In the connected age of industry 4.0, manufacturing companies have access to a massive amount of data. This data is called industrial data, and it’s primarily generated from connected industrial equipment. The low-cost sensors and technology advancements have made data availability possible. Industrial data helps companies to become data-driven and improve their decision-making process. From predicting inefficiencies in machines and processes to preventing downtime, industrial data can help maximize efficiency, make production more intelligent, and improve the ROI.

Despite the evident benefits of industrial data, companies face many challenges in leveraging its full potential. They find the data to be complicated, and most of the data go unutilized. 

Let’s find out why companies find it complicated and how to overcome these challenges. 

Why Is Industrial Data Complicated?

Data silos

Companies need to have a holistic view of the data to make the most of it. Unfortunately, most data are present in silos. Data comes from different sources – machines, product data, records of manual operations carried out by shop floor employees, data from CRM and ERP, and fault-detection or monitoring systems. Some are structured, some are not. Keeping pace with the incoming data daily can be challenging. Lack of governance and legacy systems could worsen the situation. Even basic equipment generates fragmented data that’s available across different systems. Integrating and analyzing them can be time-consuming, tedious, and can be costly. By the time the data is organized, its value deteriorates. 

Poor data quality

Despite the vast data volumes, companies face challenges with data quality. Low data quality can affect the decision-making process and even lead to delayed or flawed decisions. Various factors contribute to poor quality data. To begin with, the purpose of collecting data is not clear. Sometimes, there’s no context. So, it’s difficult to determine what characterizes good and bad data. Other challenges include:

  • Missing or incomplete data
  • Errors in data due to faulty machines or duplication in data
  • Non-standard data such as variations in time stamps and denotations
  • Old data that may not serve the current purpose

Companies need to resolve these shortcomings to derive value from industrial data. 

Short-term vision of management

Companies are aware of the potential benefits of industrial data. But due to lack of use cases, they are unable to derive full value from it. There are also other challenges, such as gaps in strategies and expectations of quick results from industrial data. Industrial data can help companies achieve short-term goals such as reducing costs or streamlining processes and also meet larger business goals such as transforming the supply chain. Learning from other sectors’ use cases and creating a value-oriented roadmap could help companies achieve transformational results from industrial data. 

Lack of collaboration

Typically, the IT teams and the operations technology (OT) teams often work in silos. The IT team looks after infrastructure management. The OT team looks after the machinery, assets, equipment, and monitors the industrial control systems. The IT team is unaware of what the OT team does and vice-a-versa. They also use different system architectures, so most data is scattered. The quality of industrial data depends on the collaborative efforts of the two teams. They need to build a common infrastructure to ensure that there is a single source of data available to all for analytical purposes. That can help improve the data quality.

Lack of visualization

As the volume and complexity of industrial data increases, companies have difficulty analyzing it. Visualization can help companies understand data at a deeper level and find a correlation between different data sets. Visualization can also help companies generate actionable insights. Lack of visualization can pose a challenge in leveraging the full potential of data. Most companies don’t have advanced systems and methods that can help analyze large data volumes that are heterogeneous and hard to summarize. The current systems and devices exchange data in different formats and frequencies, making visualization challenging. Companies need to use proper tools to identify data patterns efficiently and make data-driven decisions.

Conclusion

There are several instances where industrial data proved to be useful. Take GE’s example. They leveraged their industrial data capabilities at their Remote Monitoring & Diagnostics (RM&D) Center in Atlanta. It helped them increase productivity, reduce software development costs by $3 million, improve customer satisfaction, and propelled them towards growth. 

By removing data silos, improving data quality, harmonizing databases, and establishing data governance, companies can make data work in their favor. 

These tasks require an army of experienced data scientists and data analysts. That’s where Delpheon helps. Our low-code/no-code IIoT platform gives the power to business users to leverage the power of industrial data to increase their efficiency, reduce costs, detect anomalies, and make better data-driven decisions – without hiring an army of data scientists and data analysts. 

To get started with a free demo, contact us.