IoT and Big Data Analytics

What is Big Data and the Internet of Things (IoT)?

Big data is explained as a massive collection and understanding of data, which is made possible through strong analytical capabilities that monitor and analyze various digital streams. The ability to analyze large, complex and rapidly changing datasets comprised of structured, semi-structured or unstructured data to gain valuable insights. Sparrow ERP Offers Deeper Insights Into Your Data and gives you better-tailored insights, data Captured in Sparrow ERP stored in ElasticSearch. You can use kibana to power your next generation analytics and machine learning strategies.

The Internet of Things (IoT) includes various objects with different capabilities, which have a common way of communicating to enable information transfer. Where that information is understood by two or more objects in order to make the process more efficient.

IoT comprises the infrastructure of hardware, software and services for networking of things. Internet of things infrastructure is event-driven and real-time, supporting context-sensing, processing and exchange with other things and the environment. Sparrow ERP collects data from your IoT devices and you can visualize your data on Thingsboard platform. Sparrow ERP integrates with the Industrial Internet Of Things (IIoT) gadgets completely with your current business flows and improves efficiency.

Sparrow ERP ElasticSearch Kibana

The main objectives of the Industrial Internet of Things (IIoT) are to improved affordability and Availability of processors, sensors, microcontrollers and other technologies are helping to facilitate capture and access to real-time information. Optimizations of operations, boosting productivity, saving resources, and reducing costs are typically the main goals of IoT solutions applied in industry. For example, the industry might use IoT to keep track of business assets, improve environmental safety, and maintain quality and consistency in a production process.

Key Functions Required by IoT and Big Data Analytics

Device and Infrastructure Management Platform

  • IoT requires operators to operate the software on devices remotely, without taking the network of sensors out of service.
  • Security crucial, particularly where remote access to sensors is required.

New Platform-as-a-Service (PAAS) offerings emerging for Big Data

  • Data storage & warehousing and database tools.
  • Analytics tools and applications: real-time and predictive.

Data Filtering

  • Vast amounts of data can be generated by sensors.
  • Need to filter data to that which is necessary so that systems aren’t overwhelmed.

Analytics Platform

  • Diversity of devices producing data from different locations needs to be configured so that the data can be leveraged
  • Integration with big data analytics platforms

Utilizing Internet of Things (IoT) & Big Data in a Manufacturing Company

Internet of Things (IoT)

Today’s Digital Industry 4.0 is described as the fourth industrial revolution. Industry 4.0 is expected to have a deep impact as well as the capability to change manufacturing, design, operations, and service of production systems and products. When implementing IoT, a company must first have a big data strategy to handle the massive amounts of data that are generated. Thus, big data and IoT are closely related and do both contribute to transforming industrial production.

Big Data Analytics

Business uses Predictive Analytics to conduct a detailed analysis of historical and real-time production data with performance rankings and key insights related to production decisions, expected quality results associated with cost/budget. It gives greater insight into the production across the entire plant network, the production output can be monitored and adjusted closely and immediately, and smarter decisions can be made regarding production schedules.

Predictive Analytics helps a business to make better decisions by using accurate, reliable, and scientific information to analyze risk, optimize processes, and predict failure. The use of Predictive Analytics to improve yield was important to have a clear understanding of what data are available and which data are of relevance for future analytics.

A summary of the applications suitable to apply in the production process note that a division has been made between applications in manufacturing optimization and the ones within preventive and predictive maintenance.

Manufacturing optimization

  • Integrate currently collected data from the subprocesses through a big data approach
  • Gather additional data in the pasteurization through sensors
  • Real-time controls to make real-time adjustments for improved quality and resource usage
  • Utilize big data and IoT to collect and analyze significant amounts of data as a basis for decision making
  • Use IoT components to improve data collection and its accuracy
  • Use a big data approach to analysis to combine and analyze existing data
  • Identify missing data types and collect this data through the usage of IoT components
  • Identify the users and adapt the visualization for them

Preventive and predictive maintenance

  • Real time controls to implement predictive maintenance
  • Integrate currently collected data types from machines to implement preventive maintenance