Managing IoT data efficiently to derive with better insights

Managing the data generated by Internet of Things(IoT) efficiently helps any company to derive the new perceptions in identifying the opportunities and further innovate to grow beyond in their industry verticals. It also helps to achieve more improved design strategies and workflows.

As any Internet of Things project goes from initial concepts into real projects, main challenges are understanding about the process of data generation and transportation between the IoT systems and answering to the below questions. 

These questions must be asked during the design phase. For organizations, this preparation and design phase is essential to make sure they use the right tools from the start.

Many companies around the world are have just started taking the initial steps in capturing the potential value of streaming big data and maximizing the benefits.

Naturally, this way is paved with great challenges related to data security and ownership, creating the infrastructure for data sharing, monetization and solutions for data analysis.

IoT data can be beneficial in multiple ways, at the core - it can be helpful in better decision making to understand customers for improving business. Some of the ways data can be handled are 

Most companies and governments adopting IoT, fail to use their data fully or only getting small portion of benefits from their data. Till now, in most of the companies data is used for tracking online operational issues – monitoring and failure detection, rather than prediction and optimization. Prediction and optimization are known for revealing the true value of data reusability.

IoT data makes companies to get transitioned from the concept of “replace and repair” to the “predict and prevent”, before it becomes problematic. It also helps to increase efficiency, reduce maintenance costs and provide greater financial benefits.

McKinsey estimates, predictive models help to reduce maintenance cost by up to 40% and decrease equipment downtime by 50% in few industries. Reusing the data for future operations is one of the most vital thing in obtaining data.

To get true value of IoT data, companies need to integrate and analyse their data using data integration and analytics tools. With the help of data scientists, need to analyse vast amount of varied structured and unstructured data to get better insights. In order to implement these technologies, companies need to update their IT infrastructure which cannot be done with their traditional old infrastructure.

This sort of integration will provide the ability to communicate between different IoT systems and services simultaneously which perform multiple functions. Low cost data infrastructure services are needed for many companies to adopt IoT and further scaling out their operations. To name few - Microsoft, Cisco, Samsung, Bosch, Orange and Fujitsu are working with IOTA blockchain to launch an open data marketplace that helps any player to get the bits of valuable IoT data at a microfee. Devices are enabled to trade information autonomously by using IOTA data marketplace. This will help in increasing IoT adoption among all kind of business from small startups to big companies to derive better insights from their data and run the profitable business out of that.

For sharing and accessing data, it needs further coordination among multiple players in IoT ecosystem. And, entities should also accept to mutually access the data with the concern of security, legal conformities, etc.. For enhancing these data access and sharing, there should be need for development of new business practises, data ownership policies and behavioral ethics on collaborating with other IoT entities in the ecosystem without ambiguity. If policy makers and businesses comes together under the clear norms, linking the physical and digital worlds could generate up to $11.1 trillion a year in economic value by 2025.

Many Companies are doubtful in implementing IoT business practises in real world scenario because they are much concerned about ROI despite knowing the IoT potential. Still some of the companies are taking IoT as the secondary product or action and many of them are sticking with “wait and see” approach. This also makes delay in implementing IoT solutions.

There are cases when some of the companies need to implement their business ideas with the help of other company’s data, they need to have right consent from them in accessing their data. Making money from IoT should be enormous when multiple companies coordinate in sharing their data under a clear norms.

IoT data application in various sector tend to bring opportunities for redesigning services, products and operations to get better business impacts. The use case of IoT was unlimited, it brings traditional way of approach to new way of efficiency and accuracy in doing things. Already these changes come into existence in the form of smart homes, wearables and Industrial IoT data adoptions like oil rigs, electric grids etc..

For example : Real time data generated from various points in the roadways across a city helps to control traffic and alert vehicles to make better transportation. Data can also be used for anything like the amount of cars passing by your house, depth of the lakes in near by locality or anything.

There is a process going underhood to make use of data for any purposes. The key processes of extracting insights from the data are

These can be discussed in detail below to know what exactly happening with the data in IoT to get better insights and its importance in shaping the future world.

This is the first stage where data is generated by the device or sensor which records any events or changes in its habitat and sent it over the internet. For delivering data efficiently over the network to the destined central application, there is need for deciding from the standard protocols like MQTT, CoAP, HTTP, Mosquitto, RabbitMQ. HTTP is effective but it does not suit for low bandwidth conditions. So, for that we can use CoAP or MQTT. Sending the data is primary step in data processing in the IoT system. These data can be collected from many interconnected systems in real time or batches depending on the use case of application.

The main aspect of data usage lies in how we store the data and how we are using that stored data for future use. Data can be collected and organised in real time or in batches using database like Hadoop and Cassandra. The real value of data is obtained from the order in which data points are created. If the order of data is not accurate, then there is no use in obtaining data and it only leads to false insights. For the devices or things having battery constraints, it’s better to obtain data in batches under specific time interval.

Traditional databases doesn’t handle large volume of data (big data), for that we will need NOSQL database platforms like Cassandra. By using this sort of distributed database systems, data loss can be avoided with added efficiency.

The most important motive for storing data is to analyse and get trends from that data to make the operation more efficient. For analysing data, we can use tools like Spark, an open source cluster computing framework which helps to look for trends over the time and making predictions from behavior patterns.

Data analysing tools like Apache Spark(for analysis of big data) and Spark Streaming(for real time data) are used along with database system like Cassandra to process and analyse large amount of fast moving data. 

Understanding the storage of data for their applications and analysing of one specific industry would be helpful in promoting growth of other sectors or companies to join IoT ecosystem and able to mutually share their resources.

Companies takes their further steps based on predictions and trends to see their business growth over the time. For further improvements and scaling out their business, companies need to put their efforts in understanding more about IoT data and have a strategic vision of future prospects. These efforts may be like recruiting more knowledgeable data working professionals and improving their data IT architecture for getting better insights.

Now companies are using Cloud Analytics and Fog Computing solutions to get better insights-driven decision making and business outcomes. IoT cloud platforms provide subscription based usage for accessing these services offered by big players like Aws Amazon, Google Cloud, Alibaba Cloud and Microsoft Azure etc..