Introduction
In IoT, data is shared by connected devices throughout the day. This data is worthless if it is not being analyzed. This is where Data Analytics in IoT comes in.
“Data Analytics in IoT” means using data analysis tools to compute and analyze the data collected from different IoT sensors. This analyzed data is then used to track the current status of the devices using pre-set metrics. It is also used for:
- Optimizing Operations
- Automating the Control Processes
- Engaging more customers
- Empowering the employees
Data Analytics in IoT is carried out in one of two ways.
- Ad-hoc Data Analytics allows data analysts to collect and analyze data at specific intervals of time.
- Real-time Data Analytics allows data analysts to access data as it is streaming from the devices in real-time.
Each type of data analytics has its own set of pros and cons. For example, ad-hoc data analytics is easy to use and customizable. On the other hand, real-time data analytics allows you to perform predictive maintenance.
Steps of Data Analytics
Collecting:
Data Analytics in IoT works on an architecture where sensors are the starting point. These sensors collect the data and transfer it to the data lake, where the data is then stored. If the data needs transformation before analysis, it’s transferred to the data warehouse.
Storing:
Data warehouse stores the cleaned and groomed data for analytics. It also receives data from the control applications used to govern actuators. It stores the data related to machinery configuration, i.e. where the sensor is, etc.
In a way, the data warehouse is aware of everything. It knows what data the sensor sends, where it is placed, and the action performed using the actuators.
Analyzing:
After the data is collected, stored, and transformed, the analysis of the data occurs. This analysis can be done manually, by a professional who can read and predict the data.
On the other hand, machine learning can also be used to analyze the data. If the last link of the architecture is smart data analytics, it will watch the data, notice patterns, make new models, and send the action to the actuators.
Data Analytics for Organizations
Enterprise IoT paired with data analytics is proven to be beneficial in healthcare, manufacturing, Smart Cities, Smart Grids, etc. Similarly, IoT data analysis can be beneficial to organizations.
When a proper IoT analytics solution is in place, the data produced by the organizations will be effectively collected, analyzed, and stored. This data will allow the organizations to optimize operations and improve decision-making.
- Better Human Productivity
Organizations are now equipped with IoT devices to collect data like employee engagement, performance ratings, etc.
This data is then analyzed to improve daily operations to utilize employees’ time and energy efficiently.
- Improved Equipment Maintenance
IoT sensors with data analytics can determine when equipment needs maintenance through various parameters. This would facilitate regular equipment maintenance and contribute to predictive maintenance.
- Operations optimization and automation
As IoT and data analytics work in tandem, organizations can automate control processes instead of manual tracking.
Challenges and Barriers in IoT Data Analytics
Although data analytics in IoT is useful in many aspects and fields, there are a few challenges that come along with it.
Multiple sensors sending data every 30 seconds results in data overloading for both, workers and computers.
Data Analytics in IoT also has security issues. Since many devices exchange data, if there is a breach in any one device, there is a high chance that it could spread throughout the network. This is especially risky if the data compromised is personal information or high-level security data.
Conclusion
Once the barriers faced while implementing Data analytics in IoT are dealt with, it is not only helpful but crucial if the data being shared has to mean something. The analyzed data is especially useful to organizations as it can help improve many aspects of the operation like productivity, implementing predictive maintenance, optimizing and automating operations, etc.