The largest telecom operators are those who are providing the customer service at par excellence, at scale, with effective personalization. To be able to accomplish this, these telecom giants have a continuous need of keeping network optimization their first priority.
So let’s get to know some more about network optimization and how it involves big data.
Network optimization is all about accelerating the performance of IT and minimizing the load on the network to reduce expense and increase the flow of data traffic. Now, when it comes to the telecom industry, the phenomenon of network optimization is not just based on hardware and system architecture only, but also depends on the continually varying data consumption ratios and bandwidth of the network being utilized by the subscribers. Maintaining the equilibrium between the data strategy and the overall consumer experience is highly critical to success; however, similar to any act of balancing, it can be much stress-free than it appears to be.
Considering a major increase the forms and volumes of the kind of data within the telecommunication business such as unstructured, semi-structured, structured, it has become increasingly difficult to forecast the required network bandwidth. The ever-increasing use of Netflix has replaced the conventional media entertainment while video calling applications over the internet have disrupted the entire communication business. In the present day, telecom analysts are required to be fully capable of instantly synthesizing larger than ever volumes of semi-structured and unstructured data as well as data sourced from this parties to determine and maintain that equilibrium.
Why Network Optimization Is So Crucial For Telecom Operators?
The basics of providing a telecom service are not going to change. This means that the largest telecom operators will continue to provide the best-perceived products and services at the lowest cost possible. Network optimization is the only factor that can provide equal support for both sides of the equation, which are cost and revenue. Pertinent to cost, network optimization can be accomplished with quicker and improved understanding of data analytics and insight into the analysis of load on the network during the peak hours, maintenance of hardware, network logs, usage, and approximate real-time and granular levels of analysis, which were not possible ever before to do at scale.
On the other hand, revenue growth in network optimization can be achieved by uniting dispersed records of consumer data into a single reliable source. The utilization of sentiment data, brand perceptions, posts at social media, app logs, detailed records, satisfaction levels, and call center data in the same analysis has made it possible to gain insights that were not accessible ever before. Such data can be best utilized to improve the overall consumer experience as it empowers to make more accurate predictions about the network strain, making it possible to deliver faster than ever download speeds, reduce churn, improve already available offers, and introduce innovative products and services relevant to the market demands and consumer needs.
The Challenges Faced by Telecommunication Operators in Network Optimization
Network optimization is rather challenging for the telecom service providers. If it was convenient, every service provider in the telecom industry would have been doing it. Data stored in data warehouses throughout the multinational organizations, with diverse regulatory and legacy systems, have established an environment where data flow among the multiple stakeholders has become substantially difficult. Telecommunication service providers in a few cases have been storing data for at least a half century now, and that data is certainly not available in such format that current data scientists are familiar of seeing and working with. This established the need for putting data cleansing procedures in place, which is considered to be an additional obstacle in the regular distribution of data within a telecommunication organization.
In addition to that, telecommunication service providers are not the only one to face regulatory problems and challenges pertinent to access of data, in-house politics and culture that inhibits the flow of data, and bottlenecks occurring in the IT department, mainly because of staffing shortage and recruiting incompetent resources. When the true hold of data is achieved, approximately 80% time of an average analyst is taken up by the task of data preparation. This is another reason, creating an obstacle toward gaining the insight.
How to Power Enhanced Network Optimization for Telecom Operators
Data quality tools make real-time data synthesis possible and democratize the procedure for data analysis, even with the particularly dispersed and widespread data landscape of telecommunication companies. Telecommunication service providers can reduce the stress on their IT departments by simplifying the procedure for preparation of the data and reducing the time to gain insights sourced from the complicated network, billing, and consumer data. The eventually provides various benefits such as saving of time and resources and empowers telecom operators to cater to the ever-increasing need of network optimization.
Reduce Stress on IT
To reduce the burden on the IT department, permit non-designers to have access to data from all possible sources, secure data lineage, and minimize the requirement for valuable development time for constant analytics support.
Big Data – Approachable by Design
The solutions for preparation of the data are established to manage the extensive amount of data, where it is common to manipulate data in gigabytes and terabytes. High-performance data preparation software solutions empower quick feedback on larger volumes of data, which result in highly efficient gains.
User-friendly and high interactive illustrations such as data quality bar enable convenient validation. The user-friendly interface of data preparation software solutions empowers the users to accomplish data cleansing within a few simple clicks instead of painstaking programming.
Anticipated at-scale authentication scripts associated with data quality concerns become automated and performed by default once data preparation solution gets acquainted with the data.
Data quality tools enable analysts to work with the data in ways that were never considered achievable ever before and leverage gained insights for informed and improved decision-making in time and eventually translates into an optimized telecom network.