Driving revenues with IoT: Using big data as an engine of growth

To deal with a growing flood of IoT data in real-time, consider hardware accelerators like FPGA to help databases scale to meet the needs of IoT.

From smart homes to networked vehicles – IoT is already an everyday reality for many consumers. At the same time, a vast amount of data is collected from all of these Internet-enabled devices and products; data that opens up a new world of possibilities for manufacturers. Big Data is becoming an ever more significant engine of growth for business.

With market-disrupting innovations, current big players such as Amazon, Google and Facebook are exploiting the IoT in all sectors of daily life, and increasingly in industrial sectors too. A voice-controlled audio device that plays music and the news and answers questions, or a digital assistant that responds to voice commands, carrying out its owner’s wishes.

Unlock the full potential of Big Data with real-time analysis

The value of IoT lies in the collection of information, which can be used to respond faster and more flexibly to events and customers’ needs. This increases customer satisfaction and, in turn, customer loyalty. Making the most of Big Data means being able to process and analyze the data almost instantaneously.

For instance, when a customer is on a car journey and is running low on fuel, she needs to be informed of the location of the next filling station before she passes it, not after. This requires the real time collection, processing and analysis of data. Speed is the key. If a company is not able to evaluate its data quickly enough, it will be overtaken by its competitors. Real-time analysis is therefore the core of any IoT strategy that is focused on growth and customer satisfaction.

Overcome the technical hurdles of IoT

A range of technical issues face companies that offer networked devices. In many cases, the devices transmit data not as a continuous stream, but in no particular chronological order. What’s more, some of the data even gets lost during transmission, or is sent more than once.

Then there’s the format of the data, which often differs from device to device. Companies are faced with the challenge of sorting, classifying and analyzing this data almost instantaneously. The response to events described by the data has to be quick enough to ensure that continuous processes and interactions with people can proceed with no noticeable delay.

To deal with these challenges, Big Data’s giants have developed their own systems, managed and operated by specialists. However, these technologies and specialists are in short supply, which makes working with Big Data prohibitively expensive, especially for smaller businesses.

At the same time, introducing new technologies takes time and, for many companies, implementation would simply take too long. To benefit from the advantages of IoT, companies must be able to leverage proven, readily available technologies. These technologies must also be able to satisfy the requirements for managing Big Data. The task, therefore, is to either create the necessary technical conditions, or to optimize existing technical infrastructure, so that data sets can be analyzed quickly. The acceleration of already existing databases is one possible solution.

Efficiently accelerating big data analysis

There are now a range of technical solutions designed to process and analyze Big Data. These include Business Intelligence (BI) tools, which are also available in open source versions. These programs analyze the data once it has been saved in a database or a data warehouse.

With BI tools, valuable information can be efficiently extracted from vast pools of data, enabling companies to provide beneficial services for business analysis and customer interaction. But for real-time analysis, BI tools alone are not enough. In order to achieve real-time analysis, these tools need to be able to access a high-performance database.

One approach to solving the issue of accelerated data processing is provided by in-memory systems. These applications are special because they primarily store data in the working memory of a computer, which means it can be processed much faster than from a hard disk drive. For analytical applications, these systems are a good solution. However, if a company wants to keep large amounts of data in its database, in-memory systems are economically prohibitive because of the costs of purchasing and operating large in-memory database hardware and software.

The use of database accelerator hardware is also an option that enables companies to deal with Big Data. In order to enable the real-time processing of Big Data within an existing IT system, companies can use FPGA technology and software algorithms optimized to accelerate databases with FPGA. The FPGA board is installed in a server together with Swarm64 acceleration software, which makes it possible to process and analyze the vast amounts of data in real time with existing applications and at low cost.

Irrespective of the technical solution a company ultimately opts for, every company needs to develop an integrated IoT strategy if it wants to secure its market position. Over the next few years, the IoT is going to shape every area of business and everyday life, and will therefore play a key role in the success of every company.

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