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What Is Data Engineering, and How Does It Relate to Smart Manufacturing?

What Is Data Engineering, and How Does It Relate to Smart Manufacturing?

Data engineering refers to designing and building a system that collects, stores and analyses data. The data received from multiple sources is hence transformed into a format which is regarded to be highly reusable by the time it reaches data scientists. In simple terms, data engineering optimises big data and executes data-centric projects. Data engineers, therefore, consistently work towards ensuring that data flows smoothly.  Interestingly, it is common knowledge that the majority of big data projects end up failing. This was first declared in 2016 by the company Gartner, which estimated that 60 per cent of big data projects failed. However, an analyst from Gartner, Nick Heudecker, stated a year later that this percentage was “too conservative” and that the actual failure rate was 85 per cent. Gartner further said, in its report in 2022, that only 20% of analytic insights will deliver business outcomes. 

The failure of big data projects can be attributed to a lack of powerful technologies in software applications. The delay in receiving the latest data may mean they are no longer applicable when data scientists draw their findings. Thus, there is a need for better mechanisms to be in place to process and analyse big data. In other words, better methods of automating and streamlining real-time data should be implemented. One easy way to do so is by leveraging the power of industry 4.0 technology. This article will discuss what technologies can be adapted to carry out data engineering reliably.

Modern Technologies That Help Data Engineers to Provide Big Data in Its Best Form

The underlying purpose of data engineering in manufacturing is to gain valuable insights that help to optimise factory operations and to take precautionary steps to prevent malfunctions. In a smart manufacturing factory, this is carried out through digital tools such as Artificial Intelligence (AI), Industrial Internet of Things (IIoT), predictive and prescriptive analytics and more. Here are five ways data engineering can be used in a smart factory to help managers to succeed in their big data projects:

Data Engineering Transports Data Received by IIoT Sensors to Monitor Manufacturing Assets

Data engineering uses data from different devices and software to draw insights. One key technology that should be incorporated into manufacturing factories includes IIoT sensors. There are many types of sensors that monitor various aspects.  Integrating this data into one central platform ensures that it can produce well-rounded data-driven information. Thus, this provides the most up-to-date information for managers to make decisions. In other words, managers can timely respond to events instead of spending an enormous amount of time or money requiring a data scientist to make findings based on old information.

Data Engineering Helps Big Data Flow to Receive Accurate Predictive and Prescriptive Analytics

Predictive and prescriptive analytics is a type of technology that can warn managers of impending risks. Taking advantage of the IIoT sensor’s ability to provide real-time data on the condition of the assets, predictive and prescriptive analytics are used to detect anomalies in each asset. Thus, the sensors’ big data can accurately help the software assess when an asset will malfunction. Once the manager is notified, maintenance can be scheduled according to the criticality of assets. Additionally, while routine maintenance is essential, ensuring to do so at the right time is the only guarantee that maintenance will be carried out when required. Prolong the health of each manufacturing asset with the big data generated from smart devices. 

Data Engineering Gives Managers the Ability to Streamline Complex Supply Chain Operations

The role of the manager is not limited to the manufacturing facility. They are also responsible for ensuring that the supply chain is simplified. The supply chain includes a range of complex processes. It handles packaging and delivering the end products to the consumer who could be located in different parts of the world. This makes the delivery process the most time-consuming. Tracking products in real-time and keeping the consumer informed on their delivery is one crucial way big data and data engineering help supply chain operations. With big data, managers can also analyse risks that can occur to the product within the supply chain and take steps to prevent them. As a result, while not much can be done to speed the delivery process, reassurance can be given to the consumer on the quality of the package and where it is, letting the consumer plan ahead. 

Data Engineering Helps Managers to Make Informed Decisions Based On The Consumer Market

Depending on the type of products a manufacturer makes, the demand for them could vary according to seasons. Hence, while in some months, a high number of end products are required, in other months, the demand for them could be average or at worst, less. This is a natural part of any business. Therefore, it is vital to consider industrial market changes and consumer trends to ensure that no losses are unnecessarily incurred by way of resources or no waste is made. A good data engineering application can accurately predict the right marketing changes or consumer trends by considering current and historical data. Thus, decisions can be taken to ensure the right amount of products are manufactured according to the demand it receives.

The Conclusions Drawn by Data Engineering Helps to Create a Sustainable and Safe Environment

Digital solutions have played a significant role in helping manufacturing companies to create a safe and sustainable working environment. Since big data is produced in real-time and large quantities of data easily flow through the data engineering application, it ensures that system breakdowns are eliminated. In other words, as assets are consistently monitored and their health is kept up with industrial standards, any potential harmful gases emitted from assets in lousy condition are removed. Moreover, as all data is digitally stored on one integrated platform, there is no need to reserve space to store records documents. Therefore, storage can be utilised efficiently. Additionally, considering that the data engineering application can help the devices motor the asset’s condition, it removes any hazardous incidents that employees may be subjected to if they did manual checkups. 

Cerexio Manufacturing Execution System: The Best Software Solution in Asia That Can Unlock the True Potential of Big Data

Cerexio MES is a compact solution that addresses all of your manufacturing needs. A recognised solution in the world, Cerexio is one of the few technology vendors that provide exclusive digital solutions to the manufacturing industry. It integrates various advanced technologies, including AI, ML, predictive and prescriptive analytical tools, digital twin, IIoT and more. It, as a result, automates a range of processes and provides real-time status on the asset’s condition. With Cerexio, all hidden root causes behind your factory’s inefficiencies can be detected by the built-in data engineering process in the system. Cerexio can utilise all the data its advanced technology produces to provide accurate insights. Hence, the software notifies the manager when a manufacturing asset is due to be repaired. It can also predict any future risks and influence the manager to take steps to curtail them. It can monitor marketing changes per consumer demands and competitor rates and provide detailed reports to help you take the right call. By incorporating the Cerexio MES solution, you can be guaranteed that no big data analytics will go to waste. 

Contact us to learn how Cerexio can help eliminate all anomalies anchoring you down.

Leave No Room for Big Data to Go to Waste

The type of data engineering application adopted in the manufacturing company varies according to the software vendor. Depending on the reputation of the software solutions developer, you can guarantee that a robust data engineering application is built into the solution. Cerexio is a synonymous brand in the manufacturing world, known for providing advanced solutions equipped with the latest industry 4.0 technologies in Asia and the world.  Choose the right software vendor and leave no room for big data waste.

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