Most Big Data Analytics Projects In Telecommunications Companies Are Not Obtaining The Promised ROI. Is Big Data Analytics Another Hype? How Actionable Insights Must Be Deployed To Extract Value?
All telecommunication operators are aware of the value of the information that lies in the huge amount of data they obtain from their clients. Analytics is an important toolset that has expanded in usage dramatically over the last decade and has developed further with the emergence of new Big Data technologies.
However as shown in different studies, operators are not getting the expected ROI from their investments in this discipline. They are not exploiting the true potential that is hidden in the data.
The Top 3 Reasons Why Analytics Is Not Delivering Its Full Potential
Studies reveal that there is a lack of concretion in the actions drawn from the results of the analysis. This makes the return on investment difficult. What are the causes?
1. Unawareness of all the sources of data that can be found in a telecommunications operator: The data is dispersed in different departments, many a time silos, without other departments knowing of its existence. Lack of cleaning, integration and processing of all this data to obtain relevant variables may lead to the incorporation of incomplete sets of data in algorithms which, in the end, obtain diffuse or erroneous conclusions.
For example, it’s possible to know how many devices are connected in each house. Does the marketing manager knows it? And if you’re not processing it you’re loosing opportunities to increase upselling campaign effectiveness and ARPU.
2. Lack of integration with systems and procesess: Even though the results obtained by the data scientists may be correct, if their conclusions are not being integrated into the processes and systems of the operator,we will only have orientations and recommendations, but the operator might not be conducting them systematically. This leads to not obtaining the expected ROI and to the failure of the analytical project and inevitably the lack of support for new advances and improvements from the management.
3. Not focused on business: The real drivers, in recent years, of analytical breakthroughs are more focused on finding the best technology and the best algorithms. However the focus is not on finding the greater value for business from the analytic results.
Many projects are focused on finding the best algorithm to predict each customer churn score, but very few are worried about how to integrate this score into real actions to reduce churn, that it’s the real business problem.
Business Matters Most
The focus of analytical projects must be changed. Analytical projects should be led by marketing managers, who can define their problems and the need for insights and integration needs with their actions, processes and systems. Like in most successful IT projects and strategies they undergo a business-driven strategy supported by management.
Operator processes need to take second place as a large number of processes are involved both in the discovery of data and discovering processes in the operator’s areas where improvements can be obtained through analytics. Therefore the collaboration of all departments of the company is essential.
Indeed underlying technology is very important and must continue with the evolution it’s been taking in the last decade. Nevertheless technology must be a tool for the business and should serve the business to improve its indicators. The combination of new technologies from Big Data analytics with traditional BI tools will provide actionable insights.
For the implementation of Big Data analytics projects to deliver results, end-to-end applications are to apply insights into actions in the carrier’s systems and processes.
The goal in all of this is to obtain actionable insights to create value for the business. These can be actions with clients, processes, improvements, new products… This is what should always lead Big Data analytics projects, its results will justify the investments made.