Big data refers to mass amounts of data that are beyond the ability of traditional methods of collection and analysis due to their size or type. The value of big data can be found in any operation that can extract insightful information and draw conclusions from the big data in a way that improves their business.
This is because big data relies on the principle that the more data that businesses have on any given subject, the more they can discover hidden correlations and even predict outcomes.

Big data has three defining characteristics: high volume, high variety and high velocity. What makes big data difficult to capture and process is more than just the sheer amount of data being created and collected, but an expansion of all three of these big data’s characteristics: mass amounts of information, consisting of thousands of different types, being collected and processed at lighting quick speeds.

While big data can be difficult to analyze and draw conclusions from without the necessary resources, the value that it can provide can be revolutionary.

The process of comparing quadrillions of data points between one another has become automated, drastically limiting the necessity of human interaction. Plus, with the emergence of machine learning and artificial intelligence, big data is capable of spotting trends at previously unimaginable speed and accuracy.

An example of this can be found in the telecommunications industry with a leading telecommunications company that supports over 151 million customers.

Without big data, it would be impossible for this telecommunications giant, let alone any of its competitors, to record and store the data collected from apps, music, maps, calls, GPS and more.

However, this mass information, if it’s analyzed, can provide incredible value when it comes to sales, marketing, operations and other aspects of this telecommunication company’s business.

This actionable data could help them with pricing adjustments and increase satisfactions. Plus, depending on where customers are located, it could help management decide what the optimal location would be to build new cell towers.

Understanding the behaviour of each and every one of their customers can help them determine their pricing, guide them on marketing and enable them to cut costs on features that are lacking demand.

Big data and IoT technology are often interconnected, as the mass amounts of data collected by the hundreds of thousands of IoT-connected devices in any given IoT project or solution must be analyzed to provide informed decisions to businesses and enterprises.

As a result, the more that IoT technology grows, the more big data capabilities businesses will need if they want to keep the initiative of improving decision making.

Together, big data and IoT technology have countless applications in our world, as both provide industries with analytical capabilities that were previously unattainable.

In Barcelona, Spain, the government is using IoT-connected sensors in a variety of different applications.

They’ve created smart parking spots that indicate where available spaces are, smart bus stops that track bus locations in real time, and a city-wide network that provides information on temperature, traffic and more.

To handle the data load, Barcelona created a big-data system to process and analyze all of the information that is collected from the IoT-connected sensors.

The transportation industry, with countless vehicles in transit around the world, also has plenty to gain from implementing big data and IoT technology. For example, UPS improves efficiency and reduces environmental impact with IoT-connected sensors that measure speed, miles per gallon and mechanical health among other factors.

As one of the world’s largest shipping companies, this amounts to a large data load that needs to be filtered, which is why they use big data analytics to inform their operations.