Understanding IoT Analytics And Its Future Growth Prospects

Introduction

 

The concept of the Internet of Things is significantly altering the way modern-day organizations operate.To take full advantage of this revolution, companies must change how they process and store big data. The core idea of the Internet of Things is that every piece of machinery and equipment will have an IP address and will transmit data relating to its operation. All this data can be gathered to analyze how the entire industry is functioning and how various parameters like efficiency and production can be improved.

 

IoT is enabling enterprises to improve their productivity and to predict the maintenance requirements of machines for greater efficiency. For example, by placing sensors on every single component in a particular factory, a clear map of functionality can be obtained. When something is about to go wrong, the sensors responsible for the failing component raise an alarm and a distributed intelligence platform deployed on the factory’s sensor network will shut down the process to avoid further damage. This can help an organization to properly maintain the machinery and avoid total breakdown, which can lead to millions in losses. When such a sensor network is implemented over an entire industry of a large scale like public utilities, it can lead to massive increases in productivity and reliability.

 

How IoT analytics is different

 

IoT analytics deals with processing the large amount of data that is received from different devices, which are neither mobile phones nor computers. IoT analytics’ fundamental difference with web analytics is the number of data sources. In web analytics, there is only a single data source from a single website. All the user interactions are contained within this website. With IoT analytics, it is not as simple. IoT encompasses a large number of connected devices by definition. These devices are not mobile phones and laptops. Instead, they are appliances and consumer electronics like televisions, washing machines, microwave ovens, coffee makers, etc. Gathering data from all these devices is not simple because of the huge number of data sources, their geographical variations, and their resource and time constraints. IoT analytics deals with this problem of gathering all the device transmitted big data and deriving meaningful information and business intelligence from it. Due to this aspect, IOT analytics is a very different concept compared to web analytics.

 

The Future of IoT analytics

 

Internet of things analytics will need to deal with larger data volumes and shorter processing times in the future. Since the number of electronic devices that are being produced increases exponentially, the data generated from these devices also can be expected to follow the same pattern. Several other challenges will continue to arise during wide spread adoption of IoT. A few predictions about the future of IoT analytics areas described below:

1. Increase in number of IoT devices and IoT Big data
    
   As the cost of electronics and processing power decreases, IoT will continue to be implemented in a wide range of devices from home automation, automobiles to industrial sensors and devices. With the increase in the number of devices, the amount of big data will also increase. It will be expected of IoT analytics to process this huge data almost in real-time for the purpose of both predictive learning and equipment monitoring. Such a fast processing system will reduce the response time of companies in responding to adverse situations that may occur in their operations.
    
2. Development of modular IoT analytics solutions
    
   These days, most IoT systems use custom solution stacks that are designed for a specific set of operations like industry-based (sensors, actuators), appliance based (coffee makers, microwave ovens) and others. Although these solutions work perfectly well for the current IoT traffic, in future we may see companies opting for modular solutions, which offer blocks of custom software tools to read, process and transmit IoT data. Such modular solutions can reduce the development time of IoT solution stacks and offer a significant increase in productivity and performance.
    
3. Decentralization of data processing
    
   As the amount of data continues to increase across a large geographical area, it will become unfeasible to gather the data from a large number of devices. This will lead to the development of local data processing and analytics hubs, which make sense of the low-level sensor information, and sends the result to the forward central data analytics hub. This also helps in overcoming physical data transmission problems such as network latency and lost data. This will unclog the bandwidth and will allow other important applications to use it. Decentralizing the low-level data processing will also lead to an increase in speed and efficiency.
    
4. Development of new analytical methods based on machine learning
    
   New machine learning techniques will be integrated with analytics to create new and improved methods. Machine learning can help in identifying minor details and information, which can be easily overlooked. These methods can also be trained to identify particular problems with a system and recognize specific data patterns. For example, machine learning in a power plant can identify minor changes in load with respect to time of the day and adjust the production accordingly without any human intervention.

Internet of things is still a developing concept and only recently it has seen a sharp increase in adoption. Given this, appropriate standards still need to be established in this field. The most important challenge for IoT analytics is aggregating the data from different types of independent devices and deriving meaningful information, which the companies can use to improve efficiency. Although the data gathering and processing problems have already been offered a solution, there is still a large swath of unexplored ground with regards to IOT analytics.

 

The new developments in IoT analytics will need to deal with the challenges described above. Technology consulting companies are offering various IOT analytics consulting solutions in a large number of verticals that can be leveraged by organizations to select and implement the right solutions. As a final note, it will be some time until the Internet of things concept is firmly established and when it arrives, we would be able to control every connected device that we own with a simple touch on our smartphone. 

 

Read more articles on the Internet of Things:

• Internet of Things (Basics of IoT)
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