Pitney Bowes argues coverage estimates will not cut it in the telecom big data age
Smartphone ownership is near saturation levels for many consumer segments, yet demand for mobile connectivity is on the rise. The “Internet of Things,” wearables, smart cities and mobile payments represent just a few of the drivers increasing the need for precise, reliable service. As self-driving cars become the norm, there will be even less room for error.
Telecom providers must find new ways to increase coverage, capacity and quality. Plans for expansion, optimization and new “5G” networks, and the ability to capture, analyze and access large volumes of data becomes a distinct competitive advantage.
According to a recent Deloitte survey, U.S. consumers look at their mobile devices more than 8 billion times per day. Every call, text, logon, stream and SnapChat creates a data point. Each of these data points is linked to both a device and a location.
A few years ago, telecommunication providers had to rely on data sampling and engineering estimates to quantify signal strength and generate coverage maps. Many of these estimates were based on proximity to the next antenna. Today, you can analyze billions of actual connections across millions of cells, processing real data in real time.
This combination of big data analytics and location intelligence adds accuracy and precision to every decision. With the right technologies, you can verify customer coverage based on actual customer usage, producing coverage maps that are vastly more current and accurate. In-depth visualization details exactly where you can expect LTE, 3G or other levels of coverage – insights that can inform network optimization, marketing, customer services and emergency planning.
The convergence of big data and location intelligence
Most organizations are not yet equipped to optimize coverage in the age of big data. Certainly, more firms are building capabilities to process big data via Hadoop, Spark and other applications. Most also have some capacity to produce maps and spatial analytics. What’s missing is the ability to do them together. When it comes to network optimization, it’s the convergence of big data and location intelligence that makes all the difference.
Think of it this way: if you enrich data, create maps and conduct spatial analytics outside of your big data processing environment, you can’t take advantage of speed or scale. To overcome this challenge, you need a way to embed location technology within your big data solutions. When you embed rather than connect, you can interpret transactional data and resolve critical business issues with the speed and clarity you need.
This embedded approach is critical. The volume of data points is increasing at an exponential rate. More importantly, customer expectations are so high it may only take a few dropped connections to cause a dissatisfied consumer to switch providers.
Map out your approach to network optimization
Consumers rely on their mobile devices more than ever before, and telecom companies are investing to update infrastructure and expand footprints to handle this increased data usage. In this highly demanding, competitive environment, estimates and analysis based on sample data will no longer suffice. In order to optimize coverage in the era of big data, firms should seek more sophisticated methods to map coverage, increase network capacity and meet customer needs.
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