How Internet of Things (IOT) is used for Edge Analytics- What are its Pros and Cons?
IOT (Internet of Things) is billed to be the next big thing in the world of contemporary technology. However, it has some interesting usage possibilities in terms of Edge Analytics as well. Here’s decoding the same.
What is IOT & IOT Analytics?
IOT (Internet of Things) basically refers to the network of various things or objects which have been embedded with software, various technologies or even sensors for connectivity and data exchange purposes. The data exchange here covers other systems and devices over the internet. These devices may include smart industrial tools or even household appliances along with regular machines. The world already has more than 7 billion IOT devices as of today and the number may touch a whopping 22 billion by the year 2025.
IOT analytics refer to tapping IOT technologies for generating business analytics that help companies scale up ROI (return on investment) while optimizing systems at the same time. Mature analytics is also about scalability and maximizing productivity while tapping each level across the spectrum. With increasing volumes of IOT devices, there will be a growing need for analyzing this data for generating business insights.
What is edge analytics?
Edge analytics basically refers to tools which are positioned on or near IOT devices for collecting, analyzing and processing data at source instead of sending this data back to the cloud for analysis purposes. This streamlines the whole procedure of data analysis by executing the same in real-time, ensuring that useful data is garnered from devices.
What are its Pros & Cons?
- Time Savings- Centralized systems enabling data collection via internet linked devices can be on the slower side. All raw data must be cleaned, analyzed or processed for extracting its actual value. 90% of deployed data will otherwise be of no value with inaccuracies. In an edge analytics system, unwanted information will be filtered out prior to analysis with relevant information being run via higher order systems, saving uploading and processing timelines. This scales up efficiency of complex analytical stages on the cloud as well. Real-time analysis scales up efficiency while lowering chances of erroneous decision making.
- Cost Savings- Edge Analytics enables lower data management and storage costs, reducing operational expenses as well. It minimizes necessary bandwidth and resources that are spent on analyzing data.
- Privacy- Confidential data, when captured by any connected device, may be preprocessed on site and not bulk uploaded for processing to the cloud. This extra stage means that only privacy-compliant information leaves for further analysis from the device and there is preprocessing aggregation in place. Sensitive content can be privately preserved in this case.
- Connectivity- In spite of internet connected ecosystems, there are connectivity issues persisting till now. Using edge analytics will enable safeguarding against connectivity outages with minimal disruption via network connectivity errors. This helps in remote zones.
- Data Losses- Using Edge Analytics means processing of just a data subset and its analysis, with results being dispatched via the network. This means that raw data will be junked and this may result in some data losses overall. The key thing to remember here is the device usage and type, i.e. is the data loss vital or is it needed for higher operational efficiency? That is the key question that businesses need to answer.
Application of IOT Edge Analytics
IOT Edge Analytics are presently applicable for large-scale companies and industries which function in low-latency and low-bandwidth environments. This includes factories, oil rigs and mines and the technologies will gradually expand in sync with growth in IOT systems globally throughout all sectors. The logistics, travel and tourism industries will naturally benefit from IOT Edge Analytics with sensors and other data collection forms becoming commoner in multiple sectors.
Experts like Cisco have estimated that a whopping 507.5 zettabytes in data will already be produced by end-2019 with more in the works for 2020 and beyond. As a result, swift processing is vital for proper handling of this information. Edge Analytics functions as an efficient and comparatively affordable solution in this regard.
IOT and Edge Analytics- Fusing efficiency with cost savings
Fusing Edge Analytics with IOT will naturally give businesses a greater advantage with regard to time and cost savings along with ramping up overall efficiency. Experts estimate that a major chunk of data processing across industries will be tackled with this combination (roughly 30-50%) and the figures are only set to increase in the near future.