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Data processing: Does Analytics only begin after the ETL process has ended?

In today’s world, big data has become an essential asset for businesses, including Telcos. They say data makes the world go round, and they are not wrong, but for data to be genuinely beneficial the Extract, Transform and Loading (ETL) processes need to be accurate, relevant and consider all the necessary information. This is integral to the data processing process and critical to gaining Advanced Analytics that will add value to your business.

Not all data processing is equal. Simply loading data into a generic data processing system, without giving serious consideration to the ETL process, will not yield the best insights and will fall short of transforming complex data into meaningful, actionable information. Making sure your Advanced Analytics solution follows the right stages and best practices is essential. In this piece, we examine these in more detail.

data processing

What does data processing mean?

Data processing is the process of collecting data in its raw form and then transforming it into valuable, usable, and understandable information and insights. Within data processing two important stages exist. The first is ETL, which means Extract, Transform and Loading, whilst the second stage is the actual Advanced Analytics, and this is where all the magic happens and where the data is analyzed. In the earlier years, these stages were performed by data scientists or analysts, but more recently, technology, like machine learning and artificial intelligence, has been utilised to manage this process more efficiently and effectively.

Understanding the ETL 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 includes network transactional data, which includes voice, data usage, SMS and roaming Call Detail Records (CDR’s) etc., as well as 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 as well as 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. 
  • Loading – during the loading phase of this process, the data that has been extracted and transformed is then loaded into a detailed data warehouse, which is also known as a 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.

Advanced Analytics

From ETL to Advanced Analytics

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. 
  • 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 presentation stage will showcase the meaningful and actionable insights collected from the data in the form of Advanced Analytics.

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. This information will allow you to tailor and customise solutions related to specific customer and end-user needs. These insights also outline business performance, highlight opportunities for improvement, facilitate informed decision-making, and ultimately improve revenue and profitability.

CDRlive Adapt IT technology

Let’s look at what sets CDRlive Adapt IT technology apart from other Data Processing and Advanced Analytics solutions

This 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.

CDRlive AIT Technology

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. In order 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 prior to its deployment.

Adapt IT Technology

A robust Data Processing solution should allow you to: 

  • Breakdown data silos and ensure that the ETL process 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 customers 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. 

To learn more on 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: https://telecoms.adaptit.tech/contact-us/ 

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