tracking and RFID tags, mobile devices and customer generated content) and stored data (inventory and network data, transaction history, reviews, demographics) to increase sales through customized campaigns, optimize supply chain management, and improve fraud detection and protection through real-time monitoring and automated actions.

EXAMPLE APPLICATIONS

• Customer behavior prediction models
• 1-to-1 marketing campaigns
• Dynamic supply chain management
• Real-time data security and fraud monitoring
• Geolocation- based services
• Dynamic staffing and operations

Combine raw data (sensor, network, transaction records, social media) and stored data (personal history, network and security data, demographics, research data) to optimize operations by detecting system threats, monitoring and integrating all processes in real time, and automatizing actions. Increase revenue through support of multiple applications, millions of users and subscription-based aggregated feeds.

EXAMPLE APPLICATIONS

• Smart homes (energy, water and waste management, remote control systems, security systems, art preservation)
• Smart lifestyles (content consumption, fitness, fashion, pet care, travel)
• Smart environment (forest fire detection, air pollution and snow level monitoring, landslide and avalanche prediction, earthquake early detection)
• Water monitoring applications (tap water quality, chemical leakage detection, sea pollution monitoring, water leakage/waste detection, flood prevention)
• Physical security applications (perimeter security control, radiation and hazardous substances measuring, liquid detection)
• Smart agriculture (soil humidity and pest monitoring, greenhouse networks, offspring care, animal tracking, weather station control)
• Smart logistics and industrial control (quality of shipment conditions, item and storage incompatibility detection, fleet tracking, vehicle and machine autodiagnosis, air/temperature/ozone monitoring)

Combine raw data (waste and lighting networks, road and vehicle status, social media and geolocation sensors, public call systems and alerts, seismic and weather data) and stored data (traffic and maintenance reports, demographics, seismic and weather data) to decrease traffic congestion, reduce pollution and system outages, and optimize utilization of services through an integrated, multi-modal platform for monitoring and control.

EXAMPLE APPLICATIONS

• Real-time city planning
• Real-time traffic monitoring and control
• Smart water, lighting and waste management
• Real-time air, soil and water quality monitoring
• Early fire, flood, and earthquake detection
• Structural health monitoring
• Noise level monitoring