Challenges telecom operators face with data analytics projects.

Since the beginning of my professional career in 2003 and through the specific roles in my telecommunication and information technology industry career path, I have always wondered whether the business reports generated by the systems I supported were given any significant management attention. I can now say that they are. Now more than ever, it is clear that executive management pay full attention to these reports and rely on them for strategic forecasting, prediction, and decision-making.

This makes the importance of reliable and insightful data analytics systems paramount to the telecom enterprise.

Senior leaders are always keen to make the right decisions in order to avoid crises and prevent negative financial impacts that can send their companies on troubling trajectories. Reports about business operations and performance are critical sources of information for decision-makers and their ability to anticipate and predict the future.

With fast-paced innovation in the telecommunications digital sector, big data driven analytics solutions are poised to emerge as significant developments and disrupters in the industry. Today, we deal with data characterized by variety, velocity, and veracity.

I would like to share some insights and tips from my experience with data analytics – highlighting challenges and suggesting solutions to overcome them.

The telecom market has experienced a surge in the introduction of new products and services. Complex networks and connectivity setups generate a vast amount of data. Telecom operators must leverage the sizable data generated with powerful downstream analytics solutions in order to get accurate, descriptive and predictive reports. Telecom operators are concerned with two factors about data analytics projects. The first is using the right and effective data analytics tools that can supply meaningful insights, and the second is building and maintaining a professional, data-skilled team.

Day after day, the data analytics tools become more crucial for connectivity service providers, specifically telecom operators, as they strive to gain insights into business operations, automation processes, customer behaviors, convergence measurements, service-centered KPIs (Key Performance Indicators), market trends, and forecasts. No wonder investing in data analytics is no longer optional for telecom operators.

Data analytics projects rely on adopting the right tools and techniques to achieve their purpose. Here is a list of some challenges telecom operators face implementing data analytics projects.

  1. Data Complexity

The telecom industry is highly competitive, with companies constantly trying to improve their products and services. Telecom operators must be able to quickly find business opportunities and act accordingly in the market by introducing new services and products. With the highly competitive nature of this business, new services continue to be introduced every year, and telecom operators continue to evolve their network infrastructure. These evolutions result in new data sources. The new data is often unstructured and comes in various formats, making it challenging to process and analyze. Keeping up with the fast-evolving pace of the data sources is no small fete. These challenges are substantial factors to consider while starting and executing a data analytics project.

The right tools along with skilled professionals are critical to reduce the timeline needed to adopt any data generated and subjected to analytics processes.

  1. Data Quality

I have encountered instances in my career when data quality affects business outcomes. A friend of mine would complain that internet service providers were continuously calling him. They asked him to settle overdue amounts for his internet service, however, he was not subscribed to any package from this company. Obviously, the unpaid account was linked to the wrong contact number, which was instead connected to my friend’s mobile number.

Data quality issues are likely to happen during the data entry process. Sometimes the processes are not managed through validation and controls to ensure the data has been inserted into the system correctly and based on standard criteria. Another typical example of errors in data quality is when a company holds different accounts for the same person or client, and all transaction records relevant to that same person are scattered over those accounts.

The absence of standard criteria for data format, terminologies, and definitions involved in data entry creates a dilemma for data analytics projects. Advanced ETL (Extract, Transform, Load) engines can help the data preparation process find the issues and common attributes within the data that will mitigate the lack of standardization while also correcting the data to ensure its validity as a source before loading it to the next processing stages.

  1. Data Privacy

Telecom operators often deal with sensitive personal data about their clients. It includes e-mail addresses, bank account numbers, and dates of birth. These kinds of data must be managed by telecom operators with high-level privacy protection measures. This poses a complication to telecom operators for data analytics projects. Ethical and legal restrictions for critical personal data are a significant factors in understanding the processes for the type of data that can be used on analytics platforms. Implementing and using the proper analytics tools must protect the critical personal data types.

  1. Data Bias

One of the big issues in data analytics is when solutions are built around specific aspects of the business rather than building a solution based on the whole picture. This may form a type of confirmation bias in the data or what some call “Cherry-Picking.” Business acumen is needed to design an insightful solution that considers all aspects of the business. From a practical point of view, telecom operators must employ subject matter experts when developing specific analytics solutions or building a strong professional team to manage them.

In conclusion, data analytics has become a critical aspect of the telecom industry, enabling telecom operators to leverage the vast amounts of data generated by complex networks and connectivity setups to gain insights into business operations, customer behaviors, and market trends. However, telecom operators face significant challenges implementing data analytics projects, such as data complexity, quality, privacy, and bias. To overcome these challenges, telecom operators must adopt the right tools and techniques and build a skilled team of professionals to manage data analytics projects effectively. Ultimately, the successful implementation of data analytics projects will enable telecom operators to make informed decisions, drive business growth, and stay ahead of the competition.