Centralizing Data: How Telecom Providers Can Harness Multiple Data Streams

Posted by John Sarkis on Jan 17, 2024 1:31:24 PM

As society's dependence on telecom services intensifies, so does the surge in data volume generated by Communication Service Providers (CSPs)

Telecommunication networks often rely on a mix of backhaul vendors and technologies to ensure seamless connectivity, efficient data transfer, and robust network performance, thereby meeting the increasing demands of users. While this diversity brings benefits, it also introduces complexities in terms of monitoring, management, and issue resolution. 

Adding to the complexity, the data collected by CSP's from these vendors is sometimes scattered across numerous data lakes with various technology and vendor-proprietary formats. This abundance of data provides little benefit unless providers can extract meaningful insights from the diverse network elements embedded in their backend infrastructure.

In this blog post, we'll unravel the impact a centralized data dashboard such as our Event Storage & Analytics Platform can have in reshaping how telecom providers navigate their data operations.

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Topics: Big Data, ESAP, Machine Learning, Fraud Prevention, Data Plumbing

Next Time Call A 'Plumber'

Posted by Ryan Guthrie on Aug 21, 2018 11:00:00 AM

Tell me if this story sounds familiar. You have an idea, a hunch, a theory that you'd like to 'flush' out. BUT, in order to do so, you need access to data from last month, maybe it's CDRs, billing data, smart meter data, etc. The emails, conference calls, and requests you'll have to make to get your hands on that data are daunting at best. But it's your data right? Why should you have to jump through so many hoops to get access to it? By the time you can even figure out who "owns" the data, you're onto the next task and your great idea is wasted, yet again.

Over the years, I can't tell you how many times the 'data access' issue has delayed or even completely stopped a project we've been involved with. Not because the idea didn't make sense or the business case didn't prove out, simply because getting the data from 'Point A' to 'Point B' was going to take too much time and effort. This is the definition of a 'data plumbing' issue. Big data tools are getting better, faster, cheaper, and more available every day. But the challenge of extracting and integrating data from a variety of sources has become an issue that organizations simply can't ignore. It's the ugly truth behind data analytics - it often takes more time and energy to extract, clean, and integrate the data than it takes to do the analytics itself.

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Topics: Telecom Data Analytics, Big Data, BigQuery, ESAP, Data Plumbing