At its heart, the Web of Things (IoT) is based upon the premise that a sufficient amount of data can lead to new insights on procedures and systems. These can be used for decision support and new products and services.
Or lead to internal savings and new external revenue streams. And successful IoT implementation jobs require two major actors: the engineer and the data scientist.
Performance and analyzing machine-generated data fast yields favorable benefits in predictive maintenance and production process optimization.
But it also leads to enhanced customer satisfaction as it helps people better understand user behavior. Some time ago, a comprehensive analysis by McKinsey estimated that if machines are linked and monitored, it is likely to increase the productivity and the lifespan of machine tools, reduce the maintenance costs between 10% and 40%, and reduce the energy consumption around 20 percent.
A streamlined cycle leading to conclusive action is at the heart of the inherent strategy surrounding IoT. The challenge in this context is tobring the worlds of engineering and data science together. Within IoT contexts, these two specialty domains have to function in the most efficient way with the least possible friction.
How and at what stages of the data travel do companies close the gap between engineering (the area of hardware, microcontrollers, chips, electronics) and data science(the planet of data warehousing, algorithm development, data analytics) is a strategic decision with far-reaching consequences.
In what follows, we propose a way for the engineer and data scientist to operate together across IoT border scenarios.
The Shift Towards Edge Computing
According to the 2020 IDC FutureScape Predictions for IoT globally, by 2023, 70% of enterprises will run different levels of data processing at the IoT border. Equally so, organizations are projected to spend over $16 billion on IoT edge infrastructure.
What motivates this shift towards edge computing and why should we move intelligence to the edge of the IoT network ?A major driver behind this tendency is simultaneously a frequent challenge in big data systems. This is the requirement to capture massive amounts of data from various heterogeneous sources.
The Border Becomes Synonymous with All Speed
Further, the data has to be prepared for analysis across multiple stages. These include data validation, data cleaning, transformation, indexing, aggregation, and saving. Based on the nature of the data and the business goal in mind, companies also require a suitable processing strategy. This, in turn, can range from batch processing to real-time processing.
For those processing massive amounts of data, it makes sense to procedures the data close to where it is generated. This applies when you leverage IoT in end-to-end scenarios or in exceptionally sensor-intensive and thus data-intensive environments.
This is because of some inevitable challenges in data travel for example bandwidth, network latency, and overall speed. Edge computing becomes particularly relevant in IoT applications with a mission-critical or remote component.
Here advantage computing minimizes the risk of data loss. But more drastically, it also offers acceleration in scenarios where speed is a crucial differentiator in IoT efforts.
The speed of data analysis has become indispensable in many industrial IoT applications. Speed is a vital element of industrial transformation as companies shift towards autonomous and semi-autonomous decision-making through systems, actuators, and controls. You have to accelerate the generation of aggregated and analyzed data that can function as actionable intelligence. And you want a fast decision-making path.
Edge vs. Cloud
Whereas keeping the data and analyzing it in the cloud means deeper and more comprehensive processing, edge computing offers speed and immediacy in data processing. Broader processing can take place in cloud systems where you can combine data from different sources and generate insights not immediately available at the border.
But in regards to the speed of processing, accelerated decision-making, and greater autonomy, and thus higher degrees of automation, processing at the border has established itself as the faster and smarter approach.
Further, processing data at the edge of the IoT network enables organizations to remain fully in control of highly sensitive or proprietary information. Decision-making can take place at the border. Hereby all data remains within the company and only non-sensitive information could make its way into the cloud.
In addition, the anonymization of company data can take place at the border, allowing companies to protect critical data assets from potential security breaches.
A comprehensive IoT solution would manage devices at the edge layer, join them together, and collect data from them will also address huge data techniques for data management, data transformation, and advanced analytics. IoT and large data can be brought together on a unified platform managing IoT apparatus and deploying apps in real time while also leveraging big data frameworks and applications to store, process, analyze, and visualize industrial large data acquired by IoT. How do we achieve this confluence?
Engineering and Data Science: Closing the Gap
Edge computing is typically owned and managed by engineering departments. This is the area of hardware, microcontrollers, chips, and electronic equipment. Engineers usually operate in slow growth cycles that involve accessing machines without affecting the functionality of the entire, collecting data out of physical apparatus, and wrestling with high quantities of incoming raw data.
Cloud services, data processing, and analytics are typically owned by IT departments and data scientists. This is the world of mathematics, information technology, abstract understanding, and theoretical models.
On this end of the scale, we have agile development, fast trial and error scenarios, the development of algorithms, and data science, broadly conceived. From the classic setup, the engineering department provides data extracted from industrial devices and sends it over to the data scientists so that they can work on precision generation and come back having an actionable decision.
Some Typical Bottlenecks in IoT Development
What is today’s scenario? Well, it often involves some miscommunication between two actors: the engineer and the data scientist. Engineers gather massive amounts of data from their machines. And they want to have data science performed on the data to streamline certain aspects of their process.
So they would employ a data science team to do work on that data. Once confronted with the need to get into the machine data of the apparatus in the machine hall, however, data scientists have to discover that getting the data out of apparatus is a very laborious process. Given this scenario, it is often the case that the bottlenecks for IoT projects are usually not the ideas or algorithms although the data pipeline and data quality.
The data from the engineering department is sometimes in the wrong format, or not what is required at the moment. For example, it may turn out that the data has to be tracked at a higher rate. Engineers may have to get to the border computer to find a new way of extracting the data. To get usable data, they may even have to purchase new border apparatus to extract the data.
But then again, the data can still be unusable. Then it might emerge that a higher iteration rate or a larger time window for recording is needed. So the border devices may have to be reconfigured completely to collect that data in a different way. This process of conducting back and forth may entail numerous iterations that take weeks.
Better Communication Using a Common IoT Platform
A practicable solution, in this case, is a unified platform that covers the whole IoT development cycle from working on the machine-starting with data extraction–all the way up to the data science results. Engineers can log into the platform to access the computing apparatus at the IoT border, set values via the platform programs or set up the same code/configurations onto a fleet of IoT devices located remotely.
Once the data starts coming in, it is stored in a location on the platform from where data scientists can view the historization and versioning processes at a glance, data cleaning is performed, the data can be merged with other data, simulated, and made directly available for analytics and visualization tasks.
One such IoT platform closes the communication gap between engineering and data science. It brings these worlds together via a unified enabling interface. Data scientists are able to immediately access the advantage computer that generates the data and is responsible for data handling. The center machine remains wholly separate. It is not maintained or managed by the platform.
The engineering department no longer has to face the challenge of accessing the actual machine in real time. Via the platform, engineers have the benefits of remote access. They can also deploy code within the air and use the device management functionality to see where the data is coming from and which IoT device is set up at a certain location.
The IoT Platform As A Unifying Hub
One such IoT platform becomes the”digital backbone” of the industrial operations of an organization. This is where two actors — the engineer and data scientist — connect software and hardware to extract value from business operations.
An IoT platform that is ready for smart manufacturing incorporates machine learning and large data technology plus established automation technologies. 1 such platform not just functions as the enabler for the IoT ecosystem but also leverages its own infrastructure as a digital pulse where device management and app development fulfill an advanced data science toolchain.
An IoT platform built to face critical IoT challenges at the IoT edge offers a combination of capabilities such as the management of IoT endpoints and connectivity plus IoT application development and integration applications. Further, an IoT platform that attracts engineers and data scientists together nutritional supplements these capabilities together with the accessing, intake, and processing of IoT data, together with IoT data analysis and visualization.
1 degree of this solution is a fully-fledged IoT studio for application development and the remote management of IoT devices. Another level is a fully integratable data warehouse infrastructure. This is where you receive the data streams harvested from apparatus and construct an insight-enabling analytical data environment.
The Key Takeaway
A platform that brings together the engineer and the data scientist offers a complex end-to-end IoT solution. It starts with data acquisition from IoT apparatus, plus collecting, pre-processing, and aggregating the data at the IoT gateway. Data scientists can then transmit the data to a cloud data science platform. This is where more advanced analytics are performed and ML models are trained.
Once data scientists have trained their machine learning units in the cloudthey can bring awareness into the IoT edge. Using the platform, the trained models can be rolled out to a variety of IoT apparatus. In this way, we have an iterable cycle of data acquisition, transformation, analytics, and installation back at the IoT border.