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From data lake to data mesh: An innovative architecture every telco should know about

From data lake to data mesh: An innovative architecture every telco should know about

The telco world is awash with data. Floods of it. This won’t come as any surprise to Data Managers or IT executives working for Communications Service Providers (CSPs). Many are already struggling to manage and make sense of the rapidly rising variety, velocity and volume of data sweeping across their networks.  

There is huge value tucked away in all that data. Yet, extracting useful insights from the data silos is a major headache, especially when you consider the chronic shortage of skilled data scientists and the need for real-time insights to support advanced 5G services.

Advanced and automated data analytics powered by Artificial Intelligence and Machine Learning (AI/ML) is the only way to meet the challenge.

Data lakes often sink under oceans of data

However, to truly work their magic AI/ML technologies need a data framework that not only addresses the size and complexity of telco data, but ensures real-time data gathering, fragmented data handling, distributed data computation and the convergence of data from multiple locations and business units.

The conventional approach is to deploy data lakes that store information centrally. While data lakes work well with batch-oriented workloads, their monolithic storage is not well suited to low latency, highly demanding, real-time business applications. Furthermore, data lakes are hard to scale. Some CSPs add resources to stretch legacy data capacity as demand grows, but eventually a breaking point will be reached with a backlogged data team struggling to keep pace with the demands of the business.

A more modern and agile approach is the data mesh which enables greater autonomy and flexibility for data owners and consumers. Unlike data lakes that manage data in one central place, a data mesh supports distributed, domain-specific data consumers, with each domain handling its own data pipelines.  

This decentralized approach creates data products - basic units of consumption that are searchable and addressable through Application Programming Interfaces (APIs). This and other attributes enable a data mesh to serve a wide range of users and enables better collaboration between data producers and data analytics business groups.

A framework to accelerate innovation

The data mesh approach underpins the Nokia AVA Open Analytics framework that delivers real-time insights and ad-hoc reporting to support intelligent and automated decision making, as well as new business models for CSPs.

This state-of-the-art analytics framework helps CSPs to secure, automate and monetize data. Based on open-source technologies, AVA Open Analytics Framework offers a library of proven, valuable analytics use cases, such as anomaly detection, energy savings and more. It also enables CSPs to create their own use cases.  

In short, AVA Open Analytics Framework provides a unified approach to analytics, accelerating innovation and the creation of new value for CSPs.  

Discover more about Nokia’s data mesh approach and the AVA Open Analytics Framework.

Adnan Khan

About Adnan Khan

Adnan has been working as a Chief Architect in CNS BA organization. Primarily, he is working on Nokia’s next generation architecture for advanced data analytics from product and platform sides. Among his core focus areas include, data governance, data analytics and more recently data mesh. 

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