Understanding the challenges of Big Data Analytics in IoT tech

Understanding the challenges of Big Data Analytics in IoT tech

CIOs explain that while big data comes with its own set of issues, effective implementation helps in the analysis of assets, connected devices and making better decisions

Enterprise leaders point out that a thriving Internet of Things environment needs standardization that includes adaptability, global operations effectiveness, dependability, and interoperability. Rapid development in IoT accelerates data growth.

The launch of big data by IoT helped transcend the boundaries of existing data processing features of IoT. Experienced CIOs recommend the adoption of data analytics for robust solutions. They say that the success of IoT relies on the influential partnership of big data analytics.

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The relationship between IoT and Big Data

Leaders acknowledge that IoT and big data are two different concepts reliant on each other, ensuring final success. This inter-dependency exists because both the technologies have the conversion of data into actionable measures as their end goal.

This partnership is best observed in enterprises that leverage the collated data to make ad-hoc decisions and forecast future maintenance needs. Organizations also store the collected data for getting a better view of the performance over a prolonged period.

CIOs say that the blend of real-time IoT data and long-term Big Data analytics help reduce the extra expenditure

This also helps to improve effective use and efficiency, and ensure continued operations with available resources.

Challenges of IoT and Big Data

Data management and storage

Enterprise leaders say that the major associated with the two technologies is that the data produced from connected devices has continued to increase at a high rate. However, most big data systems have a limited storage capacity, which becomes a big challenge, making it critical to develop mechanisms or frameworks that are capable of collecting, saving, and handling data.

Data visualization 

Leaders point out that data visualization harvested from connected devices is hard to deliver immediately. This is because the data is obtained as heterogeneous data or semi-structured or structured or unstructured data of different formats.

As a result, enterprises have to polish the data for improved visualization and analysis to ensure correct decision making in real-time simultaneous to improving the efficiency of the industry.

Device security

CIOs say that the biggest issues faced by analytics are the constant breach incidents on devices as big data tech or big data solution is prone to security attacks. Additionally, data processing also faces hurdles resulting from short networking, computational, and storage at the IoT device level.

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Different big data tools serve real-time and valuable data to devices that are connected at the global level. Iot applications and big data help analysis of data efficiently and accurately via relevant mechanisms and techniques. Depending on the type of data harvested from heterogeneous sources, data analytics measures will vary.

The continuous need for power 

Enterprise leaders say that IoT devices need an uninterrupted power supply for the stable and continuous functioning of IoT operations. The connected devices need light-weighted mechanisms as they are lacking in processing power, energy, and memory.