IoT and Data Analytics

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This report determines and discusses the association of data analytics in the field of Internet of things (IoT). The internet of things (IOT) and data analytics remain intrinsically associated with each other (Xu, Zheng & Wang, 2016). The data gathered and generated keep on growing at an ever-expanding rate. This influx of data is spreading widely as it is analyzed that there will be approximately 30.73 billion IoT connected devices by the year of 2020. The internet of things (IOTs) is an interconnection of number of devices, technologies, networks and human resources to accomplish a common and basic goal. IOT-based applications are of many varieties that are being used in various domains and have succeeded to provide number of benefits to the users. The generated data from the various IOT devices turns out to be valuable only if it gets subjected to analysis that brings the data analytics into the picture. Data analytics is a process that is used to evaluate large and small sets of data with varying properties of data to extract actionable insights and meaningful conclusion. These conclusions are usually in the form of statistics, patterns, trends, which aid business organizations to proactively engage with the data to execute effective decision-making process (Ziegler, 2017).

Merging Data Analytics and IoT will Positively Impact Businesses

Data analytics plays a vital role in the success and growth of IOT based applications and investments. The analytics tools enable the organizational units to create effective use of their datasets as defined in the following points:

Volume: There are large clusters of sets of data which are used by the IOT based application. The organizations require to manage the huge amount of data and also need to analyze the same to extract relevant patterns. These datasets, along with the real-time data can be analyzed efficiently with the use of data analytics software (Ziegler, Nikoletsea, Krco, Rolim & Fernandes, 2015).

Structure: IOT-based applications involve the datasets, which may have a different structure as semi-structured, structured, unstructured sets of data. Besides, there is an essential difference in data types and formats. The business executive allows data analytics to analyze overall varying data sets that are using automated software and tools.

Driving revenue: The use of Data analytics in IOT investment enables the organizational units to achieve an insight into the customer choices and preferences. Further, this would lead to service development and provides services as per customer expectations and demands. This, in turn, will effectively increase the profits and revenue gained by the organizations (Wang, Ren, Zhang, Hou & Xiao, 2018).

Competitive edge: Internet of things (IOT) is a buzzword in the present era of technology and there are various IOT based application providers and developers present in the market. Moreover, the use of data analytics in the IOT investments will offer an organizational unit to provide better and improved services and will provide the ability to gain a competitive edge in the market.

Different types of Data analytics

There are many types of data analytics approach, which can be applied and used in the IOT based investments and application to gain benefits. Some of these types have been described and listed below:

Streaming Analytics: This form of data analytics refers to the event stream processing, which analyzes the large in-transition sets of data. Further, real-time data streams are analyzed in this process in order to evaluate and detect significant situations and immediate actions. Internet of Thing (IOT) based application of financial transactions, traffic analysis and air fleet tracking can gain benefits from this approach (Sun, Xia, Song & Bie, 2014).

Spatial Analytics: This is the data analytics approach which is used to analyze the geographic patterns in order to identify the spatial relation among the physical objects. IOT application based on location services such as smart parking applications can gain benefit from this approach of data analytics.

Time series Analytics: As the name refers, this type of data analytics is based on the time-based data that is analyzed to reveal the associated patterns and trends. Internet of Thing (IOT) based applications, such as the health monitoring system and weather forecasting systems can gain benefit from this approach of data analytics. 

Prescriptive Analysis: This form of data analytics is the set of predictive and descriptive analysis. It is used to get a clear understanding of action which can be taken in a specific situation. IOT based applications in the commercial can make use of this form of data analytics in order to gain better conclusions.

There are several scenarios wherein the IOT investments are adversely benefited from the application and the use of data analytics. With the advancement and change in technology, there are many emerging sectors in which data analytics can be used along with the Internet of thing (IOT). For instance, actionable marketing can be processed by using data analytics with the usage of the product. IOT analytics also enable surveillance abilities and increased safety through the application of data analytics approaches and video sensors. Health care is the primary field which often uses data analytics in IOT based healthcare applications providing breakthrough services within this domain. The reduction of cost of healthcare, enhancement remote health services, and telehealth monitoring increase treatment and diagnosis can be achieved using the same.


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Wang, Y., Ren, Z., Zhang, H., Hou, X., & Xiao, Y. (2018). “Combat Cloud-Fog” Network Architecture for Internet of Battlefield Things and Load Balancing Technology. 2018 IEEE International Conference On Smart Internet Of Things (SmartIoT). doi: 10.1109/smartIoT.2018.00054

Xu, B., Zheng, J., & Wang, Q. (2016). Analysis and Design of Real-Time Micro-Environment Parameter Monitoring System Based on Internet of Things. 2016 IEEE International Conference On Internet Of Things (Ithings) And IEEE Green Computing And Communications (Greencom) And IEEE Cyber, Physical And Social Computing (Cpscom) And IEEE Smart Data (Smartdata). doi: 10.1109/ithings-greencom-cpscom-smartdata.2016.87

Ziegler, S. (2017). Considerations on IPv6 scalability for the Internet of Things — Towards an intergalactic Internet. 2017 Global Internet Of Things Summit (GIoTs). doi: 10.1109/gIoTs.2017.8016238

Ziegler, S., Nikoletsea, S., Krco, S., Rolim, J., & Fernandes, J. (2015). Internet of Things and crowd sourcing - a paradigm change for the research on the Internet of Things. 2015 IEEE 2Nd World Forum On Internet Of Things (WF-IoT). doi: 10.1109/wf-IoT.2015.7389087