Rapid adoption of Big Data analytics in the telecom industry has resulted in the identification of a few important shortfalls.
For one, initial big data solutions were inflexible and slow. The realization of these shortcomings is fueling future trends in the Telecom Industry. Let’s take a look at these trends and how they may shape the industry for years to come:
Constant Access to Ad-Hoc Queries
Ad-hoc, on-demand analytics is likely one of the more powerful trends shaping the industry, but it’s a bit technical so it’s important to cover the basics.
What is an Ad Hoc Query?
In the past, analytics would focus on pre-determined queries and parameters. These queries would be run against a pre-calculated OLAP cube. The cube or stored procedures are usually quite specific, and are calculated to support a handful of queries. This reduces flexibility quite significantly when a question being asked wasn’t planned ahead of time.
So, an ad-hoc (“for this purpose”) query is really any query which the database designer or DBA hadn’t anticipated.
How Does This Relate to Telecom?
Until recently, ad-hoc querying was considered too complex, too slow, and requiring too much data volume to be practical. Now however, newer, more sophisticated databases and technologies are making the practice possible.
Ad-hoc analytics can help telecom achieve a 360 degree view of customers. They can help discover and optimize new sales and marketing initiatives, enhance customer service, and improve operational efficiency.
Telecom companies can create queries looking for things like:
- What products are losing customers?
- Which price changes impacted defection?
- Are there any major changes in customer service metrics?
- What factors outside the company are impacting customer churn?
- Are there any trends in social media comments?
- Are there any trends in customer satisfaction surveys?
- How do call detail records relate to customer satisfaction?
- What percentage of customers who lodged an email complaint cancelled theirs service?
Additionally, this is part of the broader switch away from batch processing and Hadoop. Why are telecom organizations moving away from Hadoop and batch processing, you ask?
It seems that most companies have used batch processing for their big data analytics, but now the limitations of batch processing have come to life. Batch processing with legacy OLAP cubes or even Hadoop doesn’t support real time analytics or enable ad-hoc querying. There are some workarounds that involve introducing additional layers, but for retail, energy, financial services, and telecom, that’s often not enough. Newer, better databases and models that support ad-hoc querying out of the box are typically the simplest and most cost-effective solution for this.
Telecoms must have continuous real-time data and analytics in order to stay ahead of the game. This is especially true when using data to enhance operational efficiency.
Fewer Trips to the IT Department
Another trend that is occurring is decreasing reliance on the IT Department for data. Previously, big data wasn’t self service. Now, thanks to new tools for interacting with data it’s easy for business professionals to interact with data.
Outside support is now rarely needed as business professionals can visualize, gain insights and even create their own queries for data. This trend will likely continue for two or more years as it’s predicted that data discovery tools will grow at 2.5 times the rate of the rest of the BI market.
— Ami Gal is co-founder and CEO of SQream Technologies.