Telecommunications Industry

Telecommunications Industry Solutions

Industry Background
Pain Points
Strengths of Rapids Data Solutions
Value Realization

As smartphones continue to proliferate on a global scale, massive amounts of data are transmitted over commercial networks every day. The advent of even faster 5G connectivity further fosters the explosive growth of the structured and unstructured data generated between people and things and things and things such as autonomous cars, wearable devices, smart TVs, automated drones, industrial robots, etc. Telecommunications companies collect and analyze data from call detail records, mobile phone usage, network equipment, server logs, billing, and social networks to improve network efficiency and better serve their customers.


Legacy data systems do not have the capability to empower users to access, contribute, and share massive amounts of data in real time.


It has become extremely difficult for the telecom industry to cope with the current fast-growing varieties of information beyond structured data.

03 Old billing systems have a lack of effective self-service support, which keeps the O&M costs high.


Long customer-service hold or wait times cannot meet the real-time query requirements, which can result in a higher churn rate.


The inability to mine all data efficiently hinders the path to product innovation and cuts down effective marketing and advertising revenues.


The resurgence of telecommunications fraud poses a serious threat to telecommunications companies.

01 As a distributed in-memory big data platform, RDP supports parallel processing across an arbitrary number of nodes in a cluster to ensure the lightning-fast massive data processing and analyzing speed.
02 The HDFS distributed data storage system uses commodity servers to help telecommunications companies realize PB-level massive data storage at a low cost.
03 RDP supports the integration of structured, unstructured and semi-structured data derived from a variety of sources, such as call detail records, location data, server logs, social media data, etc.
04 RDP can integrate streaming and historical data without the complex ETL process to support the optimization of self-service tools.
05 The high-performance RDP platform empowers telecommunication companies to collect, store, and analyze data across millions of customers and from billions of transactions to better understand customer behaviors and preferences to segment the market efficiently.
06 Leveraging the distributed in-memory computing technology, RDP empowers agents to run highly sophisticated ad hoc queries concurrently.
07 With the real-time data analysis capability and the AI-enabled modeling tools, RDP can help telecommunication companies gain real-time insights to detect and reduce fraud.
Distributed storage Rapids Data's big data platform allows for horizontal storage capacity expansion without compromising performance.
High hardware availability Through software design, hardware failures can be detected and mitigated as part of a routine procedure.
Non-shared architecture The independence among the distributed machine nodes, data center and data mart prevents resource contention and ensures the efficient and stable operation of the platform.
Exploratory self-servicing analysis In response to ever-changing business needs, the Rapids Data platform provides self-service data preparation and analytics.
01 RDP eliminates I/O bottlenecks to ensure the lightning-fast speed to process and analyze massive amounts of data. The HDFS distributed data storage system uses commodity servers to help telecommunications companies realize PB-level massive data storage at a low cost.
02 The high-performance distributed computing framework of RDP monitors network operations and detects performance bottlenecks and/or system anomalies automatically so that problems can be fixed quickly and networks can be recovered and optimized to maintain healthy operations.
03 With self-service tools, customers can find the information they need and do more on their own such as check their data usage and review their bills online. It saves time for customers and lowers labor costs for service providers.
04 With a holistic view of customer data such as service usage, cost of services, usage behavior, customer preference, etc., customer service personnel can respond to customers’ enquiries in real time effortlessly.
05 Personalized products can be developed and real-time promotions and advertising effectiveness can be achieved.
06 Fraudulent phone usage patterns can be detected in streaming data and analyzed to minimize the impact of fraud.