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Why IoT Data Collection is So Hard – And How to Make It Easier

Transforming device or sensor data into information that can be meaningfully acted upon (either manually or automatically) requires a journey through six steps:

  • The source of the data must be Connected to the IoT network;

Data must then be

  • Collected and transported to the right applications;

  • Visualized in a meaningful and appropriate context;

  • Monitored to allow for quick response to event changes or the need for remediation;

  • Analyzed to identify opportunities for improvement or operational efficiency; and

  • Actualized to create new business opportunities and realize ROI from the IoT investment.

Collecting data from multiple devices, sensors and machines – and making sure the right data goes to the right application at the right time – is where many IoT solution architects must resolve multiple complex issues:

What data to extract

Unlike (most) people, sensors and devices output data indiscriminately. While the average human being consumes and creates less than 1 GB of data per day, a single sensor can output 6GB per hour. Not all data is relevant – and sending it all to the cloud to be organized can become an expensive strategy.

What format to put data in

In the world of connected devices, IoT data is not simply just “data”. The format that data exists in a single sensor A is not the same as the data from machine B or even sensor C. For IoT data to become useful, it must be shared and used across multiple applications and stakeholders.

What applications to send data to and when

Just as not all data is relevant, not all data is going to the same application (and some data may need to be shared across multiple applications and stakeholders) and is needed at the same time. While an OEM of a device may only need data to be sent when a systemic error occurs or when providing maintenance, a local operator may need to be able to see device data on a daily or even hourly basis and a business analyst may need to have a subset of the same data imported into a different application in a weekly or monthly report.

Each extracted data stream has value – but to whom and when do they need it?

How long and how much data should be stored

Most devices and sensors have limited storage capacity on the local level. Still for IoT deployments to deliver enhanced value, sensor and device data should be stored so that it can be accessed and viewed in relation to the operation of other devices and sensors on the network, both in real-time and historically. Solution architects must determine the length of time that data needs to be stored locally on-prem, in the cloud or off-site.

IoT Data Collection Complexity = Siloed Projects That Deliver Limited Value

For IoT solution architects, creating a data collection solution to address the above issues can result in the need for extensive time and effort to be spent on developing APIs, custom programs and firmware to extract, format, transport and store data between:

  • the device and local applications

  • the device and the cloud

  • local applications and the cloud

More importantly, for data from the IoT network to be useful, data streams from multiple sensors and devices must be visualized and monitored together – creating more complexity for solution developers. It’s not surprising that given the complexity, many IoT projects remain siloed and separate from the enterprise – limiting the business value they can deliver.

Network-Integrated Data Management Can Simplify IoT Data Collection

For IoT solution architects, a ready-to-use data collection solution should include:

  • The ability to easily configure any device or sensor for data extraction with minimal to zero coding

  • The ability to schedule data reporting and monitoring via email and SMS

  • Transformation and storage of sensor and device data into a data format that can easily be accessed and used across multiple platforms or applications

  • Allow for on-prem and cloud optional deployment

  • Be scalable

Network-integrated data management solutions leverage the power of edge computing to simplify IoT data collection and provide IoT solution architects with a scalable solution that can allow them to more quickly deploy IoT networks and projects.

By putting data management intelligence on the edge of the network infrastructure, solutions like Machinechat’s JEDI IoT Data Manager, solve these data collection issues:

  • Eliminate the need for altering device and sensor firmware or hardware

  • Enable easy-to-deploy extraction of sensor and device data in its native protocols

  • Allow for a single pane visualization and monitoring of data from multiple devices and sensors in minutes without the need for custom programming

  • Enable automated local storage in CSV formats – allowing the data to be easily exported and shared across multiple applications

  • Allow data to stay on-prem and at the same time, enable data to be stored or transported in a cloud-ready format.



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