The Internet of Things (IoT) is nothing short of a revolution. Suddenly, vast numbers of intelligent sensors and devices are generating vast amounts of data that contain potentially game-changing information.
In traditional, data analytics, all the data is shipped to a central data warehouse for processing in order to get strategic insights, like all other Big data projects, tossing large amounts of data of varying types into a data lake to be used later.
Today, most companies are collecting data at the edge of their network : PoS, CCTV, RFID scanners, etc. IoT data being churned out in bulk by sensors in factories, warehouses, and other facilities. The volume of data generated on the edge is huge and transmitting this data to a central data center and processing it in a central data center turns out to be very expensive.
The big challenge for IT leaders is to gather insights from this data rapidly, while keeping costs under control and maintaining all security & compliance mandates.
The best way to deal with this huge volume of data is to process this data right at the edge - near the point where data generated.
Advantages of analyzing data at the edge
To understand, lets consider a factory. Sensors on a drilling machine that makes engine parts - generates hundreds of bits of data each second. Over time, there are set patterns of data. Data showing vibrations, for example - it could be an early sign of a manufacturing defect about to happen.
Instead of sending the data across a network to a central data warehouse - where it will be analyzed. This is costly and time consuming. By the time the analysis is completed and plant engineers are alerted, there may be several defective engines already manufactured.
In contrast, if this analysis was done right at the site, plant managers could have taken corrective action before defect occurs. Thus, processing the data locally at the edge lowers costs while increasing productivity.
Also keeping data locally improves security and compliance. As all IoT sensors - could potentially be hacked & compromised. If data from a compromised sensor makes its way to the central data warehouse, the entire data warehouse could be at risk. Avoiding data from traveling across a network prevents malware from wreaking the main data warehouse. If all sensor data is locally analyzed, then only the key results can be stored in a central warehouse - this reduces cost of data management and avoid storing useless data.
In case of banks, the data at the edge could be Personally Identifiable Information (PII), which is bound by several privacy laws and data compliance laws, particularly in Europe.
In short, analyzing data on the edge - near the point where data is generated is beneficial in many ways:
- Analysis can be acted on instantly as needed.
- Security & compliance is enhanced.
- Costs of data analysis are lowered.
Apart from these above mentioned obvious advantages, there are several other advantages:
1. Manageability:
It is easy to manage IoT sensors when they are connected to an edge analysis system. The local server that runs data analysis can also be used to keep track of all the sensors, monitor sensor health, and alert administrators if any sensors fail. This helps in handling a wide plethora of IoT devices used at the edge.
2. Data governance:
It is important to know what data is collected, where it is stored and to where it is sent. Sensors also generate lots of useless data that can be discarded or compressed or eliminated. Having an intelligent analytic system at the edge - allows easy data management via data governance policies.
3. Change management:
IoT sensors and devices also need a strong change management( Firmware, software, configurations etc.). Having an intelligent analytic system at the edge - enables all change management functions to be off loaded to the edge servers. This frees up central IT systems to do more valuable work.
Closing Thoughts
IoT presents a huge upside in terms of rapid data collection. Having an intelligent analytic system at the edge gives a huge advantage to companies - with the ability to process this data in real time and take meaningful actions.
Particularly in case of smart manufacturing, smart cities, security sensitive installations, offices, branch offices etc. - there is a huge value in investing in an intelligent analytic system at the edge.
As conventional business models are being disrupted. Change is spreading across nearly all industries, and organizations must move quickly or risk being left behind their faster moving peers. IT leaders should go into the new world of IoT with their eyes open to both the inherent challenges they face and the new horizons that are opening up.
Its no wonder that a large number of companies are already looking to data at the edge.
Hewlett Packard Enterprise makes specialized servers called Edgeline Systems - designed to analyze data at the edge.
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