With digital transformation impacting every aspect of businesses today, the significance of big data and its value in delivering valuable and actionable insights cannot be overstated. The saying that “data makes the world go round” holds true. However, to truly benefit businesses, the Extract, Transform, and Load (ETL) processes must be precise, accurate, relevant, and encompass all essential information. This data processing process is fundamental and vital for attaining Advanced Analytics that will enhance your business operations and business decisions.
It is important to note that not all data processing methods are equal. Merely inputting data into a standard processing system without properly considering the Extract, Transform, Load (ETL) process will not result in the delivery of valuable insights and will fail to convert intricate data into significant and practical information. Ensuring that your Advanced Analytics solution follows the appropriate stages and adheres to best practices is crucial. This article delves deeper into these aspects.
What does data processing mean?
Data processing involves gathering raw data and converting it into valuable and comprehensible information. There are two key stages in data processing. The first stage is known as ETL (Extract, Transform, and Load), which involves extracting the data, transforming it, and loading it into a database. The second stage is Advanced Analytics, where the data is analysed and insights are generated. In the past, these stages were typically carried out by data scientists or analysts. However, with recent advancements in technology, such as machine learning and artificial intelligence, these processes can now be managed more efficiently and effectively.
Understanding the ETL to Advanced Analytics Process
Data processing is a cycle and process that is split up into three phases, often referred to as the Extract, Transform and Loading (ETL) process.
- Extract – During the extract phase of data processing, the raw data that the network generates and collects is extracted and passed on to the service provider to be used for processing and analytics. This data collection includes network transactional data, which includes voice, data usage, SMS and roaming Call Detail Records (CDRs), and non-network reference data related to OCS, recharging, billing, CRM, device information, and more.
- Transform – During the transform stage of the data processing cycle, the raw data provided by the network is transformed into usable, accurate, audited and relevant data. First, any binary (machine-generated) data is converted into readable text. During this phase, metadata is incorporated into the stream and number standardisation and number portability lookups occur to enhance the use-ability and accuracy of the data. This stage is also often referred to as data cleaning and involves the verification of data. It is the process of sorting and filtering the raw data to only include necessary and accurate quality data. The raw data is then analysed and checked for errors, duplications, miscalculations, and more. This is an important stage as these data sets can create misleading results if not prepared and screened correctly.Â
- Loading – During the loading phase of this process, the data that has been extracted and transformed is then loaded into a detailed data warehouse, or landed in a big data lake.
The ETL process forms the backbone of all analytics and insights as it gives the data form and context. This process ensures accuracy and data quality. Data processing does not stop at ETL. We examine the process of accessing Advanced Analytics after the ETL process below.
From ETL to Advanced Analytics
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So you may be asking what happens after the ETL process and how do we get to the endpoint of gaining valuable and insightful Advanced Analytics? This links to the two final stages of the data processing cycle, which include
- Aggregation of Data – During this stage of the cycle, the raw data is subjected to various technical manipulations, summaries and data models that utilise machine learning and artificial algorithms to generate an output or interpretation about the data. This step can often vary depending on the source of data being processed such as data lakes, online databases, a data warehouse, connected devices, etc., and the intended use of the output.Â
- Data Output, Visualisation, and Interpretation – In this stage, the different types of data are transmitted and displayed in a readable format including dashboards, reports, graphs, tables, videos, documents, and more. This data visualisation stage will showcase the meaningful and actionable insights collected from the data in the form of Advanced Analytics.
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Once each of these stages is complete, you can gain access to Advanced Analytics which provide you with informative and valuable insights related to your Telco business processes, as well as the various Telcos-specific KPIs available through the data modeling process. This information will allow you to tailor and customise solutions related to specific customer and end-user needs due to data management. These insights also outline business performance, highlight opportunities for improvement, facilitate informed decision-making, and ultimately improve revenue and profitability.
The Benefits of the ETL Process and Advanced Analytics for Telcos
The ETL (Extract, Transform, Load) process and Advanced Analytics play crucial roles in the Telecommunications industry, offering a range of benefits. Here are some key advantages:
ETL Process:
- Data Integration – ETL processes allow Telcos to integrate data from diverse sources, such as customer databases, network logs, and service usage records. This integration provides a unified view of the data, enabling better decision-making.
- Data Quality Improvement – ETL processes often involve data cleansing and validation steps. This ensures that the data used for analytics is accurate and reliable, leading to improved decision-making and reduced errors.
- Efficient Data Warehousing – ETL processes facilitate the movement of data from operational systems to a data warehouse, making it easier to manage and analyse large volumes of data. This improves overall data governance and accessibility.
- Performance Optimisation – ETL processes can transform and optimise data for better performance in analytics systems. This is crucial in the telecommunications industry, where large datasets are common, and quick access to information is essential.
- Historical Data Analysis – ETL processes enable the storage and analysis of historical data. This is valuable for trend analysis, identifying patterns, understanding long-term customer behaviour, and helping in strategic planning.
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Advanced Analytics:
- Predictive Analytics – Telcos can use Advanced Analytics to predict future trends, customer churn, and network performance. This helps in proactive decision-making and resource planning.
- Customer Segmentation – Advanced Analytics allows for the segmentation of customers based on their behaviour, preferences, and usage patterns. This helps in targeted marketing, personalised services, and customer retention strategies.
- Fraud Detection – Telcos face challenges related to fraud. Advanced Analytics can identify unusual patterns in call records or usage, helping to detect fraudulent activities and securing the network early.
- Network Optimisation – Analytics can be used to optimise network performance by analysing data on traffic patterns, usage peaks, and network bottlenecks. This leads to improved efficiency, better quality of service, and cost savings.
- Operational Efficiency – Advanced Analytics can optimise various operational aspects, such as workforce management, resource allocation, and equipment maintenance. This results in improved efficiency and reduced operating costs.
- Regulatory Compliance – Advanced Analytics can assist in ensuring compliance with Telecommunications regulations by monitoring and reporting on various parameters, helping Telcos avoid legal issues.
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In summary, the ETL process and Advanced Analytics are integral to the Telecommunications industry, providing the means to manage and derive valuable insights from this sector’s vast amount of data effectively. These processes improve decision-making, operational efficiency, and overall business performance.
Why Adapt IT Telecoms CDRlive technology is setting a new standard for Data Processing and Advanced Analytics solutions
Adapt IT Telecoms CDRlive technology takes the heavy lifting out of ensuring that your Data Processing cycle, known as ETL, is efficient and accurate. This technology pulls the correct, network provided, raw data from suitable sources at the right time, allowing you to gain valuable insight into all elements related to your business. This technology utilises innovative automation software and programs to ensure the ETL cycle and data analysis are accurate, quick, and efficient.
CDRlive has several differentiators that set it aside from other solutions within the industry. You know the saying not all solutions are created equal, and this is the case with many Advanced Analytics solutions on the market today. These solutions often just pull data from various sources and load this into a data warehouse without ensuring accuracy and relevance. The result is that many Telcos are not getting the correct, relevant or accurate insights that they need, which can be detrimental to a business.
Key differentiators are:
- It does not restrict the types of files that can be Extracted, Transformed, and Loaded.
- Provides almost real-time aggregation of data and alerts, allowing you to know if there is a problem related to dropped calls, airtime top-ups and more.
- Auditing and logging traceability solutions that enable complete and accurate data reporting.
- Ability to customise analytics models, summaries and templates with the user-defined framework.
- Enables large amounts of data to be processed and analysed daily in almost real-time For example, at one of our largest customers, more than 25 million subscribers are maintained. To load 1.6 billion new records per day, a 117 TB solution is required. This particular solution supports over 3500 business users, maintaining a 24×7 availability.
A critical factor in choosing the right Data Processing solution for your company is ensuring it aligns with business objectives. When looking for a solution that has a comprehensive ETL process and is suitable to your environment, ensure it allows the following before its deployment.
A robust Data Processing solution should allow you to:Â
- Breakdown data silos and ensure that the ETL process is complete and holistic.
- Access relevant analytics in almost real-time that is very simple to interpret, improving the availability of vital business insights and insight into operational inefficiencies that impact business.
- Quickly deploy executive, operational, data quality, and data health management KPIs across verticals and horizontals of your Telco business.
- Improve your reporting capabilities by pulling reports on your customer’s life-cycle, acquisition and retention, spend, profitability, and other drivers that influence revenue.
- Access valuable information relating to network revenue contributors, churn management, KPIs, spend and more.
- Create a 360-degree profile of your customer segments, their user patterns and much more. Having this data helps you better target your products and services.
- Gain operational visibility of key growth drivers, including subscriber growth, revenue and profitability ratios).
- Make the most of the information and insight gained from the data processing cycle. Create cost-effective product and service strategies that satisfy your customers’ needs and requirements and increase profit margins.
- Make informed revenue and churn management decisions relating to pricing, marketing strategies, new service innovations and more.
Conclusion
You already know that big data is an essential asset for any business. Many industries loosely use the term Advanced Analytics, but its implementation and its use are not easily understood. Extract, Transform and Loading (ETL) is a crucial stage in the data process if data is to be genuinely beneficial and meaningful. Essentially, it separates the analysis part from everything else in the process and comes ahead of the actual analytics.Â
To learn more about how Advanced Analytics and data processing impact Telcos, take a look at our CDRlive White Paper. If you want to know more about Advanced Analytics and Data Processing, one of our experts would be delighted to answer your questions. You can contact us today.
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As the Product Manager of Advanced Analytics within the Adapt IT Telecoms division, I bring 16 years of Telecommunications expertise to the table. Over the past 8 years, my focus has been on Product Development. My responsibilities encompass identifying customer needs, monitoring industry trends, and driving our Advanced Analytics strategy. I’m deeply passionate about leveraging big data for analytical insights and product evolution through machine learning and AI. My experience extends to Mobile Network Events, Mobile Financial Services, and supplementary services.
