The Importance of IoT Analytics

IoT analytics is the application of data analysis tools and procedures. Used to realize value from the huge volumes of data generated by connected IoT devices.

Data Analytics (DA) is defined as a process which is used to examine big and small data sets with varying data properties to extract meaningful conclusions from these data sets. These conclusions are usually in the form of trends, patterns, and statistics that aid business organizations in effective decision-making processes.

If you look at the pace of growth of the Internet of Things (IoT) devices today, it is all pervasive.  Everything is smart – from pens, lanyards, and switches to air conditioners, traffic lights, and refrigerators.

Today almost every retail company has smart tags associated with every SKU on every aisle, which monitor when it is getting depleted so that it can trigger of an alert to an inventory management system which will trigger a replenishment procedure and enabling the item to be on the way into the supply chain in a real time.

IoT data can be thought of as a subset and a special case of big data and, as such, consists of heterogenous streams that must be combined and transformed to yield consistent, comprehensive, current and correct information for business reporting and analysis. Data integration is complex for IoT data.

There are many types of devices, most of which are not designed for compatibility with other systems. Data integration and the analytics that rely on it are two of the biggest challenges to IoT development.

Data Analytics has a significant role to play in the growth and success of IoT applications and investments. Analytics tools will allow the business units to make effective use of their datasets as explained in the points listed below.

  • Volume 
  • Structure
  • Deriving revenue
  • Competitive Edge
  • Efficiency 
  1. Volume:

    There are huge clusters of data sets that IoT applications make use of. The business organizations need to manage these large volumes of data and need to analyze the same for extracting relevant patterns. These datasets along with real-time data can be analyzed easily and efficiently with data analytics software.

  2. Structure:

    IoT applications involve data sets that may have a varied structure as unstructured, semi-structured and structured data sets. There may also be a significant difference in the data formats and types. Data analytics will allow the business executive to analyze all of these varying sets of data using automated tools and software.

  3. Driving Revenue:

    The use of data analytics in IoT investments will allow the business units to gain an insight into customer preferences and choices. This would lead to the development of services and offers as per the customer demands and expectations. This, in turn, will improve the revenues and profits earned by the organizations.

  4. Competitive Edge:

    IoT is a buzzword in the current era of technology and there are numerous IoT application developers and providers present in the market. The use of data analytics in IoT investments will provide a business unit to offer better services and will, therefore, provide the ability to gain a competitive edge in the market.

  5. Efficiency:

    Increasing the data set and analysing the data can increase the efficiency of projects. The more data will help increase the test cases thus the output while working will be more precise and accurate.

IoT Analytics is broadly an emerging area of data science and data analytics which is able to provide answers to all the questions raised above.  In particular, the challenges which are of focus in IoT analytics include:

  • Streaming Analytics Algorithms
  • Real-Time Analytics Algorithms
  • Sampling Techniques for Real-Time Data
  • Storage Techniques for Real-time data
  • High-Performance Analytics
  • Reliable Data Messaging
  • Handling multiple communication protocols
  • Security of Streaming Data
  • Anomaly Detection of Streaming Data
  • and more….

Emerging points in IoT Analytics:

  1. Consumer Product Usage Analysis for Marketing
  2. Serving Consumers and Business Users With the Same Analytics
  3. Sensors and Cameras Enable Connected Events
  4. Video Analytics for Surveillance and Safety

The full capability of IoT for business use lies in applying analytics to the data generated by the IoT ecosystem. However, this area of IoT analytics is just about emerging and needs to evolve to handle all the complexities related to streaming and real-time data.

At the same time, there is a need to understand that for data science itself this is posing a new challenge in terms of the need for new kind of algorithms capable of dealing with high velocity and high volume streaming data.

There are several things in IoT, analytics is just one of them.

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