The complicated world of telecommunications analytics continues to be a primary driver behind complex data analytics solutions and I find it mentioned time and time again in Big Data use cases and scenarios.

Those of us who have lived in this world for years will probably agree with me that we’ve been pioneers in “Big Data techniques” ever since we were asked to build CDR (call detail record) data warehouses. My first CDR solution was for customer service and marketing at AT&T in the 1990s. We used Oracle for the DW and hired PhD statisticians to build models for predictive analytics on top of that data.

The marketing department was able to utilize that data to better understand customer patterns of usage and make data-driven decisions about how to package subscriptions and products. The call center team used the analytics from our cubes for market basket and association algorithms that provided reps with the ability to cross-sell to customers, which was also used by sales for up-sell opportunities to corporate accounts.

Then there is also the mass amounts of streaming data coming from network equipment which was used by engineering and the NOC for troubleshooting, predicting outages and tuning the network. Rules for correlation, thresholds and root-cause were needed to make sense of the 1000s of events/sec and not overwhelm systems and dashboards.

Does that sound familiar to today’s “Big Data use cases”? It should. We used to call these techniques CEP (complex event processing) and VLDB (very large databases). Really, at the end of the day, what this meant was that our DBAs, architects and developers needed to think about scale and distributed architectures to account for the scale that we were dealing with.

Today, it is a nice evolution of these techniques to see Hadoop, sharded databases, NoSQL and in-database analytics providing packaged, easier ways to process and manage systems of TB & PB scale.

Essentially, what this means is that these techniques now become available to all IT departments and examples like the churn & customer analytics (the holy grail of telcos is churn management) solutions become better, faster with improved data sampling because of new, emerging Big Data technologies.

I found this story on the Internet by Harish Vadada from Telecom Cloud here. It talks about T-Mobile with databases like Oracle & SQL Server using Big Data technologies such as Hadoop, to improve the delivery of customer & churn analytics to drive both the bottom-line and top-line of their business. Very impressive and spot-on to what I am saying here in this post.

Cheers! Mark