Big Data in Telecoms

New trends in the Business Intelligence world point to the use of Big Data as a tool to get more information about customers and improve the decision-making process.

Big Data consists of a big volume of information that can be extracted from different sources, mainly from digital environments where customers are involved. So we can use varied information, quantitative/qualitative data, and get to know more about your customers.

Big Data contemplates unstructured information than can be gathered from many sources due to the use of new devices such as cell phones, notebooks, blogs, social networks, etc. Companies are recognizing the potential of information in order to achieve a better understanding of the business as well as customer’s behavior.

As far as advantages are concerned, Big Data can help improve customer fidelization strategies for the Marketing and Sales departments in companies. The key is to get the best way to process the information and take decisions in real-time.

Big Data or Business Intelligence

As we can see from the following graph, the difference between Business Intelligence and Big Data are defined by these factors:

Big Data or Business Intelligence

Unlike Big Data, which is focused mainly on unstructured data, Business Intelligence constructs important information to take corporative decisions. This information is known and focused on certain areas (sales, production, etc). In any case, it’s always based on existing and targeted data.

Big Data for the Telecommunications Industry

Big Data is changing the telecoms industry. Communication Service Providers (CSP) control the communication infrastructure, which allows them to have more data than any other industry on their customers’ location, how they interact and how they make business transactions.

Currently, Big data challenges are:

  • Volume: Amount of data coming from sources such as 3G/LTE/4G mobiles networks, GPS, social networks, web contents, etc. Even the advent of IPv6 will create as many IP addresses as there are grains of sand on Earth, allowing the number of Internet-connected devices to grow exponentially.
  • Variety: A large number of diverse data sources to integrate. It refers to the number of different types of data. Social networks, mobile devices, and sensors that monitor everything from utility use to medical compliance are flooding CSP’s infrastructures with data in myriad formats (structured data, semi structured data, unstructured data). CSP’s must enrich their CDR data with location-based services, financial information, and other structured data to standardize it for business intelligence platforms before they can analyze it.
  • Velocity: It refers to the speed at which data is processed. CSP’s must integrate their systems and implement mechanisms to deliver data from different sources. The process to take decisions depends on how fast the data is available and updated. We have different types of velocities such as Real Time Analysis, Near Real Time, Periodic and Batch.

Here are some CSPs who are using Big Data:

  • Vodafone and Argyle Data are using big data to combat fraud.
  • Globe Telecom is gaining marketing agility through smart promotions.
  • Ufone uses advanced analytics to study and capitalize on customer behavior.
  • MTS India relies on HP Vertica on a highly competitive telecom market.

Conclusions

In the coming years, the use of Big Data in telecoms will increase. It will enable companies to adapt themselves to the changes the industry demands since information plays an important role in making decisions and meeting corporate objectives.

Intraway Corporation provides a business intelligence solution to its customers in order to exploit the information stored in the OSS/BSS. Customers, such as Media Networks, Claro Puerto Rico and Claro Dominican Republic, have a BI solution to analyze data stored in their different systems either Provisioning Suite or external systems (eg. Nagravision, Osadia, ADA, M6, etc).

Finally, Big Data and Business Intelligence are different concepts, but they have the same purpose which is to provide information for analysis and taking decisions. Both systems will coexist in future, the information from Big Data can be integrated to complement a BI system and viceversa.

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