The Ultimate Guide to Telecom Data 2021
What is Telecom Data?
Telecom data is growing at a rapid rate, all because of the deep penetration of mobile phones in our life.
Needless to say, each of these devices generate a large amount of data. Nevertheless, data has always been an integral part of the telecom industry. It is ingrained into its DNA – from the time you get a new connection to the time you put your phone down – telecom companies collect your data and derive crucial insights in a range of ways – mobile phone usage, mobile location, server logs, call detail records, network equipment, social networks, and various others.
Who uses Telecom Data and for what use cases?
Telecom data is reliably used both by telecommunication companies and other businesses to improve the quality of their services. The data thus obtained can be further enhanced and fed to machine learning algorithms and artificial intelligence technologies to derive critical insights.
Even traditionally, telecom data has always played a greater role in marketing and sales departments. Let’s not forget the CRM systems which make use of telecom data to help you nurture your leads.
Let’s have a look at a few use cases of telecom data:
A subsidiary of NTT Group, Solutionary, is using telecom data to offer managed security services consisting of Vulnerability Management, Threat Intelligence and the like. The company extracts the key hidden details by analyzing and correlating vast amounts of data from network devices, endpoints, firewalls, logs, and the like.
Telecom companies use telecom data to better their services and to outperform their competitors. They gather the data related to dropped calls, bandwidth issues, poor download times, and the like to optimize their services with proper capacity planning, equipment monitoring, and preventive maintenance.
Telecom data can also be used to discover hidden details about the customers – their demographic data, sentiment analysis of social media, calling circle data, browsing behavior data, historical data and more.
Demographic data can be used to target region-specific users while browsing behavior data can be used to target ads on social media platforms. Not only this, but telecom data can also help companies in predicting the user behaviors and preferences, thus helping businesses align their business strategy accordingly.
What are typical Telecom Data attributes?
Telecom data could have a range of attributes like:
- User demographic data
- Customer call logs
- Internet browsing history
- Sentiment analysis of social media
- Call history
- Texting pattern
…essentially almost anything that can be traced back to the devices using a connection provided by a network operator.
How is Telecom Data collected?
Depending on the kind of data collected, telecom data is extracted from different sources. Call records, for instance, are collected each time a call is made. Similarly, carrier data, network data, and routing information are collected through databases. LERG database, for instance, can be purchased from Telcordia and contains information on all telephone switches in North America and the phone numbers that they cover.
How to assess the quality of Telecom Data?
As with any data type, care should be taken that the telecom data that you are buying is accurate and reliable. It must come from a reputable source and should be fresh. Essentially, it must be in line with the recent GDPR requirements and should be available in a format that could be used by you and your tools.
To be on the safer side, we will highly recommend you have a deeper insight at the raw data collection methods used by your data vendor. You might also like to use sample sets provided by your data provider to check the data in its intended environment.
How Telecom Data is typically priced?
The pricing of telecom data depends on the quality of telecom data, and it also varied from telecom data provider to provider. Common pricing models we see are:
- Monthly subscriptions - give you access to freshly updated API’s
- One time payments for large batches that enable you to access historical data for making future predictions.
- Many providers are willing to also create custom quotes for more challenging use cases.
The number #1 challenge with Telecom Data?
Since telecom data majorly revolves around user data, the recent introduction of GDPR in Europe has made the accumulation of telecom data more difficult.. According to the law, you cannot collect the data pertaining to a user without their consent.
What to ask Telecom Data providers?
Here are a few questions that you may want to ask telecom data providers before finalizing the deal:
- How do you extract the telecom data?
- Is your data in line with the recent data laws?
- How do you ensure data freshness?
Where do Telecom companies get data from?
There are a range of primary telecom data base fields that providers can reach instantly: network data including user information such as Call Detail Records (CDRs), Performance Tracking Data, Failure Monitoring Data, Call Management Data, Customer Data, Service Support Systems and Business Support Systems. These types of data are usually standardized, and it is relatively straightforward to obtain information from sources in the data marketplace for data telecom analysis. Real-time telecom data retrieval and interpretation become more complex as new data sources are used, such as: device data which includes traffic analysis, such as deep packet inspection and SMS, site, search, and email. Social networking, smartphone apps and data devices customer profile data, demographics and segmentation data.
How does Big Data affect the Telecom industry?
Operators need to collect, archive and derive insights from their available data for real-time telecom data analysis. Big Data Analytics will help them maximize revenue through helping to manage network use and resources, enhance consumer engagement and boost security. Big Data addresses concerns about how data is used by a telecom company to maximize profitability and income across the supply chain, across network activity, product growth, promotions and distribution. Big Data Analytics allows businesses to forecast peak network demand so that they can take steps to minimize congestion. It will also help classify consumers who are more likely to pay for certain telecom services. You can purchase telecom data online from a variety of telecom data vendors or make a telecom data subscription in order to get telecom data at a fair price.
Do telecom companies provide CDR data?
Call Detail Record (CDR) is a comprehensive log of any telephone calls that pass into a telephone exchange or some other telecommunications facilities. The record is kept by the telecom companies which involve and includes call information such as call time, call length, source and destination number, call completion status, consumer billing, service capacity preparation - all of which can be accessed from some commercial telecom datasets. CDRs are commonly used for network tracking, traffic analysis, CABS reconciliation, fraud prevention, customer service, and facility capacity preparation.
• How is data structured in a typical telecom company?
The Telecom Data Structure consists of a group of nodes interconnected by connections used to maintain contact between nodes. Links can use a range of technologies based on circuit switching, message switching, or packet switching methods to relay messages and signals to commercial telecom datasets. For each message, multiple nodes can co-operate to transfer the message from the originating node to the destination node through multiple network hops. These organizations have developed an infrastructure that enables real-time telecom data in phrases, voice, audio or video to be delivered anywhere around the world. Telephone (wired and wireless) networks, satellite companies, cable providers and internet service providers are the biggest corporations in the telecommunications field.
What are tools for Telecom Data analytics?
The first data telecom analysis tool is Excel with a number of powerful features, such as form formation, PivotTable, and VBA. Excel is a versatile player. It fits well with tiny files, and it can accommodate millions of data with extensions. Second, BI methods such as Power BI, FineReport, and Tableau are developed according to the data analysis process, as well as data visualization that uses map presentation to define concerns and affect decision-making. Finally, the programming language of R and Python is very strong and scalable. R and Python are invaluable methods for the study of telecommunications data. They are undoubtedly powerful from a technical point of view than the Excel and BI tools. Telecom data providers use these methods for data business research.
How is Telecom Data used for machine learning?
Machine learning is about developing algorithms to derive useful information from commercial telecom datasets, focuses extensively on continuous use of rapidly evolving contexts, and stresses the adaptation, retraining and modification of algorithms based on prior practice in certain data marketplaces. The goal of machine learning in telecommunications is to continuously adapt to new telecommunications data and to discover new trends or rules therein. It may often be realized without human guidance and explicit reprogramming. Real-time telecommunications datasets include:
- Customer data containing all data relating to Customer Care and Contact Information
- Towers and Grievances Log
- Call Detail Records “CDRs”
- Mobile IMEI information containing a brand, model, type of mobile phone, and whether it is a dual or single SIM unit.
What does a Telecom Data model look like?
Telecom data model is a common industry data model applicable for fixed and mobile telecommunications providers, addressing both conventional Business Intelligence standards and Big Data Analytics. It is based on industry best practices, developed and applied during telecom data analysis in major telecoms companies. It is also open to appropriate changes and modifications needed by each customer. The first process model involves both fiber and copper networks, which describes relationships between the most dominant network telecom data. The telecom data model is expanded to depict point-to-multipoint networks as well as continuous wireless networks using Code-Division Multiple Access (CDMA), Global System for Mobile communications (GSM), and Time Division Multiple Access (TDMA) technologies.
Who are the best Telecom Data providers?
Finding the right Telecom Data provider for you really depends on your unique use case and data requirements, including budget and geographical coverage. Popular Telecom Data providers that you might want to buy Telecom Data from are start.io, Teragence, RootMetrics, Kochava Collective, and ThinkCX.
Where can I buy Telecom Data?
Data providers and vendors listed on Datarade sell Telecom Data products and samples. Popular Telecom Data products and datasets available on our platform are Raw Telecom Datasets Europe - Telecom Network Data by Teragence by Teragence, ThinkCX | Carrier and ISPs Telecom Market Share Data TeleBreakdown for North American by ThinkCX, and Comlinkdata: Broadband Carrier Vision - Telecom & Broadband Data for USA by Comlinkdata.
How can I get Telecom Data?
You can get Telecom Data via a range of delivery methods - the right one for you depends on your use case. For example, historical Telecom Data is usually available to download in bulk and delivered using an S3 bucket. On the other hand, if your use case is time-critical, you can buy real-time Telecom Data APIs, feeds and streams to download the most up-to-date intelligence.