How IoT Data Analytics is Building Smarter and Efficient Businesses?
People and machines generate data on a daily basis. Around 2.5 quintillions of data will be generated or created by the same time tomorrow. For your information, 1 quintillion is 1 followed by 18 zeros. Since data is ubiquitous and also essential for every industry and organization, it is essential to leverage it and create more efficient products, services, systems, operations, and processes.
While data is everywhere, we have subtypes of the same, and one of them is IoT data. When data created by IoT-enabled machines needs interpretation, we need to indulge into IoT data analytics. Effective interpretation of data identifies new business insights and provides better opportunities.
It is essential to manage and handle the IoT data analysis to build pertinent solutions via the insights and recommendations provided by the same. Understanding the information sensors and controllers are providing goes a long way in helping organizations generate more revenue from the same resources, bring automation into the operations, and help with forecasting.
In the sections ahead, you will find detailed information on IoT data analytics and its importance for an organization.
What is IoT Data, Analytics, and Types of IoT Data?
IoT data represents the information and insights generated by machines and systems in an IoT environment. This includes machines, systems, and people who interact on a daily basis. This data is read by sensors installed on the machines and sent to an interface accessible by humans.
IoT data analytics is about understanding the data generated here and helping build effective solutions. The purpose is to gain value from large volumes of data. With volume, the IoT data has also been growing in terms of variety and velocity, completing the 3Vs model.
Understanding the data means you can make the correct decisions and solve many problems. However, data analytics is about working with several tools and solutions to make sense of the data. IoT data is heterogeneous, and we need to secure accurate information from the same with the right tools.
Types of IoT Data
We can put IoT data into two categories;
- Structured data:
The best thing about structured data is that it fits into a relational database management system. In other words, this type of data follows a logical schema and a model defining clearly how the data must be represented and organized.
Structure data is often arranged in a tabular form and organized into a spreadsheet. Data generated with IoT sensors use structured values integrated into the system. This includes temperature, pressure, humidity, etc.
- Unstructured data:
Unstructured data is without a logical schema and structure. Moreover, it cannot be understood and decoded with the traditional programming systems and tools.
As there are predefined data models and structures, information that does not fit into these models is unstructured. Because this data cannot be understood easily, we are using advanced technologies like machine learning and cognitive computing.
Technologies like natural language processing (NLP) are used to recognize texts and decode speech, which is also a form of unstructured doctor.
IoT sensors and systems generate both types of data. That is why we need to use simple to advanced IoT data analytics systems for their interpretation.
How do Data Analytics and IoT Merge?
IoT data analytics can take four different forms;
Descriptive IoT Data Analytics:
Descriptive data analyzes the current positioning by monitoring the IoT devices in real-time. It monitors the status of the IoT devices and machines to verify that the systems are working properly, as planned. The interface connected to a descriptive IoT analytics platform primarily shows the KPIs and sensor data.
Diagnostic IoT Data Analytics:
Diagnostic data helps identify issues to build better solutions and create fixes. With diagnostic IoT data, we can compare sets of information and create visualizations and trends. Some organizations also hire IoT data analysis experts with specific domain knowledge and expertise to diagnose the data.
Predictive IoT Data Analytics:
Predictive IoT data analytics helps assess the likelihood of some event in the future. The future associations are built on the basis of current information and relate to a specific time frame. The benefit of predictive analytics is to take corrective action well in advance and recognize opportunities.
Perspective IoT Data Analytics:
This is more of an action-oriented approach to data analytics. At the end of the analysis, the users will get a list of suggestions based on the diagnosis. Plus, the users will also get the reason behind taking the recommended action.
Due to this merger between IoT and data analytics, companies are set to benefit in three major ways.
- Higher Revenue:
With effective analytics, companies can dive deeper into the customer’s preferences and choices. As a result, the companies can build customized services and products for their customers, leading to higher revenues. Ultimately, it leads to higher sales and better revenue.
- Handle More Volume of Work:
With IoT data analytics, companies can access the real-time capacities and capabilities of their resources. This includes machines and human resources. As they are able to understand them better, operations can be scaled, and more volume of work can be processed without compromising the service quality and output.
- Build a Smooth Work Structure:
Creating a set structure in the organization can be easily executed with IoT data analytics. The data generated by machines can be structured and unstructured, but with effective analytics software, it can be converted into readable information. We can then use this data to create a smooth workflow.
Applications of IoT Data Analytics in Different Industries
IoT and data analytics has applications in every industry and organization. Here we are listing some uses and benefits of IoT data analysis in different industries.
Healthcare:
IoT data analytics can play a life-saving role in the healthcare domain. We already have several types of wearable devices linking a person’s health to the database and supplying information. In addition to this, data from fitness trackers, life support equipment, inventory systems, etc., can be harnessed to take the required action.
Transportation:
Transport companies can use IoT analytics to improve their fleet performance and manage operations effectively. IoT data analytics can improve fuel efficiency, monitor vehicle health, suggest real-time traffic route changes, monitor driver performance, etc.
Weather:
Weather forecasting and reporting has become more accurate and efficient with the help of IoT data analytics. With sensors installed in various locations, we can track, analyze, and identify weather patterns and conditions. While these aspects were possible earlier as well, with IoT, the analysis has become more accurate, precise, and quicker.
Government:
The government and its organizations can leverage IoT data analysis to improve their functioning in all domains. IoT data analytics can help monitor and maintain the government facilities. This leads to reducing the maintenance costs in different types of work.
It can help measure the efficiency of utility services, track the performance of smart meters, suggest changes to households for optimized consumption, etc. In the end, governments can use IoT data analysis to provide better services to their citizens.
Logistics and Supply Chain:
For logistics and supply chain organizations, IoT can be useful for analyzing operational data. This includes tracking the vehicle’s location, managing the packages, fuel usage, sorting the packages, maintaining the travel logs, etc. The combined impact of all these aspects is that IoT in supply chain and logistics can help make better decisions.
Conclusion
Understanding data is becoming increasingly important in every type of business and industry. With the increasing installation of IoT devices, the understanding of data generated is becoming pivotal to running a profitable business and optimizing the existing systems.
However, implementing IoT data analytics solutions can face a few challenges, including too much data, security, and device anomalies. It is imperative to address these issues with the IoT data analytics tools and install secure infrastructure plus firmware to build an efficient IoT environment.
While implementing IoT data analytics systems, evaluate your existing requirements and hire experts to integrate the required systems into your organization for the best results.
