Data Center


Subscriber-centric data architecture for telecoms

The telecom industry is currently undergoing an architectural change with the introduction of Network Function Virtualization and the service-based 5G architecture. Telecom operators can secure their long-term competitiveness through a stringent customer focus, which is reflected in a robust data center architecture.

This includes consolidating distributed data from disparate systems through a unified data model and advanced data center architectures to:

  • Provide access to mission critical data in real-time centrally
  • Distribute data fault-tolerant and efficiently, avoiding inconsistent data
  • Increase state resilience, thereby minimizing service outages and avoiding traffic storms


Get more information on One-NDS here.

Subscriber Data

Due to the changed consumer behavior, it is even more important to implement a subscriber-centered data architecture.

Many companies store subscriber data redundantly in different systems from different manufacturers. Subscriber Data Management provides a consolidated view of subscriber data so network operators can more quickly introduce new, customer-relevant services and applications to the marketplace.

In essence, subscriber-centric data architectures can be implemented with directories (e.g., one-NDS) or relational databases (e.g., Oracle or MySQL). The most efficient way to consolidate subscriber data is through repositories / directories.

Moreover, telecom companies are seeing an unprecedented rise in volume and variety of data.

To gain insights and make the right investment decision, telecom operators need to address the big data challenge consolidating data distributed across systems in a disparate IT infrastructure.

SilverEngine helps their clients design and implement projects aimed at resolving the big data challenge.

We design and implement subscriber-centric data center architectures and big data projects to help our customers improve customer experience

Our approach -
sustainable & proven

To effectively implement a subscriber-centric data center solution, the current IT landscape including the corresponding data structure and data flow across the systems is analyzed.


  1. 1 As-is analysis

    Analyze current system architecture (applications and databases), data structure, data flow and redundancies as well as evaluate and prioritize uses case

  2. 2 To-be data center architecture

    Define a robust, scalable and integrated data center architecture

  3. 3 Design transformation

    Evaluate different approaches for transformation and migration as well as develop a staged implementation plan

  4. 4 Implementation

    Migrate subscriber data using state-of-the-art tools as well as lead and provide technical support

Your benefits

  • Better customer service

    Improved customer service and thus, better customer experience

  • Time-to-market

    Faster implementation and promotion of new services and applications

  • Higher loyalty

    Increase customer satisfacton and respectively, loyalty of users

  • OPEX-optimization

    Improved performance and scalability as well as reduced OPEX

Our capabilities

Our expert consultants combine expertise in state-of-the-art network technologies with long-standing experience as practitioner in the implementation of a subscriber-centric data center architecture. We have been working at the forefront of subscriber-centric data center architectures and were one of the first to successfully implement the concept of One-NDS.

  1. Comprehensive IT knowledge

    • Knowledge of telecommunication protocols, TCP / IP protocols, application protocols
    • Security-related knowledge
    • Know-how related to virtualization technology
    • Database knowledge (e.g. directories, SQL, noSQL)

  2. Longstanding practical experience

    • Substantial experience in design and implementation of solutions for central subscriber data management
    • Extensive experience in implementing highly distributed and scalable data center architectures
    • Successful implementation of "big data" projects using Hadoop, Hbase, Hive or Storm

  3. Methodological competencies

    • Analysis of system architectures and enterprise modeling
    • Management of software development projects
    • Experience in applying different methods for software development (e.g. Scrum, V-model, X-treme programming, agile methods)

Whitepaper & Product Sheets

  • Self-learning Prioritization of End-User Services

    Self-learning prioritization of requests reduces the time and the complexity to restore end customer service to all users during signaling storms.… more


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