IoT & Embedded Technology Blog

Overcoming The Hurdles of Big Data

With the growth of the Internet of Things through the proliferation of connectivity, a massive amount of data traffic is being generated. But it is not only the volume of data, but the vast variety of data as well. This amount of data is creating huge burdens for networks and challenging the business models of service providers. Individual users are increasingly online for more minutes per day and utilizing more bits/user than ever before, and this trend will only continue. The necessary investment in backhaul technology is slow in catching up the needs of this demand, and this hinders service providers ability to capitalize on their existing investments. This demand for data and rich media content is also shifting the hardware priorities for OEMs towards enabling greater connectivity and the ability to manage systems of remote connected devices.

But ultimately this massive data traffic creates huge opportunities in the form of Big Data. The connectivity now coming into the market provides access to data that was previously locked away. Machine to machine (M2M) communications is providing an industrial kind of Big Data that previously was inaccessible to most companies. Big Data is also important for managing these fleets of connected devices to ensure optimal alignment of resources with demand at all times of day.

Big Data is not without its challenges. First the connectivity has to be in place, which is obviously a major hurdle and investment drain. Beyond this factor, survey respondents indicated a number of major obstacles to the development of Big Data solutions. First, there is the need to improve the performance of solutions. How quickly and accurately the data can be digested and transformed into useful information is the real value of any Big Data solution. Security is also a highly cited concern to ensure the quality of the data collected and that only the right people have access to this valuable information. Another area for improvement is reducing the cost of Big Data solutions. Overcoming these hurdles will of course take time, but they provide priorities for developers of Big Data analytical tools.