Modern data architecture CSP survey 2025

Communications service providers’ (CSPs’) rapidly increasing use of AI makes “democratization” of data critical as teams building AI use cases across the business need access to many sources of structured and unstructured data. Many operators are realizing that unless they start the process of auditing, reviewing and in some cases overhauling their data architectures they will never truly exploit the potential of AI.
In this report we explore the current state of CSPs’ data organizations, their approaches to data storage, optimization and orchestration, and the data governance programs they have in place. We also seek to identify best practices and the efforts underway to transition to a modern data architecture. Please note that survey results will only be presented in aggregate, and individual responses will be kept strictly confidential and anonymous. If you are interested in participating in a follow-up interview with the author, you will have an opportunity to indicate this in the survey.
Dawn Bushaus
Contributing Analyst, TM Forum

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* 1. Please provide your contact details (note that this is for TM Forum verification purposes only – results will only be presented in aggregate and individual responses will be kept anonymous).

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* 2. What is your organization’s primary region of operations? (Choose one)

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* 3. How many customers are served by your organization?

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* 4. What department do you work in?

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* 5. Would you be willing to speak with an analyst about your answers to this survey?

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* 6. Who is responsible for data architecture in your organization? (choose one)

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* 7. From a business perspective which of the following approaches most closely matches your organization? (choose one)

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* 8. How would you rate your organization's current level of data democratization? (Choose one)

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* 9. Which sources of data are most valuable to your organization?

  Very valuable Somewhat valuable Not very valuable
Data from digital channels (e.g., website, mobile app, chatbot)
Direct network data (e.g., collecting data from network elements, network gateways or network probes)
Location data
Indirect network/OSS data
Billing data (e.g., CDRs, recharge records, customer invoice history, non-billable records, interconnect, roaming, etc.)
Unstructured data from customers (e.g., comments on social media)
Other unstructured data

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* 10. What are the key drivers forcing an assessment of/changes to your current data architecture?

  Major driver Moderately important driver Not a major driver
We are refactoring our data architecture to be AI-native
We want to reduce operating costs through better data management and automation
We need to unify data to accelerate innovation and provide new services to meet customers’ expectations of personalized and seamless experiences
We need to change our data architecture so that it’s easier to participate in digital ecosystems
We need to ensure compliance with regulatory requirements across our markets
We need to address challenges to combining real-time or near-time technical data from the network with customer experience data
We are facing delays in data processing and retrieval that affect real-time analytics and decision-making
We need to improve protection of data from unauthorized access, breaches and other security threats
We are struggling to cope with an increasing volume of data (e.g., data from internal systems and from customers’ services and devices)

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* 11. How significantly is the softwarization of networks and the move towards AI-driven, intent-based networks reshaping your data architecture? (choose one)

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* 12. What percentage of your data resides/will reside in the public cloud? (choose one in each of two columns: Now and In 3 years)

  Now In 3 years
100%
75% or more
25-50%
Less than 25%
We don’t intend to put any of our data in the public cloud

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* 13. Which approaches to data storage and management are you investing in?

  We are increasing our investment Our investment is flat We are reducing our investment
Data warehouse (stores structured data from various sources for business intelligence and analytics)
Data lake (stores large amounts of raw data in its native format, including structured, semi-structured, and unstructured data)
Data lakehouse (combines the features of data lakes and data warehouses)
Data fabric (an architecture that provides a unified data management framework across various environments, such as cloud, on-premises, and edge)
Data mesh (decentralized approach to data management where data ownership is distributed across different business domains)
Data puddles (smaller, localized data storage solutions within an organization)
Data vault (a data modeling methodology that provides a scalable and flexible way to store historical data)
Data hub (a central repository that integrates data from various sources and makes it available for different applications and users)

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* 14. What enterprise data governance practices do you have in place? (choose all that apply)

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* 15. How do you address data security and privacy in your data architecture? (choose all that apply)

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* 16. Is there is a need for a standard telco industry data model that supports the implementation of end-to-end, data- and AI-driven business processes? (choose one)