Data Analytics in the Telecommunications Industry, Research Paper Example

Data analytics has become an integral part of business success. As the 21^st century has been heralded as the information age, information is a virtual currency. As such, information on consumer behaviors and habits can be useful when analyzed. All of the Fortune 500 companies employ data analytics to assess performance, increase sales and maximize profit (Pan, 2014). The telecommunication industry is one of the most vibrant industries. Network operators have access to extensive information on their customers. This information can be compiled and analyzed in a manner that trends can be identified, helping the company adjust with the changing market.

Big Data Analytics in the Telecommunications Industry

Advantages

In the telecommunication industry, Big Data analytics has numerous advantages. One of the key roles of data analytics in the telecommunications industry is to analyze customer behavior, trends and patterns. This allows telecommunications the ability to effectively structure and develop services tailored for the consumers. This offers the customer personalized service, ensuring consumer loyalty

Data analytics provides telecommunication with relevant data for market segmentation and targeting. By analyzing data on customer behavior and patterns, telecommunications can better understand the market by having the data required to highlight the existing segments within the market (Pehrsson, 2005). This allows for targeted marketing campaigns and promotions.

Planning and optimization is a crucial benefit of the use of data analytics within the telecommunications industry. The ability to understand consumer behavior, likes, dislikes and trends provides telecommunications with the proper information to base future planning while optimizing on the resources available.

Disadvantages

The effective use of data analytics requires the use of an unconventional corporate structure. Most telecommunications employ the top-down approach in structuring their organization (Sawant & Shah, 2013). This essentially entails finding business problems then seeking data relevant to the problems so as to enhance decision making. Even though this procedure may produce results, they may not be as effective as using a different approach. This involves beginning with the data and identifying the problem before its actual occurrence. This bottom-up approach may be difficult to implement.

Obstacles

Skill Shortage

In implementing data analytics, management is faced with a number of obstacles that may hinder successful implementation. One of the most common obstacle is the skilled labor. It is becoming more difficult to get individuals with the background knowledge in Information Technology (IT) and data analysis (Sheikh, 2013). Translators are vital in creating a bridge between analytics, IT and business unit teams. These employees are central in developing new data methodologies, tools and strategies.

Tool-Oriented Inefficiencies

Due to the shortage of the analytics skill, the development of inadequate self-service reports and tools that prevent access to data. By creating ineffective tools, experienced professionals may find it difficult to access data.

Diminutive Ongoing Education

As businesses, especially telecommunications, continue to grow, they collect more information that becomes increasingly complex to analyze. Consumer behavior can be influenced by a host of elements that may require complex analysis of data to understand and accurately predict. With over 60% of analytics professionals admitting to no ongoing education, it has become difficult to effectively analyze data (Sheikh, 2013). There is need for the development of new tools.

Best Practices

The best strategy for a company in the telecommunication industry can be defined by a series of best practices that are meant to help the company realize efficiency. These include;

Settingspecific goals for data analytics; this involves acknowledging the different functional areas of the business and setting specific goals for the application of data analytics in the given functional areas. The company has to define specific parameters of success or failure of the application of data analytics.

Recruiting a broad based team is crucial in ensuring the best possible team is in charge of data analytics. In order for data analytics to be successful, there is need for a broad understanding of the different aspects of the telecommunications industry. As such, the team in charge of data analytics should comprise of individuals from different departments and/or disciplines.

The kind of data that is required for a successful data analytics operation has to be determined beforehand. It is important in collecting information that is relevant to the given functional area to avoid the use of “useless” data.

The type of IT buy-in is integral in determining the success of data analytics operations. It is important that a business secures a buy-in that can easily integrate with its existing technological and data infrastructure.

Effect of Data Analytics

The telecommunication industry has been significantly transformed by data analytics. The industry is currently highly segmented as a result of extensive application of data analytics. Telecommunications can better understand the market and effectively segment it. This has led to the creation of highly personalized services for consumers. Even though data analytics has improved business performance, it has also infringed on consumer privacy rights. Telecommunications have access to confidential consumer information that can be used to analyze consumer behavior and profile each individual for advertisement purposes. This has attracted considerable discussion.

The telecommunication industry will continue to experience extensive use of data analytics in the foreseeable future, 10 years. The global market is increasingly becoming information oriented, making consumer behavior vital information for successful marketing campaigns.

References

Pan, J.-S. (2014). Intelligent data analysis and its applications. The Euro-China Conference on Intelligent Data Analysis and Applications.II. Cham: Springer.

Pehrsson, A. (2005). International strategies in telecommunications : model and applications. London: Routledge.

Sawant, N., & Shah, H. (2013). Big data application architecture Q & A : a problem-solution approach. New York: Apress.

Sheikh, N. M. (2013). Implementing analytics : a blueprint for design, development, and adoption. Burlington: Elsevier Science.

Strouse, K. G. (2006). Strategies for Success in the New Telecommunications Marketplace. Norwood: Artech House.