BI and Big Data Analytics have undergone significant evolution and development over the past decade. Now they are no longer a novelty but a requirement for successful business strategy and operations. This shift is equally relevant in the telecommunications industry,where data analysis plays a pivotal role in enhancing customer satisfaction, optimizing networks, and detecting fraudulent activities.

Despite their extensive experience in data management, telecom companies continue to face challenges that, with growing data volumes, only continue to remain relevant. Today, we’ll delve into the tips and tricks for mitigating the BI and Analytics issues commonly faced by data-driven network and telecommunication providers.

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‍The Challenges

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Challenge#1 Data type variety and multiple data sources resulting in analytics complications

Telecommunication companies nowadays encompass all types of various sources: network systems, customer databases, billing systems, and external sources like social media. After collecting the data, they are posed with a challenge:

1. To integrate all the disparate sources in a single cohesive system;
2. To ensure the different data formats and structures can be combined and processed for further BI and Analytics.

Our tip #1 will be to implement a data lake  to store the raw data from various data sources in a single place in the native format. Building a data lake makes certain that telecommunication companies will be able to scale and accommodate their data growth in the future. Data standardization, on the other hand, will ensure consistency in the data formats, while a golden record (by implementing a Master Data Management system) will update the new incoming information to avoid duplication and contradictions.

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Challenge #2 Poor data quality resulting in inaccurate analysis and BI

Due to the variety of data sources, data discrepancies and inconsistencies can be encountered in every company dealing with hundreds of thousands (or even millions) of clients. Duplicate client entries, faulty billing entries, and incomplete call detail records will result in misleading and nonrepresentational analytics. In such a situation, the challenges here will be:

1. To identify the root causes of poor data and sources with the most inaccuracies;
2. To implement cleansing tools, create a golden record, and automate data quality management for regular data monitoring.

Our tip #2 will be to leverage cleaning tools, such as Apache Spark, to standardize formats, correct errors, and fill in missing values. Implementation of a Master Data Management system (MDM) will ensure the timely update and consistency of information, while usage of data quality management tools (like Blitz DMS Data Quality Management module) will guarantee real-time monitoring and data anomaly detection.

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Challenge #3 Poor real-time data processing speed resulting in unsatisfactory real-time analytic capabilities

Slow real-time data processing speed can become a serious hindrance to telecom operations. In serious cases, it can cause a delay in the detection and resolution of network issues, failure to prevent call and billing frauds, and incomplete analytics with compromised decision-making. The solution here will be:

1. To analyze infrastructure and equipment for bottlenecks, consider alternative data storage solutions;
2. To streamline extract, transform, load (ETL) processes for real-time data flow and implement data compression;
3. To ensure system scalability to large data volumes.

Our tip #3 will be to analyze the solutions at use, as not all operate suitably to the requirements and tasks of the organization. For example, monolithic applications can become bottlenecks by not being able to scale specific components independently, where changes to the system might bring about potential downtime and slow responses. At the same time, database management systems like Greenplum will excel in complex queries and timely large-scale analytics, and NoSQL databases will provide for handling large volumes of data with high availability and performance.

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Challenge #4 Analytical model complexity and challenge to maintain them

With analytics becoming more complex and intricate, offering more accurate insights and predictions, their support and maintenance become a constant challenge. Failure to provide the correct quality data,scalability, computational resources, and oversights in performance monitoring might lead to faulty analytics, wrong conclusions, and thus, wrong strategic planning. To combat the challenge, telecommunication companies should:

1. Provide tools and resources for keeping data quality high and scalability up to the expected workloads and data growth (as was mentioned in the previous challenges);
2. Monitor and adapt analytical models to the changing environments.

Our tip #4 is to track key performance metrics, detect model drifts or degradations, and retrain the model periodically to adapt to changing data patterns. For example, customer behaviors and churn may change based on the season (in summer during peak traveling season) or after the expiration of promotional offers. Without considering such factors, the model’s analytics will be inaccurate and lead to misleading conclusions.

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Challenge #5 The complexity of self-service analytic tools and their adoption

While self-service analytics is currently on the rise with companies adopting it more than ever, its operational complexity remains an obstacle to hassle-free use. The minority that can utilize them becomes the single source of analytics, thus making other departments adapt and hinder their processes due to the long delays in analysis. How to avoid such pitfalls when adopting the approach?

1. Provide user-training sessions and teaching manuals to employees adopting self-service analytics;
2. Monitor adoption, continue advanced functionality training;
3. Choose the right tool that will suit both the business requirements and be compatible with existing systems.

Our tip #5 is to ensure the support of the tech team after the implementation to make the transition to self-service analytics smoother for employees. So, in case of difficulties, they will have a dedicated support team to turn to. At the same time, the tool should both accommodate the business needs and be user-friendly: Tableau and Power BI are usually considered a good choice, while, for example, SAS and other extensive analytics software are mostly designed for data scientists trained to work with them and often requiring advanced proficiency to operate.

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Use Cases

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How have been these challenges solved by other telecommunication companies? Have a look at these use cases:

№1 Tackling data quality issues with MDM implementation

A Forbes 2000 telecommunication company was experiencing poor analytics and reporting due to highly decentralized and complex data systems. The plan to tackle these issues was by integrating and merging data, as well as creating a golden record for keeping the data consistent and up-to-date.

In the end, by implementing an MDM solution, the telecommunication company found that due to inaccuracies and disorganization, 4% of billed contracts were under billed, resulting in $50 million/year of lost revenue. With designed golden record and improved data quality, the quality of the reporting and analytics increased as well.

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№2 Improving slow data processing speed and delayed analytics

Our client - an Eastern European telecommunication company - was no longer able to process the large bulks of information in time. The daily collection and processing of information took over 16 hours, which was not sufficient for smooth operations and timely analytics.

To solve the issue, our team was tasked with designing and implementing a new BI platform that would be able to receive operational data in a shorter time frame. Additionally, the team proposed the client migrate from the old corporate data warehouse to the Greenplum distributed database as a more scalable and quick alternative.

As a result, the time spent on data collection and processing decreased from 16 to just 2 hours, improving the efficiency and timeliness of operations within the company.

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Conclusion

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Even from our experience, the Big Data Analytics and BI challenges mentioned above, although widely known, still remain an issue for many growing telecommunication companies. Increasing client base numbers, diversification of data sources, and new demands for advanced analytics, - all require continuous developments and transformations of the data management systems. That’s why we hope our tips will prepare what to expect in advance or will be able to help in case of already existing problems.

Still, even in advanced telecoms, there is always room for improvement. With BlitzBrain Data Analytics and Business Intelligence solutions, we offer:

1. A unified Data Intelligence platform for analytics and reporting needs;
2. Historical data analysis and process optimization;
3. Data sources integration and in-depth data relationship analysis.

Contact us to find out how we can help you implement BI and Analytics solutions that will suit your needs and goals.

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