- By Doug Lawson
- March 15, 2023
Manufacturers rush to the cloud but struggle to access data with actionable insights.
During the past few years, the majority of manufacturers have migrated their systems to the cloud to increase business agility, access unlimited data and reduce costs. In fact, the manufacturing industry leads all other sectors in cloud adoption. As a result, cloud-based manufacturing systems are becoming the gold standard for building the future smart factory as more manufacturing leaders migrate their operations.
However, modern manufacturers receive massive amounts of data from various sources, including IIoT devices, machines, vendors, customers and supply chains. This vast amount of data stored in the cloud can be overwhelming and challenging to make sense of. As a result, manufacturing firms cannot easily access helpful information from their data to deliver actionable insights.
In most organizations, this data is still “raw”—relatively unprocessed information in various forms. Some are collected in real-time, while others are delivered in batches later. Almost none of the collected data is clearly correlated, making it impossible to analyze or identify trends relevant to their operations.
The leading challenge manufacturers face with raw data is understanding how to contextualize it. However, this raw data can be incredibly valuable to businesses if they can mine the critical parts of it. Manufacturers need real-time, cost-effective, actionable insights derived from their data. As a result, there has been a tremendous resurgence in demand for a cloud-based semantic layer to ensure material traceability while improving speed, scale and cost savings in a manufacturing firm.
A semantic layer is a business representation of data that allows users to access insights across an entire organization quickly. This creates a unified and consolidated view of data for users throughout the business to analyze and leverage its insights to benefit the company. A semantic layer translates technical information into business meaning. Instead of teams using faulty tribal knowledge or sifting through large amounts of technical documentation, the semantic layer directly helps to draw a link between the data and analytics. A semantic layer also shrinks the effort leaders must put into becoming ever more data literate.
All types of businesses need help accessing and managing the data under their control. According to Inc, around 75% of data are never used or examined. The distance between data sources and business users is one of the causes of this. Data silos frequently happen in most businesses as a result of the decentralized data landscape, leaving gaps in the "data-driven strategy" and leaving business users without any common or unified definitions.
To close this gap, forward-thinking manufacturers should implement centralized data warehouses and leverage semantic layer technology. According to DBP Institute, organizations that have implemented a semantic layer cite over 4X improvement in performance with less than half the effort required. This is a significant adjustment considering a project that generally takes four months could be completed in four weeks with the help of a cloud-based semantic layer.
The semantic layer helps manufacturers quickly and easily identify opportunities for improvement, predict maintenance needs, and identify potential issues before they become costly problems. For example, by analyzing data from machines in real-time and using historical data, manufacturers leveraging this technology in their data platform can predict when machine maintenance will be needed, reducing downtime and increasing efficiency. This can lead to significant cost savings, as well as improved safety and quality.
Actionable findings will only become available once organizations connect their data and analytics to a cloud-based semantic layer. This will give manufacturers a powerful tool to turn their data into insights that can revolutionize their operations, making them more efficient, cost-effective and competitive. As the manufacturing industry continues to evolve and adapt, manufacturers who embrace this technology will be better equipped to navigate the future and reap the full benefits of this new manufacturing era.
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