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7 min read

Top 5 Data Management Challenges that Threaten the Telecom Sector

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The telecommunications industry is working hard to harness the latest technology to deliver cutting-edge digital services to customers.

A new era in the telecommunications industry is emerging that will be powered by AI, machine learning, IoT, and other "smart" technologies in what's being referred to as the "Machine Economy".  

These smart technologies are already permeating all aspects of the telecommunications sector. For example: 

  • Smart Networks: The self-optimizing capabilities of "smart" networks (powered by AI and machine learning algorithms) enable telecommunications providers to preempt network performance issues, optimize network quality, ease congestion, reduce latency, and avoid service downtime. 
  • Data Analytics: Data analytics provides telecoms with deeper insights into customer behavior to develop more personalized services and improve the end-user experience. 
  • Internet of Things (IoT): IoT networks are creating new business models (smarter factories, cities, and homes), and enabling predictive maintenance of physical assets. 
  • Smart Automation: Automated customer support tools, such chatbots and intelligent digital assistants, are enabling telecommunications providers to offer instant, personalized real-time support to their customers. 

Top 5 data management challenges that threaten the telecommunications sector 

As the Machine Economy matures, telecommunications providers will continue to seek out new ways to use smart technologies to increase efficiency and improve service delivery.  

Unfortunately, the volume and variety of data required by these new smart technologies is stretching traditional data management infrastructures beyond their limits. Without an adequate solution, these 5 data management challenges threaten to stifle innovation and put the future of the industry at risk: 

  1. Exploding data volumes

Organizations are experiencing an explosion in both the amount and the types of data that need to be collected and stored from a growing number of sources. 

These data sources include data generated from new "smart" technologies (machine learning algorithms, IoT networks, etc.), data shared across the extended network of partners and ecosystem participants, data created through social media interactions with customers, data generated through transactions, and much more. 

New data types and formats are also being generated, such as data from sensors, data generated using AI/machine learning algorithms, data stored in video formats, data contained within 3D models, data containing location-based information, etc. 

In traditional data management environments, data is stored in a series of isolated data silos that are typically accessed and used independently from one another. Adding data from new data sources or data types to existing data silos leads to data duplication, data quality issues, and data integration challenges. Data silos also prevent data from being shared across the extended network of partners and ecosystem participants, thus limiting data reuse. 

The result? Businesses are spending a lot of time and money trying to track down all their data sources. This slows business processes and data insights, increases data storage costs, and undermines competitiveness. 

  1. Time-consuming data preparation tasks

Data scientists still report spending around 45% of their time just on data preparation tasks. Data preparation is an extremely tedious and time-consuming and costly process that often involves: 

  • Data discovery to identify data sources 
  • Data profiling to determine data attributes and data quality 
  • Data ingestion into data warehouses or data lakes using complex ETL processes  
  • Data cleansing to deal with data errors such as missing data, wrong data, or illogical data 
  • Data enrichment to fill in missing information such as location or date 
  • Data transformation to convert data from one format to another 

Data preparation tasks are labor-intensive, error-prone, and data quality is often inadequate. Data accuracy can be compromised, data lineage is often unavailable, and data often does not reflect the latest version of data objects in production systems. 

Increasing data volumes will only serve to exacerbate data preparation challenges, as data scientists will be forced to wade through more data than ever before just to extract the most useful data insights. 

  1. Talent shortages

This assumes you already have a large data team of highly-specialized professionals who can do all this work. Demand for data science skills is outstripping supply, leaving many companies struggling to find enough talent.  

Data engineering skills shortages are also starting to emerge. Data engineers are responsible for designing data pipelines and data architectures that can handle complex data management processes.  

The lack of data engineers means that overworked data scientists end up spending more time on data engineering tasks than they would like—and this slows down the rate at which they can extract valuable insights from their data. 

The data science and engineering skills gap is widening due to several factors: 

  • Data skills are in high demand and data professionals can command increasingly high salaries 
  • Data science and data engineering are relatively new disciplines, so the data talent pool is still small 
  • Data scientists and data engineers require a diverse set of technical skills that aren't always easy to find in a single data professional 
  • The data preparation process itself is labor-intensive and requires a lot of patience and attention to detail, skills that can take years to develop 
  1. Data access and security

With growing data volumes comes growing concerns over data security and privacy. Security is a top concern for companies that are collecting large amounts of data about their customers. Customers are now more aware than ever of data breaches, identity theft, cybercrime, and the use of data without their knowledge or consent.  

In an effort to increase data accessibility while still maintaining data protection standards, data management teams have to strike a careful data balancing act between data sharing and data security. Data management teams must ensure data availability while still protecting data from unauthorized access, which is no easy feat given the rising volume of data and an increasingly complex data landscape. 

The increasing amount of data that telecommunications companies are collecting poses a serious challenge to how it is managed. Many organizations may not even know where they store their data or if it's safe from unauthorized access. With so much data constantly changing—becoming more complex, diverse, and scattered across multiple devices—telecommunications organizations need a data management strategy that is designed to keep data safe, accurate, and readily accessible. 

  1. Regulatory compliance

The data management team has to maintain data lineage, data quality, data availability, data security, and data compliance—all while meeting an ever-growing list of data regulations.  

The data management team can't afford to ignore data compliance issues, either. Telecom companies must be able to provide data upon request for audits and investigations into data breaches and other data security incidents.  

With data volumes continuing to explode and the regulatory landscape becoming more complex, data management teams will need to find data management tools that are not only scalable, but also flexible enough to adapt to data compliance requirements. 

Data management is dead 

Traditionally, the data preparation process has relied on a highly-complex stack of tools, a growing list of data sources and systems, and months spent hand-coding each piece together to form fragile data “pipelines”.   

Then came data management “platforms” that promised to reduce complexity by combining everything into a single, unified, end-to-end solution. In reality, these platforms impose strict controls and lock you into a proprietary ecosystem that won’t allow you to truly own, store, or move your own data.   

It’s clear that the old ways of doing data management simply cannot meet the needs of modern data teams in the Machine Economy.   

Data professionals are in desperate need of a faster, smarter, more flexible way to build and manage their data estates.   

Meet TimeXtender, the low-code data estate builder.   

TimeXtender empowers you to build a modern data estate 10x faster with a simple, drag-and-drop solution for data ingestion and preparation.   

Code and documentation are generated automatically, which reduces build costs by 70%, frees data teams from manual, repetitive tasks, and empowers BI and analytics experts to easily create their own data products.   

TimeXtender seamlessly overlays your data infrastructure, connects to 250+ data sources, and integrates all the powerful data preparation capabilities you need into a low-code, agile, future-proof solution.   

We do this for one simple reason: because time matters.   

How TimeXtender Accelerates Your Journey to Data Empowerment 

Low-Code Simplicity: TimeXtender empowers you to build a modern data estate 10x faster with a simple, drag-and-drop solution for data ingestion and preparation. Our data estate builder automatically generates all code and documentation, which reduces build costs by 70%, frees data teams from manual, repetitive tasks, and empowers BI and analytics experts to easily create their own data products.  

Don’t worry! Powerful developer tools, SQL scripting, and custom coding capabilities are still available, if needed.  

Agile Integration: TimeXtender seamlessly overlays your data infrastructure, connects to 250+ data sources, and integrates all the powerful data preparation capabilities you need into a low-code, agile, unified solution. This eliminates the need for complex stacks of tools, lengthy implementations, and costly disruptions, while still giving you complete control over how your data is stored and deployed.  

Future-Proof Scalability: Because TimeXtender is independent from data sources, storage services, and visualization tools, you can eliminate solution lock-in, and ensure your data infrastructure is highly-scalable to meet future analytics demands. With TimeXtender, you can quickly adopt new technologies and deployment models, prep data for AI and machine learning, and migrate to cloud platforms with one click.  

Smarter Pipelines: When unexpected changes occur, fragile pipelines can easily break and must be manually debugged. With our metadata-based approach, whenever a change in your data sources or systems is made, you can instantly propagate those changes across the entire pipeline with just a few clicks. In addition, TimeXtender provides built-in data quality rules, alerts, and impact analysis, while leveraging machine learning to power our Intelligent Execution Engine and Performance Recommendations.  

Self-Service Analytics: Our low-code technology, drag-and-drop interface, and Semantic Layer allow for fast creation and modification of data products, without requiring extensive data engineering knowledge. These data products can be created by BI and analytics experts once, and then be deployed to multiple visualization tools (such as Power BI, Qlik, or Tableau) to quickly generate graphs, dashboards, and reports.  

Holistic Governance: Our metadata-based approach allows neat organization of your data projects, while providing end-to-end documentation, data lineage visualization, and version control across multiple environments. By using metadata to drive the model and deploy the code, TimeXtender never requires any access or control over your actual data, eliminating security vulnerabilities and compliance issues, while giving you a holistic view of what is happening across your entire data estate.  

Enterprise-Grade Security: TimeXtender’s security features allow you to provide users with access to sensitive data, while maintaining data security and quality. You can easily create database roles, and then restrict access to specific views, schemas, tables, and columns (object-level permissions), or specific data in a table (data-level permissions). Our security design approach allows you to create one security model and reuse it on any number of tables.  

Trust and Support: As a Microsoft Gold-Certified Partner, we have 15+ years of experience building modern data estates for over 3,300 organizations with an unprecedented 97% retention rate. When you choose TimeXtender, one of our hand-selected partners will get you set up quickly and help you develop a data strategy that maximizes results, with ongoing support from our Customer Success and Solution Specialist Teams. We also provide an online academy, comprehensive certifications, and weekly blog articles to help your whole team stay ahead of the curve as you grow.  

Learn how to become Data Empowered with TimeXtender    

Book a demo to learn how we can help you build a modern data estate 10x faster, become data empowered, and win in the Machine Economy. 

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