The purpose of this use case is to use of big data to employ bots in call centers that are more anthropomorphic and realistic. There is a database created aiming at developing speech technologies that transform audio calls into relevant information for the Call Center that can be used to assess its performance and/or to screen automatically phone calls. I-BiDaaS product will improve the number of audio calls that can be processed per time unit. Additionally, it will assist for accurate location prediction with high traffic and visibility. This would be valuable insights on the audience, understand the behavior of local and non-local customers over various periods of time (e.g holidays), and extract insights on the behavioral patterns of groups of people.It is planned also to execute algorithms and further improve the routing and placement of the telecommunication equipment that is already in place or arrange accordingly the new equipment obtained.


Telecoms collect massive amounts of data and a key challenge is to exploit this data to improve their business. Most big data related telecom use cases fall into these main categories: (i) customer acquisition and retention, (ii) network services optimization, and (iii) security. Data-driven improvement of services or product is a key: telecoms need to share data between cell towers, users and processing centers, and due to the sheer volume of this data, it is important to process it near the source and then efficiently transfer it to various data centers for further use

New distributed messaging systems are required to effectively transport huge amounts of data and to make this data available with reliable geo-distributed replication across multiple data centers. Additional challenges include real-time deep packet inspection to optimize traffic routing and steer network quality of service, real-time call data record analysis to identify fraud, event-based marketing campaigns that use geo-location and social media, allowing differentiated responses, and many more.