The era of IoT brought along a big transformation in the enterprise data lifecycle but as if that was not enough, the implementation of AI on the edge is completely revolutionizing the whole ecosystem in order to make sure it meets real-time analysis needs.
It’s very obvious that IT pros will be making grave mistakes if they think they can afford a room for guesswork in IoT infrastructure planning. The fact on the ground these days is that your digital architecture can undergo changes unpredictably within a space of two years because IoT and AI have seriously started playing great roles in the gathering and handling of data.
Minimal diversity in the traditional data lifecycle
When we have not started operating in clouds, it was easier for IT pros to execute, understand, and control the data lifecycle, since the enterprise data lifecycle was relatively manageable and essentially static and circular. Then, data was more structured, less diverse, and traversed only a few routes to lesser destinations.
At that point in time, we did not have the need for the mazy steps we have now in the traditional data lifecycle you could easily carry out the following processes on your data and that will suffice:
- Plan. All that was necessary was to find out what data you needed to bolster your existing business processes. Obviously, you would only have needed to utilize the most current data at hand for operational planning and process support.
- Procurement. Input data into application systems via data entry and integration with external systems.
- Processing. Verify by authenticating and error-check the data, enriching it if there is a need.
- Analysis. Utilize the data for the required business processes and observe it keenly for indicators and insights that are in accord with decisions.
- Integration. Blend the outcomes of your data analysis into decision-making processes and all further analyses.
- Storage. Store the data in transactional systems for access by ongoing applications and processes and long-term archives.
Even before the cloud came to the rescue, the internet had been straining to contain this supposedly simple, orderly scheme. The clouds, however, accorded us new prospects for connecting the IT core of the enterprise to a mammoth dimension of new devices, and captains of industries were quick to grab the yearning opportunity with open arms.
Edge devices bringing about changes in IoT data lifecycle
The Statista reports that the total global installed base of the internet of things (IoT) connected devices is estimated to amount to 75.44 billion by 2025. This will not just put undue strain on the old data lifecycle, it will completely crumble old-school architecture like cookies.
While cloud connectivity is relatively easy, it still does not in any way start to open enough channels for the data created by IoT. This unprecedented increase in traffic calls for a new architecture.
However, with the IoT data lifecycle, the problem can be split into three manageable sections: the edge, where IoT gathers data; the enterprise front end which is also known as the cloud, where corporate processes and communications happen, including traditional enterprise data architecture; and the enterprise back end, where data is stored long term for onward mining and analysis.
The processes data undergo in the traditional data lifecycle have become distributed and sometimes can be repeated among the edge, front end (cloud), and back end.
The edge where the data lifecycle starts is the local-proximity network which contains IoT devices generally beyond your company firewall. You have a gateway layer of field servers that are dedicated to supporting your IoT network and providing secure access to the enterprise cloud, as well as the many modalities used by the IoT device population for your data acquisition.
Data is collated from IoT devices and the gateway layer for the purpose of processing by edge nodes. The nodes ensure your data is prepared and also undergo conversion for the purpose of transportation to cloud systems.
From the nodes, data is transported to the cloud where the data undergoes processing, validation, and enrichment, they also manage IoT network traffic and security. The analysis of data in the cloud lays emphasis on edge application performance evaluation.
In the back end, there may be the need for data to undergo additional analysis and data mining, if this is not necessary, it will continue its journey to storage or long-term archives. Based on the challenges you encounter in the management of infrastructure as well as security, your IT pros will definitely find the IoT data lifecycle at the edge more difficult.
There more must be a dedicated approach to updating and patching a wide range of IoT devices. Once you are able to take care of these handicaps, the data lifecycle becomes relatively more manageable.
The place of AI in the IoT data lifecycle
The deployment of AI on the edge apart from being an attractive venture is a competitive necessity. Smart buildings, augmented reality, and real-time facial recognition systems are quickly becoming the norm and are gradually finding their way into the cost of running a business.
One major setback that you might have worried about is that where IoT networks are found on the edge, AI is cloud-based and your applications really require real-time AI-based decisions. For this reason, you would have needed to take into consideration the latency issues for your IoT application packets to make multiple round trips to the cloud for real-time analysis.
However, AI can now be deployed to the edge, this has brought great relief to IT pros. Since you’ll now have edge nodes to carry out the processing resources near IoT networks, this brings the double advantage of running the models that provide the intelligence.
Edge nodes also have the capability of supporting more complex routing and retain some IoT data for AI support in the edge network and at the same time ensuring the availability of any data the cloud systems need.
The basic difference between the current data lifecycle with The IoT data lifecycle with AI at the edge is that some data processing steps are integrated. It is now relatively easier for devices at the edge to acquire data, then carry out a preprocessing step in which each node aggregates data with the addition of complex filtering and real-time analytics.
What AI brings into processing at the edge is to enhance the inclusion of decision support for the purposes of holding or sharing of distinct IoT data before it is conveyed to the next stage. IoT applications are able to conduct monitoring and proceed to adjust their own performance based on the data they produce.
Performance data – raw measurements and observations you identify in the course of the tasks you have to perform in order to carry out the project work – can then be integrated and merged into local analytics and machine learning without having to go all the way through the network, before your data is sent to the cloud.
The ability to preprocess at the edge enables you to apply AI to real-time data on local devices, this enables applications to respond promptly to ingested data thereby, circumventing the need to send that data over the network. It is not out of place to still see the need for devices at the edge to send some data to the cloud for further processing and analysis.
It is possible for cloud systems to merge inbound IoT data into existing models to perfect performance and eventually send application control data to the edge where updates are enhanced. At the end of all the processing, the back end can now store your data, with the prospects of further mining and analysis.
It’s imperative that you have the ability to scale both edge and cloud infrastructure in this IoT data lifecycle model. You can reduce possible handicaps by ensuring dynamic routing.