Big Data era: the trend of refined operation

In the big data era, with the rapid development of the telecom industry and the upgrading of the network standard, as the scale of 4G mobile networks continues to expand, the mobile network data traffic will surge again. For operators, traffic has become the main revenue growth point, but how to refine network operations and improve the profit of traffic operations has become an urgent problem for operators.

For operators, optimizing the network, improving the basic channel capabilities of mobile networks, and intelligent traffic management have become the most direct and effective means. But how to precisely scale up and optimize, as well as precise analysis of traffic operations, providing strategies and final feedback on the market, whether the investment and operating income match, it is the main means to effectively collect and analyze mobile network data, provide a strong basis for the analyzed data and form a strategy to provide guidance for the refined operation of the network.

Although the traditional method can deploy various collection systems in mobile networks to collect, monitor, and store data, and analyze the stored data, decisions or strategies are obtained based on the data analysis results. However, the traditional methods of data collection, analysis, and decision making and market support are all independent systems, including intelligent control and traffic analysis, user perception, network data analysis and monitoring, signaling analysis, boss systems, and other systems are not linked, especially in the case of multiple manufacturers and systems, the various analysis systems are varied and there is no association analysis, there is no organic whole, independent of each other, and there is a lack of end-to-end full data analysis. As a result, the operator's Market Decision-making basis is relatively discrete and summarized as follows:

1. network optimization is time-consuming and labor-consuming to handle customer complaints. The traditional Discrete tools lack coherent analysis, resulting in a long response time to customer complaints and reducing customer satisfaction.

2. Network Optimization cannot reflect and solve network problems in a timely manner. It can be viewed as a controllable and predictable network planning and network optimization method.

3. Massive Data Analysis is difficult. The current processing methods are difficult to adapt to the processing of large data volumes, and it is difficult to further refine the analysis.

4. The pipeline traffic is invisible and messy, with a large number of operating businesses. Effective monitoring and orderly management are not implemented;

5. The quality of equipment indicators cannot be mapped or reflect the customer's actual perceived experience, and the customer's perception and evaluation of the entire network cannot be determined;

6. The traffic is increasing, but the operator's revenue is gradually decreasing. The O & M data cannot be effectively converted to powerful support for marketing;

D1net comments:

To meet the above problems, the operator effectively integrates existing data analysis systems into a sharing center, provides big data analysis and processing, and helps carry out end-to-end data business analysis, evaluate and safeguard the virtual experience of VIP customers, optimize network rules, and provide basic support data for marketing. To adapt to the growing network scale and complexity requirements and achieve refined operation, this is the trend of the times.